diff --git a/cellmap_flow/blockwise/blockwise_processor.py b/cellmap_flow/blockwise/blockwise_processor.py
index 32e10b9..92f72e1 100644
--- a/cellmap_flow/blockwise/blockwise_processor.py
+++ b/cellmap_flow/blockwise/blockwise_processor.py
@@ -1,4 +1,10 @@
 import logging
+logging.getLogger().setLevel(logging.INFO)
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+logging.getLogger().setLevel(logging.INFO)
+logging.basicConfig(level=logging.INFO)
+
 import subprocess
 from pathlib import Path
 
@@ -16,8 +22,9 @@
 from cellmap_flow.utils.config_utils import build_models, load_config
 from cellmap_flow.utils.serilization_utils import get_process_dataset
 from cellmap_flow.utils.ds import generate_singlescale_metadata
+from cellmap_flow.models.model_merger import get_model_merger
+
 
-logger = logging.getLogger(__name__)
 
 
 class CellMapFlowBlockwiseProcessor:
@@ -98,15 +105,33 @@ def __init__(self, yaml_config: str, create=False):
 
         # Support multiple models with model_mode
         self.models = models
-        self.model_mode = self.config.get("model_mode", "AND").upper()
-        if self.model_mode not in ["AND", "OR", "SUM"]:
-            raise Exception(
-                f"Invalid model_mode: {self.model_mode}. Must be one of: AND, OR, SUM"
-            )
+        self.model_mode_str = self.config.get("model_mode", "AND").upper()
+        try:
+            self.model_merger = get_model_merger(self.model_mode_str)
+        except ValueError as e:
+            raise Exception(str(e))
 
         if len(models) > 1:
             logger.info(
-                f"Using {len(models)} models with merge mode: {self.model_mode}"
+                f"Using {len(models)} models with merge mode: {self.model_mode_str}"
+            )
+
+        # Support cross-channel processing
+        self.process_only = self.config.get("process_only", None)
+        self.cross_channels_mode = self.config.get("cross_channels", None)
+        
+        if self.cross_channels_mode:
+            self.cross_channels_mode = self.cross_channels_mode.upper()
+            try:
+                self.cross_channels_merger = get_model_merger(self.cross_channels_mode)
+            except ValueError as e:
+                raise Exception(f"Invalid cross_channels setting: {e}")
+        else:
+            self.cross_channels_merger = None
+        
+        if self.process_only:
+            logger.info(
+                f"Processing only channels: {self.process_only} with merge mode: {self.cross_channels_mode}"
             )
 
         self.model_config = models[0]
@@ -114,7 +139,7 @@ def __init__(self, yaml_config: str, create=False):
         # this is zyx
 
         block_shape = [int(x) for x in self.model_config.config.block_shape][:3]
-        self.block_shape = self.config.get("block_size", block_shape)
+        self.block_shape = tuple(self.config.get("block_size", block_shape))
 
         self.input_voxel_size = Coordinate(self.model_config.config.input_voxel_size)
         self.output_voxel_size = Coordinate(self.model_config.config.output_voxel_size)
@@ -133,6 +158,9 @@ def __init__(self, yaml_config: str, create=False):
             self.output_channel_names = self.channels
             self.output_channel_indices = None
 
+        if not isinstance(self.output_channels, list):
+            self.output_channels = [self.output_channels]
+
         if json_data:
             g.input_norms, g.postprocess = get_process_dataset(json_data)
 
@@ -161,6 +189,12 @@ def __init__(self, yaml_config: str, create=False):
 
         # Ensure we have output channels to iterate over
         channels_to_create = self.output_channels if self.output_channels else []
+        if not isinstance(channels_to_create, list):
+            channels_to_create = [channels_to_create]
+
+        # check if there is two channels_to_create with same name
+        if len(channels_to_create) != len(set(channels_to_create)):
+            raise Exception(f"output_channels has duplicated channel names. channels: {channels_to_create}")
 
         for channel in channels_to_create:
             if create:
@@ -190,7 +224,7 @@ def __init__(self, yaml_config: str, create=False):
                         chunk_shape=(
                             self.block_shape
                             if len(final_output_shape) == 3
-                            else (len(channel_indices),) + tuple(self.block_shape)
+                            else (len(channel_indices),) + self.block_shape
                         ),
                         voxel_size=(
                             self.output_voxel_size
@@ -254,6 +288,8 @@ def __init__(self, yaml_config: str, create=False):
                     if "multiscales" in list(zg.attrs):
                         old_multiscales = zg.attrs["multiscales"]
                         if old_multiscales != zattrs["multiscales"]:
+                            logger.info(f"Old multiscales: {old_multiscales}")
+                            logger.info(f"New multiscales: {zattrs['multiscales']}")
                             raise ValueError(
                                 f"multiscales attribute already exists in {z_store.path} and is different from the new one"
                             )
@@ -302,10 +338,15 @@ def process_fn(self, block):
             model_outputs = []
             for inferencer in self.inferencers:
                 output = inferencer.process_chunk(self.idi_raw, block.write_roi)
+                if self.process_only and self.cross_channels_merger:
+                    # Extract only the specified channels
+                    channel_outputs = [output[ch_idx] for ch_idx in self.process_only]
+                    # Merge the extracted channels based on cross_channels mode
+                    output = self.cross_channels_merger.merge(channel_outputs)
                 model_outputs.append(output)
 
             # Merge outputs based on model_mode
-            chunk_data = self._merge_model_outputs(model_outputs)
+            chunk_data = self.model_merger.merge(model_outputs)
 
         chunk_data = chunk_data.astype(self.dtype)
 
@@ -372,38 +413,6 @@ def process_fn(self, block):
                     continue
                 array[array_write_roi] = predictions.to_ndarray(array_write_roi)
 
-    def _merge_model_outputs(self, model_outputs):
-        """
-        Merge outputs from multiple models based on the configured model_mode.
-
-        Args:
-            model_outputs: List of numpy arrays from different models
-
-        Returns:
-            Merged numpy array
-        """
-        if self.model_mode == "AND":
-            # Element-wise minimum (logical AND for binary, minimum for continuous)
-            merged = model_outputs[0]
-            for output in model_outputs[1:]:
-                merged = np.minimum(merged, output)
-            return merged
-
-        elif self.model_mode == "OR":
-            # Element-wise maximum (logical OR for binary, maximum for continuous)
-            merged = model_outputs[0]
-            for output in model_outputs[1:]:
-                merged = np.maximum(merged, output)
-            return merged
-
-        elif self.model_mode == "SUM":
-            # Sum all outputs and normalize by number of models
-            merged = np.sum(model_outputs, axis=0) / len(model_outputs)
-            return merged
-
-        else:
-            raise ValueError(f"Unknown model_mode: {self.model_mode}")
-
     def client(self):
         client = daisy.Client()
         while True:
@@ -484,7 +493,7 @@ def run_worker():
                 f"prediction_logs/out.out",
                 "-e",
                 f"prediction_logs/out.err",
-                "cellmap_flow_blockwise_processor",
+                "cellmap_flow_blockwise",
                 f"{yaml_config}",
                 "--client",
             ]
diff --git a/cellmap_flow/blockwise/cli.py b/cellmap_flow/blockwise/cli.py
index 28a3dc0..6aee0fc 100644
--- a/cellmap_flow/blockwise/cli.py
+++ b/cellmap_flow/blockwise/cli.py
@@ -1,6 +1,8 @@
 import click
 import logging
-
+import logging
+logging.getLogger().setLevel(logging.INFO)
+logging.basicConfig(level=logging.INFO)
 from cellmap_flow.blockwise import CellMapFlowBlockwiseProcessor
 
 
@@ -32,3 +34,7 @@ def cli(yaml_config, client, log_level):
 
 
 logger = logging.getLogger(__name__)
+
+
+if __name__ == "__main__":
+    cli()
diff --git a/cellmap_flow/cli/yaml_cli.py b/cellmap_flow/cli/yaml_cli.py
index 3d34b5b..929af72 100644
--- a/cellmap_flow/cli/yaml_cli.py
+++ b/cellmap_flow/cli/yaml_cli.py
@@ -172,23 +172,23 @@ def main(config_path: str, log_level: str, list_types: bool, validate_only: bool
 
     # Build model configuration objects dynamically
     logger.info("Building model configurations...")
-    models = build_models(config["models"])
+    g.models_config = build_models(config["models"])
 
-    logger.info(f"Configured {len(models)} model(s):")
-    for i, model in enumerate(models, 1):
+    logger.info(f"Configured {len(g.models_config)} model(s):")
+    for i, model in enumerate(g.models_config, 1):
         model_name = getattr(model, "name", None) or type(model).__name__
         logger.info(f"  {i}. {model_name} ({type(model).__name__})")
 
     # Validation mode - exit without running
     if validate_only:
         click.echo("\n✓ Configuration is valid!")
-        click.echo(f"  - Models: {len(models)}")
+        click.echo(f"  - Models: {len(g.models_config)}")
         click.echo(f"  - Data path: {data_path}")
         click.echo(f"  - Queue: {queue}")
         return
 
     # Run the models
-    run_multiple(models, data_path, charge_group, queue)
+    run_multiple(g.models_config, data_path, charge_group, queue)
 
 
 if __name__ == "__main__":
diff --git a/cellmap_flow/dashboard/PIPELINE_BUILDER.md b/cellmap_flow/dashboard/PIPELINE_BUILDER.md
new file mode 100644
index 0000000..553d193
--- /dev/null
+++ b/cellmap_flow/dashboard/PIPELINE_BUILDER.md
@@ -0,0 +1,222 @@
+# CellMapFlow Pipeline Builder
+
+A drag-and-drop visual pipeline builder for creating and managing CellMapFlow inference workflows.
+
+## Features
+
+- **Visual Pipeline Editor**: Drag-and-drop interface to connect normalization models and postprocessors
+- **Node-based Workflow**: Connect input → normalizers → postprocessors → output
+- **Parameter Configuration**: Set parameters for each processing step
+- **Export/Import**: Save pipelines as YAML or JSON and reload them later
+- **Real-time Validation**: Validate pipelines before applying them
+
+## Architecture
+
+### Frontend (React Flow)
+
+The pipeline builder uses **React Flow** for the visual node editor:
+
+**Components:**
+- [pipeline-builder.js](static/js/pipeline-builder.js) - Main React component
+- [pipeline-nodes.js](static/js/pipeline-nodes.js) - Node styling and layout
+- [pipeline-toolbar.js](static/js/pipeline-toolbar.js) - Toolbar for adding nodes
+- [pipeline-exporter.js](static/js/pipeline-exporter.js) - Export/Import functionality
+- [pipeline_builder.html](templates/pipeline_builder.html) - HTML template
+
+### Backend (Flask)
+
+Flask routes handle validation and application of pipelines:
+
+**Endpoints:**
+- `GET /pipeline-builder` - Render the pipeline builder UI
+- `POST /api/pipeline/validate` - Validate pipeline configuration
+- `POST /api/pipeline/apply` - Apply pipeline to inference
+
+## File Structure
+
+```
+cellmap_flow/dashboard/
+├── app.py                          # Flask app with pipeline routes
+├── package.json                    # npm dependencies
+├── static/
+│   └── js/
+│       ├── pipeline-builder.js     # Main React component
+│       ├── pipeline-nodes.js       # Node components
+│       ├── pipeline-toolbar.js     # Toolbar component
+│       ├── pipeline-exporter.js    # Export/import logic
+│       └── index-pipeline.js       # Entry point
+└── templates/
+    └── pipeline_builder.html       # HTML template
+```
+
+## Usage
+
+### Access the Pipeline Builder
+
+Navigate to: `http://localhost:PORT/pipeline-builder`
+
+### Creating a Pipeline
+
+1. **Add Normalizers**: Select from dropdown in toolbar to add normalization steps
+2. **Add Postprocessors**: Select from dropdown to add post-processing steps
+3. **Connect Nodes**: Drag from output handle to input handle to connect steps
+4. **Configure Parameters**: Click a node and enter JSON parameters in the panel
+5. **Apply**: Use the "Apply Pipeline" button to use the configuration
+
+### Exporting a Pipeline
+
+**As YAML:**
+```yaml
+input_normalizers:
+  - name: StandardNormalizer
+    params:
+      mean: 0.5
+      std: 0.1
+postprocessors:
+  - name: InstanceSegmentation
+    params:
+      threshold: 0.5
+```
+
+**As JSON:**
+```json
+{
+  "nodes": [...],
+  "edges": [...],
+  "timestamp": "2024-01-28T..."
+}
+```
+
+### Importing a Pipeline
+
+Click "Import Pipeline" and select a previously saved `.yaml` or `.json` file.
+
+## Configuration
+
+### Node Types
+
+- **Input** (green): Data entry point
+- **Normalizer** (blue): Input normalization steps
+- **Postprocessor** (pink): Post-processing operations
+- **Output** (gold): Final output
+
+### Parameter Format
+
+Parameters are specified as JSON. Examples:
+
+```json
+{
+  "clip_min": -1.0,
+  "clip_max": 1.0,
+  "bias": 1.0,
+  "multiplier": 127.5
+}
+```
+
+## Integration with CellMapFlow
+
+The pipeline builder integrates with your existing CellMapFlow infrastructure:
+
+1. **Normalizers**: Uses `get_input_normalizers()` from [input_normalize.py](../norm/input_normalize.py)
+2. **Postprocessors**: Uses `get_postprocessors_list()` from [postprocessors.py](../post/postprocessors.py)
+3. **Validation**: Validates against available models before applying
+4. **Application**: Applies to global state (`g.input_norms`, `g.postprocess`)
+
+## API Reference
+
+### GET /pipeline-builder
+
+Returns the pipeline builder UI with available normalizers and postprocessors.
+
+**Response:**
+- HTML page with React Flow editor
+
+### POST /api/pipeline/validate
+
+Validates a pipeline configuration.
+
+**Request:**
+```json
+{
+  "input_normalizers": [
+    { "name": "StandardNormalizer", "params": {...} }
+  ],
+  "postprocessors": [
+    { "name": "InstanceSegmentation", "params": {...} }
+  ]
+}
+```
+
+**Response:**
+```json
+{
+  "valid": true,
+  "message": "Pipeline is valid"
+}
+```
+
+### POST /api/pipeline/apply
+
+Applies a validated pipeline to the current inference.
+
+**Request:** Same as validate
+
+**Response:**
+```json
+{
+  "message": "Pipeline applied successfully",
+  "normalizers_applied": 2,
+  "postprocessors_applied": 1
+}
+```
+
+## Development
+
+### Setup
+
+```bash
+cd cellmap_flow/dashboard
+npm install
+npm run build  # Build for production
+npm run dev    # Watch mode for development
+```
+
+### Building the Frontend
+
+React Flow components are bundled using webpack. Run `npm run build` to generate the bundled JavaScript.
+
+## Dependencies
+
+### Frontend
+- **React** 18.2+ - UI library
+- **React DOM** 18.2+ - DOM rendering
+- **React Flow** 11.10+ - Node-based visual editor
+
+### Backend
+- **Flask** - Web framework (already in CellMapFlow)
+- **Pydantic** - Configuration validation (already in CellMapFlow)
+
+## Future Enhancements
+
+- [ ] Real-time pipeline preview/simulation
+- [ ] Custom node templates for complex operations
+- [ ] Pipeline library/templates for common workflows
+- [ ] Performance profiling for pipeline execution
+- [ ] Undo/Redo functionality
+- [ ] Keyboard shortcuts for faster node creation
+- [ ] Search/filter for large model lists
+
+## Troubleshooting
+
+### Pipeline not applying
+- Check browser console for errors
+- Verify all node names match available normalizers/postprocessors
+- Ensure JSON parameter format is valid
+
+### Import fails
+- Ensure file is valid YAML or JSON
+- Check that all referenced models exist in your installation
+
+### Styling issues
+- Clear browser cache
+- Rebuild with `npm run build`
diff --git a/cellmap_flow/dashboard/app.py b/cellmap_flow/dashboard/app.py
index 901a1e9..154b406 100644
--- a/cellmap_flow/dashboard/app.py
+++ b/cellmap_flow/dashboard/app.py
@@ -6,12 +6,17 @@
 from flask import Flask, request, jsonify, render_template
 from flask_cors import CORS
 import logging
+import subprocess
+import yaml
+import tempfile
+import re
 from cellmap_flow.utils.web_utils import get_free_port
 from cellmap_flow.norm.input_normalize import (
     get_input_normalizers,
     get_normalizations,
 )
 from cellmap_flow.post.postprocessors import get_postprocessors_list, get_postprocessors
+from cellmap_flow.models.model_merger import get_model_mergers_list
 from cellmap_flow.utils.load_py import load_safe_config
 from cellmap_flow.utils.scale_pyramid import get_raw_layer
 from cellmap_flow.utils.web_utils import (
@@ -34,6 +39,12 @@
 CORS(app)
 NEUROGLANCER_URL = None
 INFERENCE_SERVER = None
+
+# Blockwise task directory will be set from globals or use default
+def get_blockwise_tasks_dir():
+    tasks_dir = getattr(g, 'blockwise_tasks_dir', None) or os.path.expanduser("~/.cellmap_flow/blockwise_tasks")
+    os.makedirs(tasks_dir, exist_ok=True)
+    return tasks_dir
 CUSTOM_CODE_FOLDER = os.path.expanduser(
     os.environ.get(
         "CUSTOM_CODE_FOLDER",
@@ -47,6 +58,7 @@ def index():
     # Render the main page with tabs
     input_norms = get_input_normalizers()
     output_postprocessors = get_postprocessors_list()
+    model_mergers = get_model_mergers_list()
     model_catalog = g.model_catalog
     model_catalog["User"] = {j.model_name: "" for j in g.jobs}
     default_post_process = {d.to_dict()["name"]: d.to_dict() for d in g.postprocess}
@@ -61,6 +73,7 @@ def index():
         inference_servers=INFERENCE_SERVER,
         input_normalizers=input_norms,
         output_postprocessors=output_postprocessors,
+        model_mergers=model_mergers,
         default_post_process=default_post_process,
         default_input_norm=default_input_norm,
         model_catalog=model_catalog,
@@ -68,6 +81,192 @@ def index():
     )
 
 
+@app.route("/pipeline-builder")
+def pipeline_builder():
+    """Render the drag-and-drop pipeline builder interface with current state from globals"""
+    input_norms = get_input_normalizers()
+    output_postprocessors = get_postprocessors_list()
+
+    # Get available models from models_config (not from catalog)
+    available_models = {}
+    # if hasattr(g, 'models_config') and g.models_config:
+    #     for model_config in g.models_config:
+    #         model_dict = model_config.to_dict()
+    #         available_models[model_config.name] = model_dict
+    
+    logger.warning(f"\n{'='*80}")
+    logger.warning(f"AVAILABLE MODELS DEBUG:")
+    logger.warning(f"  Initial available_models keys: {list(available_models.keys())}")
+    logger.warning(f"  g.models_config: {g.models_config if hasattr(g, 'models_config') else 'NOT SET'}")
+    logger.warning(f"  Sample model with config:")
+    for model_name, model_data in list(available_models.items())[:1]:
+        logger.warning(f"    {model_name}: {model_data}")
+    models_with_config = {}
+    for model_name in available_models.keys():
+        # Find matching config (strip _server suffix for matching)
+        model_name_stripped = model_name.replace('_server', '')
+        for model_config in g.models_config:
+            config_name = getattr(model_config, 'name', '').replace('_server', '')
+            if config_name == model_name_stripped:
+                if hasattr(model_config, 'to_dict'):
+                    models_with_config[model_name] = {
+                        'name': model_name,
+                        'config': model_config.to_dict()
+                    }
+                break
+        # If no config found, just use the name
+        if model_name not in models_with_config:
+            models_with_config[model_name] = {'name': model_name}
+    available_models = models_with_config
+
+    # Check if we have stored pipeline state from previous apply
+    if hasattr(g, 'pipeline_normalizers') and len(g.pipeline_normalizers) > 0:
+        # Use stored pipeline state (includes IDs, positions, params)
+        current_normalizers = g.pipeline_normalizers
+        current_postprocessors = g.pipeline_postprocessors
+        current_models = g.pipeline_models
+        # Enrich current_models with config from g.models_config if available
+        if hasattr(g, 'models_config') and g.models_config:
+            for model_dict in current_models:
+                if 'config' not in model_dict:
+                    # Strip _server suffix for matching
+                    model_name = model_dict['name'].replace('_server', '')
+                    for model_config in g.models_config:
+                        config_name = getattr(model_config, 'name', '').replace('_server', '')
+                        if config_name == model_name:
+                            if hasattr(model_config, 'to_dict'):
+                                model_dict['config'] = model_config.to_dict()
+                            break
+        current_inputs = g.pipeline_inputs
+        current_outputs = g.pipeline_outputs
+        current_edges = g.pipeline_edges
+    else:
+        # Fall back to converting from globals.input_norms and globals.postprocess
+        current_normalizers = []
+        for idx, norm in enumerate(g.input_norms):
+            norm_dict = norm.to_dict() if hasattr(norm, 'to_dict') else {'name': str(norm)}
+            norm_name = norm_dict.get('name', str(norm))
+            # Extract params: all dict items except 'name'
+            params = {k: v for k, v in norm_dict.items() if k != 'name'}
+            current_normalizers.append({
+                'id': f'norm-{idx}-{int(time.time()*1000)}',
+                'name': norm_name,
+                'params': params
+            })
+
+        # Current models (from jobs and models_config)
+        current_models = []
+        logger.warning(f"\n{'='*80}")
+        logger.warning(f"Building current_models from g.jobs:")
+        logger.warning(f"  g.jobs count: {len(g.jobs)}")
+        logger.warning(f"  g.models_config exists: {hasattr(g, 'models_config')}")
+        if hasattr(g, 'models_config'):
+            logger.warning(f"  g.models_config count: {len(g.models_config) if g.models_config else 0}")
+            logger.warning(f"  g.models_config type: {type(g.models_config)}")
+            logger.warning(f"  g.models_config value: {g.models_config}")
+            if g.models_config:
+                logger.warning(f"  g.models_config names: {[getattr(mc, 'name', 'NO_NAME') for mc in g.models_config]}")
+                for mc in g.models_config:
+                    logger.warning(f"    Config object: {mc}, has to_dict: {hasattr(mc, 'to_dict')}")
+        
+        # If models_config is empty but we have jobs, try to get configs from model_catalog
+        if (not hasattr(g, 'models_config') or not g.models_config) and hasattr(g, 'model_catalog'):
+            logger.warning(f"  models_config is empty, checking model_catalog for configs...")
+            # Check if available_models dict has configs
+            if available_models:
+                logger.warning(f"  available_models has {len(available_models)} entries with potential configs")
+
+        for idx, job in enumerate(g.jobs):
+            if hasattr(job, 'model_name'):
+                logger.warning(f"\n  Processing job {idx}: model_name={job.model_name}")
+                model_dict = {'id': f'model-{idx}-{int(time.time()*1000)}', 'name': job.model_name, 'params': {}}
+                # Try to find the corresponding ModelConfig to get full configuration
+                config_found = False
+                
+                # First try g.models_config
+                if hasattr(g, 'models_config') and g.models_config:
+                    # Strip _server suffix for matching
+                    job_model_name = job.model_name.replace('_server', '')
+                    for model_config in g.models_config:
+                        model_config_name = getattr(model_config, 'name', None)
+                        config_name_stripped = model_config_name.replace('_server', '') if model_config_name else None
+                        logger.warning(f"    Checking model_config: {model_config_name} (stripped: {config_name_stripped}) vs job: {job.model_name} (stripped: {job_model_name})")
+                        if config_name_stripped and config_name_stripped == job_model_name:
+                            # Export the full model config using to_dict()
+                            if hasattr(model_config, 'to_dict'):
+                                model_dict['config'] = model_config.to_dict()
+                                logger.warning(f"    ✓ Config attached from models_config: {model_dict['config']}")
+                                config_found = True
+                            break
+                
+                # Fallback: check available_models dict (which was enriched earlier)
+                if not config_found and available_models:
+                    job_model_name = job.model_name.replace('_server', '')
+                    for model_name, model_data in available_models.items():
+                        model_name_stripped = model_name.replace('_server', '')
+                        logger.warning(f"    Checking available_models: {model_name} (stripped: {model_name_stripped}) vs job: {job.model_name} (stripped: {job_model_name})")
+                        if model_name_stripped == job_model_name and isinstance(model_data, dict) and 'config' in model_data:
+                            model_dict['config'] = model_data['config']
+                            logger.warning(f"    ✓ Config attached from available_models: {model_dict['config']}")
+                            config_found = True
+                            break
+                
+                # Second fallback: check previously saved pipeline_model_configs
+                if not config_found and hasattr(g, 'pipeline_model_configs'):
+                    job_model_name = job.model_name.replace('_server', '')
+                    for saved_name, saved_config in g.pipeline_model_configs.items():
+                        saved_name_stripped = saved_name.replace('_server', '')
+                        logger.warning(f"    Checking pipeline_model_configs: {saved_name} (stripped: {saved_name_stripped}) vs job: {job.model_name} (stripped: {job_model_name})")
+                        if saved_name_stripped == job_model_name:
+                            model_dict['config'] = saved_config
+                            logger.warning(f"    ✓ Config attached from pipeline_model_configs: {model_dict['config']}")
+                            config_found = True
+                            break
+                
+                if not config_found:
+                    logger.warning(f"    ✗ No matching config found for {job.model_name}")
+                    logger.warning(f"       TIP: Import a YAML with full model configs to populate g.pipeline_model_configs")
+                current_models.append(model_dict)
+        logger.warning(f"{'='*80}\n")
+
+        current_postprocessors = []
+        for idx, post in enumerate(g.postprocess):
+            post_dict = post.to_dict() if hasattr(post, 'to_dict') else {'name': str(post)}
+            post_name = post_dict.get('name', str(post))
+            # Extract params: all dict items except 'name'
+            params = {k: v for k, v in post_dict.items() if k != 'name'}
+            current_postprocessors.append({
+                'id': f'post-{idx}-{int(time.time()*1000)}',
+                'name': post_name,
+                'params': params
+            })
+
+        current_inputs = []
+        current_outputs = []
+        current_edges = []
+
+    # Get current dataset_path from globals
+    dataset_path = getattr(g, 'dataset_path', None) or ''
+    
+    # Get available model mergers
+    model_mergers = get_model_mergers_list()
+
+    return render_template(
+        "pipeline_builder_v2.html",
+        input_normalizers=input_norms or {},
+        available_models=available_models or {},
+        output_postprocessors=output_postprocessors or {},
+        model_mergers=model_mergers or {},
+        current_normalizers=current_normalizers,
+        current_models=current_models,
+        current_postprocessors=current_postprocessors,
+        current_inputs=current_inputs,
+        current_outputs=current_outputs,
+        current_edges=current_edges,
+        dataset_path=dataset_path,
+    )
+
+
 def is_output_segmentation():
     if len(g.postprocess) == 0:
         return False
@@ -177,6 +376,464 @@ def process():
     )
 
 
+@app.route("/api/pipeline/validate", methods=["POST"])
+def validate_pipeline():
+    """Validate a pipeline configuration"""
+    try:
+        data = request.get_json()
+
+        # Validate normalizers
+        normalizer_names = [n.get("name") for n in data.get("input_normalizers", [])]
+        available_norms = get_input_normalizers()
+        # Extract just the normalizer names from the list of dicts
+        available_norm_names = [norm["name"] for norm in available_norms]
+        for norm_name in normalizer_names:
+            if norm_name not in available_norm_names:
+                return jsonify(
+                    {"valid": False, "error": f"Unknown normalizer: {norm_name}"}
+                ), 400
+
+        # Validate postprocessors
+        processor_names = [p.get("name") for p in data.get("postprocessors", [])]
+        available_procs = get_postprocessors_list()
+        # Extract just the postprocessor names from the list of dicts
+        available_proc_names = [proc["name"] for proc in available_procs]
+        for proc_name in processor_names:
+            if proc_name not in available_proc_names:
+                return jsonify(
+                    {"valid": False, "error": f"Unknown postprocessor: {proc_name}"}
+                ), 400
+
+        return jsonify({"valid": True, "message": "Pipeline is valid"})
+
+    except Exception as e:
+        logger.error(f"Error validating pipeline: {e}")
+        return jsonify({"valid": False, "error": str(e)}), 500
+
+
+@app.route("/api/dataset-path", methods=["GET", "POST"])
+def dataset_path_api():
+    """Get or set the dataset path in globals"""
+    if request.method == "GET":
+        dataset_path = getattr(g, 'dataset_path', None) or ''
+        return jsonify({'dataset_path': dataset_path})
+    elif request.method == "POST":
+        data = request.get_json()
+        dataset_path = data.get('dataset_path', '')
+        g.dataset_path = dataset_path
+        logger.warning(f"Dataset path updated to: {dataset_path}")
+        return jsonify({'success': True, 'dataset_path': g.dataset_path})
+
+
+@app.route("/api/blockwise-config", methods=["GET", "POST"])
+def blockwise_config_api():
+    """Get or set blockwise configuration in globals"""
+    if request.method == "GET":
+        return jsonify({
+            'queue': g.queue,
+            'charge_group': g.charge_group,
+            'nb_cores_master': g.nb_cores_master,
+            'nb_cores_worker': g.nb_cores_worker,
+            'nb_workers': g.nb_workers,
+            'tmp_dir': g.tmp_dir,
+            'blockwise_tasks_dir': g.blockwise_tasks_dir
+        })
+    elif request.method == "POST":
+        data = request.get_json()
+        g.queue = data.get('queue')
+        g.charge_group = data.get('charge_group')
+        g.nb_cores_master = int(data.get('nb_cores_master'))
+        g.nb_cores_worker = int(data.get('nb_cores_worker'))
+        g.nb_workers = int(data.get('nb_workers'))
+        g.tmp_dir = data.get('tmp_dir')
+        g.blockwise_tasks_dir = data.get('blockwise_tasks_dir')
+        logger.warning(f"Blockwise config updated: queue={g.queue}, charge_group={g.charge_group}, cores_master={g.nb_cores_master}, cores_worker={g.nb_cores_worker}, workers={g.nb_workers}, tmp_dir={g.tmp_dir}, blockwise_tasks_dir={g.blockwise_tasks_dir}")
+        return jsonify({'success': True, 'config': {
+            'queue': g.queue,
+            'charge_group': g.charge_group,
+            'nb_cores_master': g.nb_cores_master,
+            'nb_cores_worker': g.nb_cores_worker,
+            'nb_workers': g.nb_workers,
+            'tmp_dir': g.tmp_dir,
+            'blockwise_tasks_dir': g.blockwise_tasks_dir
+        }})
+
+
+@app.route("/api/pipeline/apply", methods=["POST"])
+def apply_pipeline():
+    """Apply a pipeline configuration to the current inference"""
+    try:
+        data = request.get_json()
+        logger.warning(f"\n{'='*80}")
+        logger.warning(f"APPLY PIPELINE - Received data:")
+        logger.warning(f"  Input normalizers: {data.get('input_normalizers', [])}")
+        logger.warning(f"  Postprocessors: {data.get('postprocessors', [])}")
+
+        # Validate first
+        validation = validate_pipeline_config(data)
+        if not validation["valid"]:
+            return jsonify(validation), 400
+
+        # Apply normalizers
+        input_norms_config = {
+            n["name"]: n.get("params", {}) for n in data.get("input_normalizers", [])
+        }
+        logger.warning(f"\nNormalizers config dict: {input_norms_config}")
+        g.input_norms = get_normalizations(input_norms_config)
+
+        # Apply postprocessors
+        postprocs_config = {
+            p["name"]: p.get("params", {}) for p in data.get("postprocessors", [])
+        }
+        logger.warning(f"Postprocessors config dict: {postprocs_config}")
+        g.postprocess = get_postprocessors(postprocs_config)
+
+        # Save complete pipeline visual state to globals
+        g.pipeline_inputs = data.get("inputs", [])
+        g.pipeline_outputs = data.get("outputs", [])
+        g.pipeline_edges = data.get("edges", [])
+        g.pipeline_normalizers = data.get("input_normalizers", [])
+        g.pipeline_models = data.get("models", [])
+        g.pipeline_postprocessors = data.get("postprocessors", [])
+        
+        # Also save model configs separately for easier access
+        if not hasattr(g, 'pipeline_model_configs'):
+            g.pipeline_model_configs = {}
+        for model in data.get("models", []):
+            if 'config' in model and model['config']:
+                g.pipeline_model_configs[model['name']] = model['config']
+
+        # Log the updated globals state
+        logger.warning(f"\n{'='*80}")
+        logger.warning(f"UPDATED GLOBALS (g) STATE:")
+        logger.warning(f"{'='*80}")
+        logger.warning(f"\ng.input_norms ({len(g.input_norms)} items):")
+        for idx, norm in enumerate(g.input_norms):
+            logger.warning(f"  [{idx}] {norm}")
+
+        logger.warning(f"\ng.postprocess ({len(g.postprocess)} items):")
+        for idx, post in enumerate(g.postprocess):
+            logger.warning(f"  [{idx}] {post}")
+
+        logger.warning(f"\ng.jobs ({len(g.jobs)} items):")
+        for idx, job in enumerate(g.jobs):
+            logger.warning(f"  [{idx}] model_name={getattr(job, 'model_name', 'N/A')}, host={getattr(job, 'host', 'N/A')}")
+
+        logger.warning(f"\ng.pipeline_inputs ({len(g.pipeline_inputs)} items): {g.pipeline_inputs}")
+        logger.warning(f"\ng.pipeline_outputs ({len(g.pipeline_outputs)} items): {g.pipeline_outputs}")
+        logger.warning(f"\ng.pipeline_edges ({len(g.pipeline_edges)} items): {g.pipeline_edges}")
+        logger.warning(f"\ng.pipeline_normalizers ({len(g.pipeline_normalizers)} items): {g.pipeline_normalizers}")
+        logger.warning(f"\ng.pipeline_models ({len(g.pipeline_models)} items): {g.pipeline_models}")
+        logger.warning(f"\ng.pipeline_postprocessors ({len(g.pipeline_postprocessors)} items): {g.pipeline_postprocessors}")
+
+        logger.warning(f"{'='*80}\n")
+
+        return jsonify({
+            "message": "Pipeline applied successfully",
+            "normalizers_applied": len(g.input_norms),
+            "postprocessors_applied": len(g.postprocess),
+        })
+
+    except Exception as e:
+        logger.error(f"Error applying pipeline: {e}")
+        return jsonify({"error": str(e)}), 500
+
+
+def validate_pipeline_config(config):
+    """Helper function to validate pipeline configuration"""
+    try:
+        normalizer_names = [n.get("name") for n in config.get("input_normalizers", [])]
+        available_norms = get_input_normalizers()
+        # Extract just the normalizer names from the list of dicts
+        available_norm_names = [norm["name"] for norm in available_norms]
+        for norm_name in normalizer_names:
+            if norm_name not in available_norm_names:
+                return {"valid": False, "error": f"Unknown normalizer: {norm_name}"}
+
+        processor_names = [p.get("name") for p in config.get("postprocessors", [])]
+        available_procs = get_postprocessors_list()
+        # Extract just the postprocessor names from the list of dicts
+        available_proc_names = [proc["name"] for proc in available_procs]
+        for proc_name in processor_names:
+            if proc_name not in available_proc_names:
+                return {"valid": False, "error": f"Unknown postprocessor: {proc_name}"}
+
+        return {"valid": True}
+
+    except Exception as e:
+        return {"valid": False, "error": str(e)}
+
+
+@app.route("/api/blockwise/validate", methods=["POST"])
+def validate_blockwise():
+    """Validate if pipeline is ready for blockwise processing"""
+    try:
+        data = request.get_json()
+        pipeline = data.get("pipeline", {})
+        
+        # Check required components
+        if not pipeline.get("inputs") or len(pipeline["inputs"]) == 0:
+            return {"valid": False, "error": "No input nodes defined"}
+        
+        if not pipeline.get("outputs") or len(pipeline["outputs"]) == 0:
+            return {"valid": False, "error": "No output nodes defined"}
+        
+        if not pipeline.get("models") or len(pipeline["models"]) == 0:
+            return {"valid": False, "error": "No models defined"}
+        
+        # Check blockwise config
+        if not pipeline.get("blockwise_config") or len(pipeline["blockwise_config"]) == 0:
+            return {"valid": False, "error": "No blockwise configuration defined"}
+        
+        # Check input has dataset_path
+        input_node = pipeline["inputs"][0]
+        if not input_node.get("params", {}).get("dataset_path"):
+            return {"valid": False, "error": "Input node missing dataset_path"}
+        
+        # Check output has dataset_path
+        output_node = pipeline["outputs"][0]
+        if not output_node.get("params", {}).get("dataset_path"):
+            return {"valid": False, "error": "Output node missing dataset_path"}
+        
+        logger.info("Pipeline validation passed")
+        return {"valid": True, "message": "Pipeline is ready for blockwise processing"}
+        
+    except Exception as e:
+        logger.error(f"Validation error: {str(e)}")
+        return {"valid": False, "error": str(e)}
+
+
+@app.route("/api/blockwise/generate", methods=["POST"])
+def generate_blockwise_task():
+    """Generate blockwise task YAML files"""
+    try:
+        data = request.get_json()
+        pipeline = data.get("pipeline", {})
+        
+        # First validate
+        validation = validate_blockwise()
+        if not validation.get("valid"):
+            return {"success": False, "error": validation.get("error")}
+        
+        # Get blockwise config
+        blockwise_config = pipeline["blockwise_config"][0]
+        input_node = pipeline["inputs"][0]
+        output_node = pipeline["outputs"][0]
+        
+        # Create task YAML content
+        task_name = f"cellmap_flow_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+        task_yaml = {
+            "data_path": input_node["params"]["dataset_path"],
+            "output_path": output_node["params"]["dataset_path"],
+            "task_name": task_name,
+            "charge_group": blockwise_config["params"]["charge_group"],
+            "queue": blockwise_config["params"]["queue"],
+            "workers": blockwise_config["params"]["nb_workers"],
+            "cpu_workers": blockwise_config["params"]["nb_cores_worker"],
+            "tmp_dir": blockwise_config["params"]["tmp_dir"],
+            "models": [],
+            "input_normalizers": [],
+            "postprocessors": []
+        }
+        
+        # Add model_mode if multiple models are present and a merge mode is selected
+        model_count = len(pipeline.get("models", []))
+        model_mode = pipeline.get("model_mode", "")
+        if model_count > 1 and model_mode:
+            task_yaml["model_mode"] = model_mode
+            logger.info(f"Adding model_mode: {model_mode} for {model_count} models")
+        
+        # Add models with full config
+        for model in pipeline.get("models", []):
+            model_entry = {
+                "name": model.get("name"),
+                **model.get("params", model.get("config", {}))
+            }
+            # Parse string representations of lists/tuples back to actual lists for specific fields
+            import ast
+            import re
+            for field in ["channels", "input_size", "output_size", "input_voxel_size", "output_voxel_size"]:
+                if field in model_entry:
+                    value = model_entry[field]
+                    # If it's already a list, keep it
+                    if isinstance(value, (list, tuple)):
+                        model_entry[field] = list(value)
+                        logger.info(f"Field {field} is already a list: {model_entry[field]}")
+                    # If it's a string that looks like a list/tuple, parse it
+                    elif isinstance(value, str):
+                        value_stripped = value.strip().strip("'\"")  # Remove outer quotes
+                        if (value_stripped.startswith('[') or value_stripped.startswith('(')) and \
+                           (value_stripped.endswith(']') or value_stripped.endswith(')')):
+                            try:
+                                # Fix unquoted identifiers: convert [mito] to ['mito']
+                                # Replace word characters not inside quotes with quoted versions
+                                fixed_value = re.sub(r'\b([a-zA-Z_][a-zA-Z0-9_]*)\b', r"'\1'", value_stripped)
+                                # Remove duplicate quotes: ''mito'' -> 'mito'
+                                fixed_value = re.sub(r"''+", "'", fixed_value)
+                                logger.info(f"Fixing {field}: {value_stripped!r} -> {fixed_value!r}")
+                                
+                                parsed = ast.literal_eval(fixed_value)
+                                if isinstance(parsed, (list, tuple)):
+                                    model_entry[field] = list(parsed)
+                                    logger.info(f"Parsed {field} from string {value!r} to list {model_entry[field]}")
+                            except Exception as e:
+                                logger.warning(f"Failed to parse {field}: {value}, error: {e}")
+                    
+            task_yaml["models"].append(model_entry)
+        
+        # Add normalizers
+        for norm in pipeline.get("normalizers", []):
+            norm_entry = {
+                "name": norm.get("name"),
+                "params": norm.get("params", {})
+            }
+            task_yaml["input_normalizers"].append(norm_entry)
+        
+        # Add postprocessors
+        for post in pipeline.get("postprocessors", []):
+            post_entry = {
+                "name": post.get("name"),
+                "params": post.get("params", {})
+            }
+            task_yaml["postprocessors"].append(post_entry)
+        
+        # Add output_channels from OUTPUT node if configured
+        output_channels = output_node.get("params", {}).get("output_channels", [])
+        if output_channels and isinstance(output_channels, list) and len(output_channels) > 0:
+            task_yaml["output_channels"] = output_channels
+            logger.info(f"Adding output_channels to YAML: {output_channels}")
+        
+        # Convert to YAML format with proper list handling
+        yaml_content = yaml.dump(task_yaml, default_flow_style=False, allow_unicode=True)
+        
+        # Save to file
+        yaml_filename = f"{task_name}.yaml"
+        tasks_dir = get_blockwise_tasks_dir()
+        yaml_path = os.path.join(tasks_dir, yaml_filename)
+        with open(yaml_path, 'w') as f:
+            f.write(yaml_content)
+        
+        logger.info(f"Generated blockwise task YAML at: {yaml_path}")
+        logger.info(f"Task YAML content:\n{yaml_content}")
+        
+        return {
+            "success": True,
+            "task_yaml": yaml_content,
+            "task_config": task_yaml,
+            "task_path": yaml_path,
+            "task_name": task_name,
+            "message": "Blockwise task generated successfully"
+        }
+        
+    except Exception as e:
+        logger.error(f"Task generation error: {str(e)}")
+        return {"success": False, "error": str(e)}
+
+
+@app.route("/api/blockwise/precheck", methods=["POST"])
+def precheck_blockwise_task():
+    """Precheck blockwise task configuration"""
+    try:
+        from cellmap_flow.blockwise.blockwise_processor import CellMapFlowBlockwiseProcessor
+        
+        data = request.get_json()
+        pipeline = data.get("pipeline", {})
+        
+        # First validate
+        validation = validate_blockwise()
+        if not validation.get("valid"):
+            return {"success": False, "error": validation.get("error")}
+        
+        # Generate task YAML first
+        gen_result = generate_blockwise_task()
+        if not gen_result.get("success"):
+            return {"success": False, "error": gen_result.get("error")}
+        
+        yaml_path = gen_result.get("task_path")
+        
+        # Try to instantiate the processor to validate configuration
+        try:
+            _ = CellMapFlowBlockwiseProcessor(yaml_path, create=True)
+            logger.info(f"Blockwise precheck passed for: {yaml_path}")
+            return {
+                "success": True,
+                "message": "success"
+            }
+        except Exception as e:
+            logger.error(f"Blockwise precheck failed: {str(e)}")
+            return {"success": False, "error": str(e)}
+        
+    except Exception as e:
+        logger.error(f"Precheck error: {str(e)}")
+        return {"success": False, "error": str(e)}
+
+
+@app.route("/api/blockwise/submit", methods=["POST"])
+def submit_blockwise_task():
+    """Submit blockwise task to LSF"""
+    try:
+        data = request.get_json()
+        pipeline = data.get("pipeline", {})
+        job_name = data.get("job_name", f"cellmap_flow_{int(time.time())}")
+        
+        # First validate
+        validation = validate_blockwise()
+        if not validation.get("valid"):
+            return {"success": False, "error": validation.get("error")}
+        
+        # Generate task YAML
+        gen_result = generate_blockwise_task()
+        if not gen_result.get("success"):
+            return {"success": False, "error": gen_result.get("error")}
+        
+        yaml_path = gen_result["task_path"]
+        blockwise_config = pipeline["blockwise_config"][0]
+        
+        # Build bsub command
+        cores_master = blockwise_config["params"]["nb_cores_master"]
+        charge_group = blockwise_config["params"]["charge_group"]
+        queue = blockwise_config["params"]["queue"]
+        
+        bsub_cmd = [
+            "bsub",
+            "-J", job_name,
+            "-n", str(cores_master),
+            "-P", charge_group,
+            # "-q", queue,
+            "python", "-m", "cellmap_flow.blockwise.cli",
+            yaml_path
+        ]
+        
+        logger.info(f"Submitting LSF job: {' '.join(bsub_cmd)}")
+        
+        # Submit job - use same environment as parent process
+        result = subprocess.run(bsub_cmd, capture_output=True, text=True, env=os.environ)
+        
+        if result.returncode == 0:
+            output = result.stdout.strip()
+            logger.info(f"Job submitted successfully: {output}")
+            
+            # Extract job ID from bsub output (format: "Job <12345> is submitted")
+            match = re.search(r'<(\d+)>', output)
+            job_id = match.group(1) if match else "unknown"
+            
+            return {
+                "success": True,
+                "job_id": job_id,
+                "task_path": yaml_path,
+                "command": " ".join(bsub_cmd),
+                "message": f"Task submitted as job {job_id}"
+                }
+        else:
+            error_msg = result.stderr or result.stdout
+            logger.error(f"LSF submission failed: {error_msg}")
+            return {"success": False, "error": f"LSF error: {error_msg}"}
+        
+    except Exception as e:
+        logger.error(f"Submission error: {str(e)}")
+        return {"success": False, "error": str(e)}
+
+
 def create_and_run_app(neuroglancer_url=None, inference_servers=None):
     global NEUROGLANCER_URL, INFERENCE_SERVER
     NEUROGLANCER_URL = neuroglancer_url
diff --git a/cellmap_flow/dashboard/package.json b/cellmap_flow/dashboard/package.json
new file mode 100644
index 0000000..168dba6
--- /dev/null
+++ b/cellmap_flow/dashboard/package.json
@@ -0,0 +1,21 @@
+{
+  "name": "cellmap-flow-pipeline-builder",
+  "version": "0.1.0",
+  "description": "Drag-and-drop pipeline builder for CellMapFlow",
+  "dependencies": {
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
+    "reactflow": "^11.10.0"
+  },
+  "devDependencies": {
+    "@babel/preset-react": "^7.22.0",
+    "@babel/preset-env": "^7.22.0",
+    "webpack": "^5.88.0",
+    "webpack-cli": "^5.1.0",
+    "babel-loader": "^9.1.3"
+  },
+  "scripts": {
+    "build": "webpack --mode production",
+    "dev": "webpack --mode development --watch"
+  }
+}
diff --git a/cellmap_flow/dashboard/static/css/pipeline_builder.css b/cellmap_flow/dashboard/static/css/pipeline_builder.css
new file mode 100644
index 0000000..57f55d4
--- /dev/null
+++ b/cellmap_flow/dashboard/static/css/pipeline_builder.css
@@ -0,0 +1,726 @@
+
+        :root {
+            --bg-primary: #0f172a;
+            --bg-secondary: #1e293b;
+            --bg-tertiary: #334155;
+            --text-primary: #f1f5f9;
+            --text-secondary: #cbd5e1;
+            --border-color: #475569;
+            --accent-blue: #3b82f6;
+            --accent-purple: #8b5cf6;
+            --accent-green: #10b981;
+            --accent-red: #ef4444;
+        }
+
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
+            background: var(--bg-primary);
+            color: var(--text-primary);
+            height: 100vh;
+            overflow: hidden;
+        }
+
+        .app-container {
+            display: flex;
+            height: 100vh;
+            flex-direction: column;
+        }
+
+        .header {
+            background: var(--bg-secondary);
+            border-bottom: 1px solid var(--border-color);
+            padding: 16px 24px;
+            display: flex;
+            align-items: center;
+            justify-content: space-between;
+            box-shadow: 0 2px 8px rgba(0, 0, 0, 0.3);
+        }
+
+        .header h1 {
+            font-size: 20px;
+            font-weight: 600;
+            margin: 0;
+        }
+
+        .apply-btn {
+            background: linear-gradient(135deg, #10b981 0%, #059669 100%);
+            color: white;
+            border: none;
+            padding: 8px 16px;
+            border-radius: 6px;
+            cursor: pointer;
+            font-size: 14px;
+            font-weight: 500;
+            transition: all 0.2s;
+        }
+
+        .apply-btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 4px 12px rgba(16, 185, 129, 0.3);
+        }
+
+        .header-actions {
+            display: flex;
+            gap: 8px;
+            align-items: center;
+        }
+
+        .action-btn {
+            background: var(--bg-primary);
+            color: var(--text-primary);
+            border: 1px solid var(--border-color);
+            padding: 8px 14px;
+            border-radius: 6px;
+            cursor: pointer;
+            font-size: 13px;
+            font-weight: 500;
+            transition: all 0.2s;
+        }
+
+        .action-btn:hover {
+            background: var(--bg-hover);
+            border-color: #64748b;
+        }
+
+        .action-btn:disabled {
+            opacity: 0.6;
+            cursor: not-allowed;
+            color: var(--text-secondary);
+        }
+
+        .action-btn:disabled:hover {
+            background: var(--bg-primary);
+            border-color: var(--border-color);
+            transform: none;
+        }
+
+        .import-btn {
+            background: var(--bg-primary);
+            color: var(--text-primary);
+            border: 1px solid var(--border-color);
+            padding: 8px 14px;
+            border-radius: 6px;
+            cursor: pointer;
+            font-size: 13px;
+            font-weight: 500;
+            transition: all 0.2s;
+            display: inline-block;
+        }
+
+        .import-btn:hover {
+            background: var(--bg-hover);
+            border-color: #64748b;
+        }
+
+        .file-input {
+            display: none;
+        }
+
+        .main {
+            display: flex;
+            flex: 1;
+            overflow: hidden;
+            gap: 0;
+        }
+
+        /* Sidebar */
+        .sidebar {
+            width: 280px;
+            background: var(--bg-secondary);
+            border-right: 1px solid var(--border-color);
+            overflow-y: auto;
+            padding: 16px;
+            display: flex;
+            flex-direction: column;
+            gap: 16px;
+        }
+
+        .library-section {
+            display: flex;
+            flex-direction: column;
+            gap: 8px;
+        }
+
+        .section-title {
+            font-size: 12px;
+            font-weight: 600;
+            text-transform: uppercase;
+            letter-spacing: 1px;
+            color: var(--text-secondary);
+            opacity: 0.7;
+            margin-bottom: 4px;
+        }
+
+        .section-toggle {
+            background: var(--bg-tertiary);
+            border: 1px solid var(--border-color);
+            color: var(--text-primary);
+            padding: 8px 12px;
+            border-radius: 6px;
+            cursor: pointer;
+            font-size: 13px;
+            font-weight: 500;
+            transition: all 0.2s;
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+        }
+
+        .section-toggle:hover {
+            background: var(--accent-blue);
+            border-color: var(--accent-blue);
+        }
+
+        .section-toggle.active {
+            background: var(--accent-blue);
+            border-color: var(--accent-blue);
+        }
+
+        .library-items {
+            display: flex;
+            flex-direction: column;
+            gap: 6px;
+            max-height: 200px;
+            overflow-y: auto;
+            display: none;
+        }
+
+        .library-items.show {
+            display: flex;
+        }
+
+        .library-item {
+            background: var(--bg-primary);
+            border: 1px solid var(--border-color);
+            padding: 8px 12px;
+            border-radius: 4px;
+            cursor: grab;
+            font-size: 12px;
+            transition: all 0.2s;
+            user-select: none;
+        }
+
+        .library-item:hover {
+            background: var(--accent-blue);
+            border-color: var(--accent-blue);
+            color: white;
+        }
+
+        .library-item:active {
+            cursor: grabbing;
+            opacity: 0.8;
+        }
+
+        /* Canvas */
+        .canvas {
+            flex: 1;
+            background: linear-gradient(135deg, #0f172a 0%, #1a1f35 100%);
+            overflow: auto;
+            padding: 32px;
+            position: relative;
+        }
+
+        .canvas-content {
+            position: relative;
+            width: 100%;
+            height: 100%;
+            min-height: 600px;
+        }
+
+        /* SVG connections layer */
+        .connections-svg {
+            position: absolute;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            pointer-events: none;
+            z-index: 1;
+        }
+
+        .connection-path {
+            fill: none;
+            stroke: rgba(16, 185, 129, 0.6);
+            stroke-width: 3;
+            pointer-events: stroke;
+            cursor: pointer;
+            transition: all 0.2s;
+        }
+
+        .connection-path:hover {
+            stroke: rgba(16, 185, 129, 1);
+            stroke-width: 4;
+            filter: drop-shadow(0 0 8px rgba(16, 185, 129, 0.8));
+        }
+
+        .connection-path.dragging-connection {
+            stroke: rgba(59, 130, 246, 0.8);
+            stroke-dasharray: 5, 5;
+            animation: dash 0.5s linear infinite;
+        }
+
+        @keyframes dash {
+            to {
+                stroke-dashoffset: -10;
+            }
+        }
+
+        /* Nodes */
+        .node-box {
+            background: var(--bg-secondary);
+            border: 2px solid var(--border-color);
+            border-radius: 8px;
+            padding: 16px;
+            min-width: 280px;
+            max-width: 320px;
+            box-shadow: 0 4px 12px rgba(0,0,0,0.3);
+            transition: box-shadow 0.2s;
+            position: absolute;
+            cursor: grab;
+            user-select: none;
+            z-index: 10;
+        }
+
+        .node-box:active {
+            cursor: grabbing;
+        }
+
+        .node-box.dragging {
+            z-index: 1000;
+            box-shadow: 0 8px 24px rgba(59, 130, 246, 0.4);
+            opacity: 0.95;
+        }
+
+        .node-box.dirty {
+            border-color: var(--accent-blue);
+            box-shadow: 0 0 12px rgba(59, 130, 246, 0.3);
+        }
+
+        .node-box:hover {
+            border-color: var(--accent-blue);
+            box-shadow: 0 6px 16px rgba(59, 130, 246, 0.2);
+        }
+
+        /* Special INPUT/OUTPUT nodes */
+        .node-box.io-node {
+            min-width: 120px;
+            max-width: 1600px;
+            padding: 12px;
+            background: linear-gradient(135deg, var(--bg-tertiary), var(--bg-secondary));
+        }
+
+        .node-box.io-node.input-node {
+            border-color: var(--accent-purple);
+        }
+
+        .node-box.io-node.output-node {
+            border-color: var(--accent-green);
+        }
+
+        .node-box.io-node .node-input {
+            display: none;
+        }
+
+        .node-box.io-node.input-node .node-output {
+            display: block;
+        }
+
+        .node-box.io-node.output-node .node-input {
+            display: block;
+        }
+
+        .node-box.io-node.output-node .node-output {
+            display: none;
+        }
+
+        /* Configuration nodes */
+        .node-box.config-node {
+            min-width: 350px;
+            max-width: 450px;
+            background: linear-gradient(135deg, #2a2a3e, var(--bg-secondary));
+            border-color: #f59e0b;
+        }
+
+        .node-box.config-node:hover {
+            border-color: #fbbf24;
+            box-shadow: 0 6px 16px rgba(245, 158, 11, 0.2);
+        }
+
+        .node-box.config-node .node-input,
+        .node-box.config-node .node-output {
+            display: none;
+        }
+
+        /* Node connection dots */
+        .node-input, .node-output {
+            position: absolute;
+            width: 14px;
+            height: 14px;
+            border-radius: 50%;
+            background: radial-gradient(circle at 30% 30%, #10b981, #059669);
+            box-shadow: 0 0 8px rgba(16, 185, 129, 0.5), inset 0 1px 2px rgba(255,255,255,0.2);
+            cursor: crosshair;
+            transition: all 0.2s;
+            top: 50%;
+            transform: translateY(-50%);
+            z-index: 100;
+            pointer-events: all;
+        }
+
+        .node-input:hover, .node-output:hover {
+            transform: translateY(-50%) scale(1.4);
+            box-shadow: 0 0 16px rgba(16, 185, 129, 1), inset 0 1px 2px rgba(255,255,255,0.4);
+        }
+
+        .node-input {
+            left: -9px;
+        }
+
+        .node-output {
+            right: -9px;
+        }
+
+        .node-input.active, .node-output.active {
+            background: radial-gradient(circle at 30% 30%, #3b82f6, #2563eb);
+            box-shadow: 0 0 16px rgba(59, 130, 246, 1);
+            transform: translateY(-50%) scale(1.5);
+        }
+
+        .node-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 12px;
+            padding-bottom: 12px;
+            border-bottom: 1px solid var(--border-color);
+        }
+
+        .node-title {
+            font-size: 14px;
+            font-weight: 600;
+            color: var(--text-primary);
+        }
+
+        .node-type {
+            font-size: 11px;
+            text-transform: uppercase;
+            letter-spacing: 0.5px;
+            color: var(--text-secondary);
+            opacity: 0.7;
+        }
+
+        .node-actions {
+            display: flex;
+            gap: 6px;
+        }
+
+        .icon-btn {
+            background: none;
+            border: none;
+            color: var(--text-secondary);
+            cursor: pointer;
+            font-size: 16px;
+            padding: 4px 8px;
+            border-radius: 4px;
+            transition: all 0.2s;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+        }
+
+        .icon-btn:hover {
+            color: var(--text-primary);
+            background: var(--bg-tertiary);
+        }
+
+        .icon-btn.delete:hover {
+            color: var(--accent-red);
+        }
+
+        .icon-btn.save {
+            display: none;
+        }
+
+        .node-box.dirty .icon-btn.save {
+            display: flex;
+            color: var(--accent-green);
+        }
+
+        .configure-btn {
+            background: var(--bg-tertiary);
+            color: var(--text-primary);
+            border: 1px solid var(--border-color);
+            padding: 6px 10px;
+            border-radius: 4px;
+            cursor: pointer;
+            font-size: 12px;
+            font-weight: 500;
+            width: 100%;
+            transition: all 0.2s;
+        }
+
+        .configure-btn:hover {
+            background: var(--accent-blue);
+            border-color: var(--accent-blue);
+            color: white;
+        }
+
+        .selected-channels {
+            padding: 4px;
+            background: rgba(0, 0, 0, 0.2);
+            border-radius: 3px;
+            max-height: 50px;
+            overflow-y: auto;
+            word-break: break-word;
+        }
+
+        .node-box.dirty .icon-btn.save:hover {
+            color: white;
+            background: var(--accent-green);
+        }
+
+        .node-params {
+            display: flex;
+            flex-direction: column;
+            gap: 10px;
+        }
+
+        .param-group {
+            display: flex;
+            flex-direction: column;
+            gap: 4px;
+        }
+
+        .param-label {
+            font-size: 11px;
+            font-weight: 600;
+            text-transform: uppercase;
+            letter-spacing: 0.5px;
+            color: var(--text-secondary);
+            opacity: 0.8;
+        }
+
+        .param-input {
+            background: var(--bg-primary);
+            border: 1px solid var(--border-color);
+            color: var(--text-primary);
+            padding: 3px 6px;
+            border-radius: 3px;
+            font-size: 11px;
+            font-family: 'Menlo', 'Monaco', monospace;
+            transition: all 0.2s;
+            height: 24px;
+            min-height: 24px;
+        }
+
+        .param-input:focus {
+            outline: none;
+            border-color: var(--accent-blue);
+            box-shadow: 0 0 8px rgba(59, 130, 246, 0.2);
+        }
+
+        .param-input:disabled {
+            opacity: 0.5;
+            cursor: not-allowed;
+        }
+
+        /* Blockwise config fields */
+        .blockwise-fields {
+            display: flex;
+            flex-direction: column;
+            gap: 10px;
+        }
+
+        .field-group {
+            display: flex;
+            flex-direction: column;
+            gap: 4px;
+        }
+
+        /* Message */
+        .message {
+            position: fixed;
+            bottom: 20px;
+            right: 20px;
+            padding: 12px 16px;
+            border-radius: 6px;
+            font-size: 13px;
+            max-width: 400px;
+            animation: slideIn 0.3s ease-out;
+            z-index: 10000;
+        }
+
+        .message.success {
+            background: rgb(16, 185, 129);
+            color: #cae7d5;
+            border: 1px solid rgba(16, 185, 129, 0.4);
+        }
+
+        .message.error {
+            background: rgba(239, 68, 68);
+            color: #fefefe;
+            border: 1px solid rgba(239, 68, 68, 0.4);
+        }
+
+        .message.info {
+            background: rgba(59, 130, 246, 0.2);
+            color: #93c5fd;
+            border: 1px solid rgba(59, 130, 246, 0.4);
+        }
+
+        @keyframes slideIn {
+            from {
+                transform: translateX(400px);
+                opacity: 0;
+            }
+            to {
+                transform: translateX(0);
+                opacity: 1;
+            }
+        }
+
+        /* Modal Styles */
+        .modal-overlay {
+            position: fixed;
+            top: 0;
+            left: 0;
+            right: 0;
+            bottom: 0;
+            background: rgba(0, 0, 0, 0.7);
+            z-index: 5000;
+        }
+
+        .modal {
+            position: fixed;
+            top: 50%;
+            left: 50%;
+            transform: translate(-50%, -50%);
+            z-index: 5001;
+        }
+
+        .modal-content {
+            background: var(--bg-secondary);
+            border: 1px solid var(--border-color);
+            border-radius: 8px;
+            box-shadow: 0 10px 40px rgba(0, 0, 0, 0.5);
+            display: flex;
+            flex-direction: column;
+        }
+
+        .modal-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            padding: 16px;
+            border-bottom: 1px solid var(--border-color);
+        }
+
+        .modal-header h2 {
+            margin: 0;
+            font-size: 18px;
+            font-weight: 600;
+        }
+
+        .modal-close-btn {
+            background: none;
+            border: none;
+            color: var(--text-secondary);
+            font-size: 20px;
+            cursor: pointer;
+            padding: 0;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+        }
+
+        .modal-close-btn:hover {
+            color: var(--text-primary);
+        }
+
+        .modal-body {
+            padding: 16px;
+            flex: 1;
+            overflow-y: auto;
+        }
+
+        .modal-footer {
+            display: flex;
+            gap: 8px;
+            padding: 16px;
+            border-top: 1px solid var(--border-color);
+            justify-content: flex-end;
+        }
+
+        .channel-checkbox-group {
+            display: flex;
+            align-items: center;
+            gap: 8px;
+            padding: 8px;
+            border-radius: 4px;
+            margin-bottom: 8px;
+            background: var(--bg-tertiary);
+        }
+
+        .channel-checkbox-group input[type="checkbox"] {
+            cursor: pointer;
+            width: 16px;
+            height: 16px;
+        }
+
+        .channel-checkbox-group label {
+            cursor: pointer;
+            flex: 1;
+            margin: 0;
+            display: flex;
+            align-items: center;
+            gap: 4px;
+        }
+
+        /* Scrollbars */
+        ::-webkit-scrollbar {
+            width: 8px;
+            height: 8px;
+        }
+
+        ::-webkit-scrollbar-track {
+            background: var(--bg-primary);
+        }
+
+        ::-webkit-scrollbar-thumb {
+            background: var(--border-color);
+            border-radius: 4px;
+        }
+
+        ::-webkit-scrollbar-thumb:hover {
+            background: #64748b;
+        }
+
+        /* Empty state */
+        .empty-state {
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            justify-content: center;
+            height: 100%;
+            color: var(--text-secondary);
+            opacity: 0.5;
+            gap: 12px;
+        }
+
+        .empty-state-icon {
+            font-size: 48px;
+            opacity: 0.3;
+        }
+
+        .empty-state-text {
+            font-size: 14px;
+            text-align: center;
+            max-width: 300px;
+            line-height: 1.5;
+        }
\ No newline at end of file
diff --git a/cellmap_flow/dashboard/static/js/bundle-pipeline.js b/cellmap_flow/dashboard/static/js/bundle-pipeline.js
new file mode 100644
index 0000000..381a974
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/bundle-pipeline.js
@@ -0,0 +1,324 @@
+// In-browser bundle for the pipeline builder — uses UMD React and ReactFlow globals
+// This file is transpiled by Babel in the browser (development convenience).
+
+// Defer accessing ReactFlow UMD globals until runtime to avoid errors
+let ReactFlowProvider, ReactFlow, addEdge, useNodesState, useEdgesState, Controls, Background, useReactFlow, Handle, Position;
+const { useCallback, useState } = React;
+
+const nodeTypes = {
+  normalizer: PipelineNode,
+  postprocessor: PipelineNode,
+  input: PipelineNode,
+  output: PipelineNode,
+};
+
+function PipelineNode({ data, selected, id }) {
+  const nodeColors = {
+    input: '#90EE90',
+    normalizer: '#87CEEB',
+    postprocessor: '#FFB6C1',
+    output: '#FFD700',
+  };
+  const bgColor = nodeColors[data.type] || '#fff';
+
+  return (
+    React.createElement('div', {
+      style: {
+        padding: '10px',
+        border: `2px ${selected ? 'blue' : '#ddd'} solid`,
+        borderRadius: '8px',
+        background: bgColor,
+        minWidth: '120px',
+        textAlign: 'center',
+        fontWeight: 'bold',
+        cursor: 'pointer',
+      },
+    },
+      data.type !== 'input' && React.createElement(Handle, { type: 'target', position: Position.Top }),
+      React.createElement('div', null, data.label),
+      data.type !== 'output' && React.createElement(Handle, { type: 'source', position: Position.Bottom })
+    )
+  );
+}
+
+function PipelineToolbar({
+  availableNormalizers,
+  availablePostprocessors,
+  onAddNormalizer,
+  onAddPostprocessor,
+}) {
+  return (
+    React.createElement('div', { style: {
+      padding: '10px',
+      background: '#f5f5f5',
+      borderBottom: '1px solid #ddd',
+      display: 'flex',
+      gap: '10px',
+      alignItems: 'center',
+    } },
+      React.createElement('label', { style: { fontWeight: 'bold' } }, 'Add Normalizer:'),
+      React.createElement('select', {
+        onChange: (e) => {
+          if (e.target.value) {
+            onAddNormalizer(e.target.value);
+            e.target.value = '';
+          }
+        }
+      },
+        React.createElement('option', { value: '' }, 'Select a normalizer...'),
+        availableNormalizers && Object.keys(availableNormalizers).map((norm) => React.createElement('option', { key: norm, value: norm }, norm))
+      ),
+
+      React.createElement('label', { style: { fontWeight: 'bold', marginLeft: '20px' } }, 'Add Postprocessor:'),
+      React.createElement('select', {
+        onChange: (e) => {
+          if (e.target.value) {
+            onAddPostprocessor(e.target.value);
+            e.target.value = '';
+          }
+        }
+      },
+        React.createElement('option', { value: '' }, 'Select a postprocessor...'),
+        availablePostprocessors && Object.keys(availablePostprocessors).map((post) => React.createElement('option', { key: post, value: post }, post))
+      )
+    )
+  );
+}
+
+function PipelineExporter({
+  selectedNode,
+  nodes,
+  edges,
+  onExport,
+  onImport,
+  onUpdateParams,
+  onDeleteNode,
+}) {
+  const [paramInputs, setParamInputs] = useState({});
+  const selectedNodeData = nodes.find((n) => n.id === selectedNode);
+
+  const applyParams = () => {
+    if (selectedNode && selectedNodeData) {
+      try {
+        const params = {};
+        Object.keys(paramInputs).forEach((key) => {
+          try {
+            params[key] = JSON.parse(paramInputs[key]);
+          } catch {
+            params[key] = paramInputs[key];
+          }
+        });
+        onUpdateParams(selectedNode, params);
+        setParamInputs({});
+      } catch (error) {
+        alert('Invalid parameter format: ' + error.message);
+      }
+    }
+  };
+
+  const exportToYAML = () => {
+    const workflow = {
+      input_normalizers: [],
+      postprocessors: [],
+    };
+    nodes.forEach((node) => {
+      if (node.type === 'normalizer') {
+        workflow.input_normalizers.push({ name: node.data.name, params: node.data.params || {} });
+      } else if (node.type === 'postprocessor') {
+        workflow.postprocessors.push({ name: node.data.name, params: node.data.params || {} });
+      }
+    });
+    const yamlContent = generateYAML(workflow);
+    downloadFile(yamlContent, 'pipeline.yaml', 'text/yaml');
+  };
+
+  const exportToJSON = () => {
+    const pipelineData = onExport();
+    const jsonContent = JSON.stringify(pipelineData, null, 2);
+    downloadFile(jsonContent, 'pipeline.json', 'application/json');
+  };
+
+  const importFromFile = (event) => {
+    const file = event.target.files[0];
+    if (file) {
+      const reader = new FileReader();
+      reader.onload = (e) => {
+        try {
+          const content = e.target.result;
+          const data = file.name.endsWith('.yaml') ? parseYAML(content) : JSON.parse(content);
+          onImport(data);
+        } catch (error) {
+          alert('Error importing file: ' + error.message);
+        }
+      };
+      reader.readAsText(file);
+    }
+  };
+
+  return (
+    React.createElement('div', { style: {
+      width: '300px',
+      padding: '15px',
+      background: '#fff',
+      borderLeft: '1px solid #ddd',
+      overflowY: 'auto',
+      display: 'flex',
+      flexDirection: 'column',
+    } },
+      React.createElement('h3', null, 'Pipeline Controls'),
+      selectedNodeData && selectedNodeData.type !== 'input' && selectedNodeData.type !== 'output' && (
+        React.createElement('div', { style: { marginBottom: '20px', padding: '10px', background: '#f9f9f9', borderRadius: '5px' } },
+          React.createElement('h4', { style: { marginTop: 0 } }, selectedNodeData.data.label),
+          React.createElement('p', { style: { fontSize: '12px', color: '#666' } }, `Node ID: ${selectedNode}`),
+          React.createElement('label', { style: { fontWeight: 'bold', fontSize: '12px' } }, 'Parameters:'),
+          React.createElement('div', { style: { marginTop: '8px', marginBottom: '10px' } },
+            React.createElement('input', {
+              type: 'text',
+              placeholder: '{"param1": 0.5}',
+              onChange: (e) => setParamInputs({ param_input: e.target.value }),
+              style: { width: '100%', padding: '5px', fontSize: '11px' },
+            })
+          ),
+          React.createElement('button', { onClick: applyParams, style: { width: '100%', padding: '6px', background: '#4CAF50', color: 'white', border: 'none', borderRadius: '4px', cursor: 'pointer', fontSize: '12px', marginBottom: '8px' } }, 'Apply Parameters'),
+          React.createElement('button', { onClick: () => onDeleteNode(selectedNode), style: { width: '100%', padding: '6px', background: '#f44336', color: 'white', border: 'none', borderRadius: '4px', cursor: 'pointer', fontSize: '12px' } }, 'Delete Node')
+        )
+      ),
+      React.createElement('div', { style: { marginTop: 'auto' } },
+        React.createElement('h4', null, 'Export Pipeline'),
+        React.createElement('button', { onClick: exportToYAML, style: { width: '100%', padding: '8px', background: '#2196F3', color: 'white', border: 'none', borderRadius: '4px', cursor: 'pointer', marginBottom: '8px' } }, 'Export as YAML'),
+        React.createElement('button', { onClick: exportToJSON, style: { width: '100%', padding: '8px', background: '#2196F3', color: 'white', border: 'none', borderRadius: '4px', cursor: 'pointer', marginBottom: '8px' } }, 'Export as JSON'),
+        React.createElement('h4', { style: { marginTop: '15px' } }, 'Import Pipeline'),
+        React.createElement('input', { type: 'file', accept: '.yaml,.json', onChange: importFromFile, style: { width: '100%', fontSize: '12px' } })
+      )
+    )
+  );
+}
+
+function PipelineBuilder({ availableNormalizers, availablePostprocessors }) {
+  const [nodes, setNodes, onNodesChange] = useNodesState([
+    { id: 'input', data: { label: 'Input Data', type: 'input' }, position: { x: 0, y: 0 }, type: 'input' },
+    { id: 'output', data: { label: 'Output', type: 'output' }, position: { x: 400, y: 300 }, type: 'output' },
+  ]);
+  const [edges, setEdges, onEdgesChange] = useEdgesState([]);
+  const [selectedNode, setSelectedNode] = useState(null);
+  const reactFlowInstance = useReactFlow();
+
+  const onConnectCb = useCallback((connection) => setEdges((eds) => addEdge(connection, eds)), [setEdges]);
+
+  const addNormalizer = useCallback((normalizerName) => {
+    const id = `norm-${Date.now()}`;
+    const newNode = { id, type: 'normalizer', data: { label: normalizerName, type: 'normalizer', name: normalizerName, params: {} }, position: { x: 100, y: 150 } };
+    setNodes((nds) => [...nds, newNode]);
+  }, [setNodes]);
+
+  const addPostprocessor = useCallback((processorName) => {
+    const id = `post-${Date.now()}`;
+    const newNode = { id, type: 'postprocessor', data: { label: processorName, type: 'postprocessor', name: processorName, params: {} }, position: { x: 250, y: 150 } };
+    setNodes((nds) => [...nds, newNode]);
+  }, [setNodes]);
+
+  const updateNodeParams = useCallback((nodeId, params) => setNodes((nds) => nds.map((node) => node.id === nodeId ? { ...node, data: { ...node.data, params } } : node)), [setNodes]);
+
+  const deleteNode = useCallback((nodeId) => { setNodes((nds) => nds.filter((node) => node.id !== nodeId)); setEdges((eds) => eds.filter((edge) => edge.source !== nodeId && edge.target !== nodeId)); }, [setNodes, setEdges]);
+
+  const exportPipeline = useCallback(() => ({ nodes, edges, timestamp: new Date().toISOString() }), [nodes, edges]);
+  const importPipeline = useCallback((pipelineData) => { if (pipelineData.nodes) setNodes(pipelineData.nodes); if (pipelineData.edges) setEdges(pipelineData.edges); }, [setNodes, setEdges]);
+
+  return (
+    React.createElement('div', { style: { width: '100%', height: '100vh', display: 'flex', flexDirection: 'column' } },
+      React.createElement(PipelineToolbar, { availableNormalizers, availablePostprocessors, onAddNormalizer: addNormalizer, onAddPostprocessor: addPostprocessor }),
+      React.createElement('div', { style: { flex: 1, display: 'flex' } },
+        React.createElement('div', { style: { flex: 1 } },
+          React.createElement(ReactFlow, { nodes, edges, onNodesChange, onEdgesChange, onConnect: onConnectCb, nodeTypes, onNodeClick: (event, node) => setSelectedNode(node.id) },
+            React.createElement(Background, null),
+            React.createElement(Controls, null)
+          )
+        ),
+        React.createElement(PipelineExporter, { selectedNode, nodes, edges, onExport: exportPipeline, onImport: importPipeline, onUpdateParams: updateNodeParams, onDeleteNode: deleteNode })
+      )
+    )
+  );
+}
+
+// Helpers
+function generateYAML(data) {
+  let yaml = '';
+  yaml += 'input_normalizers:\n';
+  data.input_normalizers.forEach((norm) => {
+    yaml += `  - name: ${norm.name}\n`;
+    yaml += '    params:\n';
+    Object.keys(norm.params).forEach((key) => { yaml += `      ${key}: ${JSON.stringify(norm.params[key])}\n`; });
+  });
+  yaml += 'postprocessors:\n';
+  data.postprocessors.forEach((post) => {
+    yaml += `  - name: ${post.name}\n`;
+    yaml += '    params:\n';
+    Object.keys(post.params).forEach((key) => { yaml += `      ${key}: ${JSON.stringify(post.params[key])}\n`; });
+  });
+  return yaml;
+}
+
+function parseYAML(yaml) {
+  const lines = yaml.split('\n');
+  const result = { input_normalizers: [], postprocessors: [] };
+  let currentSection = null;
+  let currentItem = null;
+  lines.forEach((line) => {
+    const trimmed = line.trim();
+    if (trimmed.startsWith('input_normalizers:')) currentSection = 'input_normalizers';
+    else if (trimmed.startsWith('postprocessors:')) currentSection = 'postprocessors';
+    else if (trimmed.startsWith('- name:')) { currentItem = { name: trimmed.replace('- name: ', ''), params: {} }; }
+    else if (trimmed.startsWith('name:') && !trimmed.startsWith('- name:')) { if (currentItem) currentItem.name = trimmed.replace('name: ', ''); }
+    else if (trimmed.startsWith('params:')) {}
+    else if (trimmed && currentItem && !trimmed.startsWith('-')) {
+      const [key, value] = trimmed.split(':').map((s) => s.trim());
+      if (key && value) {
+        try { currentItem.params[key] = JSON.parse(value); } catch { currentItem.params[key] = value; }
+      }
+    }
+    if ((trimmed.startsWith('- name:') || trimmed.startsWith('name:')) && currentItem && Object.keys(currentItem.params).length > 0) { if (currentSection) result[currentSection].push(currentItem); currentItem = null; }
+  });
+  if (currentItem && currentSection) result[currentSection].push(currentItem);
+  return result;
+}
+
+function downloadFile(content, filename, mimeType) {
+  const element = document.createElement('a');
+  element.setAttribute('href', 'data:' + mimeType + ';charset=utf-8,' + encodeURIComponent(content));
+  element.setAttribute('download', filename);
+  element.style.display = 'none';
+  document.body.appendChild(element);
+  element.click();
+  document.body.removeChild(element);
+}
+
+// Mount app
+document.addEventListener('DOMContentLoaded', function () {
+  const APP_CONFIG = window.APP_CONFIG || { availableNormalizers: {}, availablePostprocessors: {} };
+
+  // Ensure ReactFlow UMD global is available
+  const RF = window.ReactFlow;
+  if (!RF) {
+    console.error(
+      'ReactFlow UMD not found on window.ReactFlow. Ensure ReactFlow script is loaded before bundle.'
+    );
+    return;
+  }
+
+  // Extract needed ReactFlow exports at runtime
+  ReactFlowProvider = RF.ReactFlowProvider;
+  ReactFlow = RF.ReactFlow;
+  addEdge = RF.addEdge;
+  useNodesState = RF.useNodesState;
+  useEdgesState = RF.useEdgesState;
+  Controls = RF.Controls;
+  Background = RF.Background;
+  useReactFlow = RF.useReactFlow;
+  Handle = RF.Handle;
+  Position = RF.Position;
+
+  ReactDOM.render(
+    React.createElement(ReactFlowProvider, null, React.createElement(PipelineBuilder, { availableNormalizers: APP_CONFIG.availableNormalizers, availablePostprocessors: APP_CONFIG.availablePostprocessors })),
+    document.getElementById('pipeline-root')
+  );
+});
diff --git a/cellmap_flow/dashboard/static/js/index-pipeline.js b/cellmap_flow/dashboard/static/js/index-pipeline.js
new file mode 100644
index 0000000..c0d3d35
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/index-pipeline.js
@@ -0,0 +1,20 @@
+import React from 'react';
+import ReactDOM from 'react-dom';
+import { ReactFlowProvider } from 'reactflow';
+import { PipelineBuilder } from './pipeline-builder';
+
+// This will be injected by Flask
+const APP_CONFIG = window.APP_CONFIG || {
+  availableNormalizers: {},
+  availablePostprocessors: {},
+};
+
+ReactDOM.render(
+  <ReactFlowProvider>
+    <PipelineBuilder
+      availableNormalizers={APP_CONFIG.availableNormalizers}
+      availablePostprocessors={APP_CONFIG.availablePostprocessors}
+    />
+  </ReactFlowProvider>,
+  document.getElementById('pipeline-root')
+);
diff --git a/cellmap_flow/dashboard/static/js/pipeline-builder.js b/cellmap_flow/dashboard/static/js/pipeline-builder.js
new file mode 100644
index 0000000..5c500af
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/pipeline-builder.js
@@ -0,0 +1,170 @@
+import React, { useCallback, useState, useRef } from 'react';
+import ReactFlow, {
+  Node,
+  Edge,
+  addEdge,
+  useNodesState,
+  useEdgesState,
+  Controls,
+  Background,
+  useReactFlow,
+  NodeTypes,
+} from 'reactflow';
+import 'reactflow/dist/style.css';
+import { PipelineNode } from './pipeline-nodes';
+import { PipelineToolbar } from './pipeline-toolbar';
+import { PipelineExporter } from './pipeline-exporter';
+
+const nodeTypes = {
+  normalizer: PipelineNode,
+  postprocessor: PipelineNode,
+  input: PipelineNode,
+  output: PipelineNode,
+};
+
+export const PipelineBuilder = ({ availableNormalizers, availablePostprocessors }) => {
+  const [nodes, setNodes, onNodesChange] = useNodesState([
+    {
+      id: 'input',
+      data: { label: 'Input Data', type: 'input' },
+      position: { x: 0, y: 0 },
+      type: 'input',
+    },
+    {
+      id: 'output',
+      data: { label: 'Output', type: 'output' },
+      position: { x: 400, y: 300 },
+      type: 'output',
+    },
+  ]);
+
+  const [edges, setEdges, onEdgesChange] = useEdgesState([]);
+  const [selectedNode, setSelectedNode] = useState(null);
+  const reactFlowInstance = useReactFlow();
+
+  const onConnect = useCallback(
+    (connection) => {
+      setEdges((eds) => addEdge(connection, eds));
+    },
+    [setEdges]
+  );
+
+  const addNormalizer = useCallback(
+    (normalizerName) => {
+      const id = `norm-${Date.now()}`;
+      const newNode = {
+        id,
+        type: 'normalizer',
+        data: {
+          label: normalizerName,
+          type: 'normalizer',
+          name: normalizerName,
+          params: {},
+        },
+        position: { x: 100, y: 150 },
+      };
+      setNodes((nds) => [...nds, newNode]);
+    },
+    [setNodes]
+  );
+
+  const addPostprocessor = useCallback(
+    (processorName) => {
+      const id = `post-${Date.now()}`;
+      const newNode = {
+        id,
+        type: 'postprocessor',
+        data: {
+          label: processorName,
+          type: 'postprocessor',
+          name: processorName,
+          params: {},
+        },
+        position: { x: 250, y: 150 },
+      };
+      setNodes((nds) => [...nds, newNode]);
+    },
+    [setNodes]
+  );
+
+  const updateNodeParams = useCallback(
+    (nodeId, params) => {
+      setNodes((nds) =>
+        nds.map((node) =>
+          node.id === nodeId
+            ? { ...node, data: { ...node.data, params } }
+            : node
+        )
+      );
+    },
+    [setNodes]
+  );
+
+  const deleteNode = useCallback(
+    (nodeId) => {
+      setNodes((nds) => nds.filter((node) => node.id !== nodeId));
+      setEdges((eds) =>
+        eds.filter((edge) => edge.source !== nodeId && edge.target !== nodeId)
+      );
+    },
+    [setNodes, setEdges]
+  );
+
+  const exportPipeline = useCallback(() => {
+    return {
+      nodes,
+      edges,
+      timestamp: new Date().toISOString(),
+    };
+  }, [nodes, edges]);
+
+  const importPipeline = useCallback(
+    (pipelineData) => {
+      if (pipelineData.nodes) {
+        setNodes(pipelineData.nodes);
+      }
+      if (pipelineData.edges) {
+        setEdges(pipelineData.edges);
+      }
+    },
+    [setNodes, setEdges]
+  );
+
+  return (
+    <div style={{ width: '100%', height: '100vh', display: 'flex', flexDirection: 'column' }}>
+      <PipelineToolbar
+        availableNormalizers={availableNormalizers}
+        availablePostprocessors={availablePostprocessors}
+        onAddNormalizer={addNormalizer}
+        onAddPostprocessor={addPostprocessor}
+      />
+
+      <div style={{ flex: 1, display: 'flex' }}>
+        <div style={{ flex: 1 }}>
+          <ReactFlow
+            nodes={nodes}
+            edges={edges}
+            onNodesChange={onNodesChange}
+            onEdgesChange={onEdgesChange}
+            onConnect={onConnect}
+            nodeTypes={nodeTypes}
+            onNodeClick={(event, node) => setSelectedNode(node.id)}
+          >
+            <Background />
+            <Controls />
+          </ReactFlow>
+        </div>
+
+        <PipelineExporter
+          selectedNode={selectedNode}
+          nodes={nodes}
+          edges={edges}
+          onExport={exportPipeline}
+          onImport={importPipeline}
+          onUpdateParams={updateNodeParams}
+          onDeleteNode={deleteNode}
+        />
+      </div>
+    </div>
+  );
+};
diff --git a/cellmap_flow/dashboard/static/js/pipeline-exporter.js b/cellmap_flow/dashboard/static/js/pipeline-exporter.js
new file mode 100644
index 0000000..5997103
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/pipeline-exporter.js
@@ -0,0 +1,337 @@
+import React, { useState } from 'react';
+
+export const PipelineExporter = ({
+  selectedNode,
+  nodes,
+  edges,
+  onExport,
+  onImport,
+  onUpdateParams,
+  onDeleteNode,
+}) => {
+  const [paramInputs, setParamInputs] = useState({});
+
+  const selectedNodeData = nodes.find((n) => n.id === selectedNode);
+
+  const handleParamChange = (key, value) => {
+    setParamInputs({ ...paramInputs, [key]: value });
+  };
+
+  const saveNodeData = () => {
+    if (selectedNodeData && (selectedNodeData.type === 'input' || selectedNodeData.type === 'output')) {
+      alert(`${selectedNodeData.type.charAt(0).toUpperCase() + selectedNodeData.type.slice(1)} node saved.`);
+    }
+  };
+
+  const applyParams = () => {
+    if (selectedNode && selectedNodeData) {
+      try {
+        const params = {};
+        Object.keys(paramInputs).forEach((key) => {
+          try {
+            params[key] = JSON.parse(paramInputs[key]);
+          } catch {
+            params[key] = paramInputs[key];
+          }
+        });
+        onUpdateParams(selectedNode, params);
+        setParamInputs({});
+      } catch (error) {
+        alert('Invalid parameter format: ' + error.message);
+      }
+    }
+  };
+
+  const exportToYAML = async () => {
+    try {
+      // Fetch dataset_path from backend
+      const response = await fetch('/api/dataset_path');
+      const data = await response.json();
+      const datasetPath = data.dataset_path || '';
+      
+      const pipelineData = onExport();
+      
+      // Extract workflow structure
+      const workflow = {
+        input_normalizers: [],
+        postprocessors: [],
+      };
+
+      // Order nodes by edges
+      nodes.forEach((node) => {
+        if (node.type === 'normalizer') {
+          workflow.input_normalizers.push({
+            name: node.data.name,
+            params: node.data.params || {},
+          });
+        } else if (node.type === 'postprocessor') {
+          workflow.postprocessors.push({
+            name: node.data.name,
+            params: node.data.params || {},
+          });
+        }
+      });
+
+      const yamlContent = generateYAML(workflow, datasetPath);
+      downloadFile(yamlContent, 'pipeline.yaml', 'text/yaml');
+    } catch (error) {
+      console.error('Error exporting YAML:', error);
+      alert('Error exporting YAML: ' + error.message);
+    }
+  };
+
+  const exportToJSON = () => {
+    const pipelineData = onExport();
+    const jsonContent = JSON.stringify(pipelineData, null, 2);
+    downloadFile(jsonContent, 'pipeline.json', 'application/json');
+  };
+
+  const importFromFile = (event) => {
+    const file = event.target.files[0];
+    if (file) {
+      const reader = new FileReader();
+      reader.onload = (e) => {
+        try {
+          const content = e.target.result;
+          const data = file.name.endsWith('.yaml')
+            ? parseYAML(content)
+            : JSON.parse(content);
+          onImport(data);
+        } catch (error) {
+          alert('Error importing file: ' + error.message);
+        }
+      };
+      reader.readAsText(file);
+    }
+  };
+
+  return (
+    <div style={{
+      width: '300px',
+      padding: '15px',
+      background: '#fff',
+      borderLeft: '1px solid #ddd',
+      overflowY: 'auto',
+      display: 'flex',
+      flexDirection: 'column',
+    }}>
+      <h3>Pipeline Controls</h3>
+
+      {/* Node Inspector */}
+      {selectedNodeData && (
+        <div style={{ marginBottom: '20px', padding: '10px', background: '#f9f9f9', borderRadius: '5px' }}>
+          <h4 style={{ marginTop: 0 }}>{selectedNodeData.data.label}</h4>
+          <p style={{ fontSize: '12px', color: '#666' }}>Node ID: {selectedNode}</p>
+          <p style={{ fontSize: '11px', color: '#999' }}>Type: {selectedNodeData.type}</p>
+
+          {(selectedNodeData.type === 'input' || selectedNodeData.type === 'output') ? (
+            <button
+              onClick={saveNodeData}
+              style={{
+                width: '100%',
+                padding: '6px',
+                background: '#4CAF50',
+                color: 'white',
+                border: 'none',
+                borderRadius: '4px',
+                cursor: 'pointer',
+                fontSize: '12px',
+              }}
+            >
+              Save {selectedNodeData.type.charAt(0).toUpperCase() + selectedNodeData.type.slice(1)}
+            </button>
+          ) : (
+            <>
+              <label style={{ fontWeight: 'bold', fontSize: '12px' }}>Parameters:</label>
+              <div style={{ marginTop: '8px', marginBottom: '10px' }}>
+                <input
+                  type="text"
+                  placeholder='JSON params e.g. {"param1": 0.5}'
+                  onChange={(e) => setParamInputs({ param_input: e.target.value })}
+                  style={{ width: '100%', padding: '5px', fontSize: '11px' }}
+                />
+              </div>
+
+              <button
+                onClick={applyParams}
+                style={{
+                  width: '100%',
+                  padding: '6px',
+                  background: '#4CAF50',
+                  color: 'white',
+                  border: 'none',
+                  borderRadius: '4px',
+                  cursor: 'pointer',
+                  fontSize: '12px',
+                  marginBottom: '8px',
+                }}
+              >
+                Apply Parameters
+              </button>
+
+              <button
+                onClick={() => onDeleteNode(selectedNode)}
+                style={{
+                  width: '100%',
+                  padding: '6px',
+                  background: '#f44336',
+                  color: 'white',
+                  border: 'none',
+                  borderRadius: '4px',
+                  cursor: 'pointer',
+                  fontSize: '12px',
+                }}
+              >
+                Delete Node
+              </button>
+            </>
+          )}
+        </div>
+      )}
+
+      {/* Export/Import */}
+      <div style={{ marginTop: 'auto' }}>
+        <h4>Export Pipeline</h4>
+        <button
+          onClick={exportToYAML}
+          style={{
+            width: '100%',
+            padding: '8px',
+            background: '#2196F3',
+            color: 'white',
+            border: 'none',
+            borderRadius: '4px',
+            cursor: 'pointer',
+            marginBottom: '8px',
+          }}
+        >
+          Export as YAML
+        </button>
+        <button
+          onClick={exportToJSON}
+          style={{
+            width: '100%',
+            padding: '8px',
+            background: '#2196F3',
+            color: 'white',
+            border: 'none',
+            borderRadius: '4px',
+            cursor: 'pointer',
+            marginBottom: '8px',
+          }}
+        >
+          Export as JSON
+        </button>
+
+        <h4 style={{ marginTop: '15px' }}>Import Pipeline</h4>
+        <input
+          type="file"
+          accept=".yaml,.json"
+          onChange={importFromFile}
+          style={{ width: '100%', fontSize: '12px' }}
+        />
+      </div>
+    </div>
+  );
+};
+
+// Helper functions
+const generateYAML = (data, datasetPath = '') => {
+  let yaml = '';
+  
+  if (datasetPath) {
+    yaml += `dataset_path: ${datasetPath}\n`;
+    yaml += '\n';
+  }
+
+  yaml += 'input_normalizers:\n';
+  if (data.input_normalizers.length === 0) {
+    yaml += '  []\n';
+  } else {
+    data.input_normalizers.forEach((norm) => {
+      yaml += `  - name: ${norm.name}\n`;
+      yaml += '    params:\n';
+      if (Object.keys(norm.params).length === 0) {
+        yaml += '      {}\n';
+      } else {
+        Object.keys(norm.params).forEach((key) => {
+          yaml += `      ${key}: ${JSON.stringify(norm.params[key])}\n`;
+        });
+      }
+    });
+  }
+
+  yaml += 'postprocessors:\n';
+  if (data.postprocessors.length === 0) {
+    yaml += '  []\n';
+  } else {
+    data.postprocessors.forEach((post) => {
+      yaml += `  - name: ${post.name}\n`;
+      yaml += '    params:\n';
+      if (Object.keys(post.params).length === 0) {
+        yaml += '      {}\n';
+      } else {
+        Object.keys(post.params).forEach((key) => {
+          yaml += `      ${key}: ${JSON.stringify(post.params[key])}\n`;
+        });
+      }
+    });
+  }
+
+  return yaml;
+};
+
+const parseYAML = (yaml) => {
+  // Simple YAML parser for our use case
+  const lines = yaml.split('\n');
+  const result = {
+    input_normalizers: [],
+    postprocessors: [],
+  };
+
+  let currentSection = null;
+  let currentItem = null;
+
+  lines.forEach((line) => {
+    const trimmed = line.trim();
+    if (trimmed.startsWith('input_normalizers:')) currentSection = 'input_normalizers';
+    else if (trimmed.startsWith('postprocessors:')) currentSection = 'postprocessors';
+    else if (trimmed.startsWith('- name:')) {
+      currentItem = { name: trimmed.replace('- name: ', ''), params: {} };
+    } else if (trimmed.startsWith('name:') && !trimmed.startsWith('- name:')) {
+      if (currentItem) currentItem.name = trimmed.replace('name: ', '');
+    } else if (trimmed.startsWith('params:')) {
+      // params section
+    } else if (trimmed && currentItem && !trimmed.startsWith('-')) {
+      const [key, value] = trimmed.split(':').map((s) => s.trim());
+      if (key && value) {
+        try {
+          currentItem.params[key] = JSON.parse(value);
+        } catch {
+          currentItem.params[key] = value;
+        }
+      }
+    }
+
+    // Save item when moving to next
+    if ((trimmed.startsWith('- name:') || trimmed.startsWith('name:')) && currentItem && Object.keys(currentItem.params).length > 0) {
+      if (currentSection) result[currentSection].push(currentItem);
+      currentItem = null;
+    }
+  });
+
+  // Push last item
+  if (currentItem && currentSection) result[currentSection].push(currentItem);
+
+  return result;
+};
+
+const downloadFile = (content, filename, mimeType) => {
+  const element = document.createElement('a');
+  element.setAttribute('href', 'data:' + mimeType + ';charset=utf-8,' + encodeURIComponent(content));
+  element.setAttribute('download', filename);
+  element.style.display = 'none';
+  document.body.appendChild(element);
+  element.click();
+  document.body.removeChild(element);
+};
diff --git a/cellmap_flow/dashboard/static/js/pipeline-nodes.js b/cellmap_flow/dashboard/static/js/pipeline-nodes.js
new file mode 100644
index 0000000..2a649cc
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/pipeline-nodes.js
@@ -0,0 +1,36 @@
+import React from 'react';
+import { Handle, Position } from 'reactflow';
+
+const nodeColors = {
+  input: '#90EE90',
+  normalizer: '#87CEEB',
+  postprocessor: '#FFB6C1',
+  output: '#FFD700',
+};
+
+export const PipelineNode = ({ data, selected, id }) => {
+  const bgColor = nodeColors[data.type] || '#fff';
+
+  return (
+    <div
+      style={{
+        padding: '10px',
+        border: `2px ${selected ? 'blue' : '#ddd'} solid`,
+        borderRadius: '8px',
+        background: bgColor,
+        minWidth: '120px',
+        textAlign: 'center',
+        fontWeight: 'bold',
+        cursor: 'pointer',
+      }}
+    >
+      {data.type !== 'input' && (
+        <Handle type="target" position={Position.Top} />
+      )}
+      <div>{data.label}</div>
+      {data.type !== 'output' && (
+        <Handle type="source" position={Position.Bottom} />
+      )}
+    </div>
+  );
+};
diff --git a/cellmap_flow/dashboard/static/js/pipeline-toolbar.js b/cellmap_flow/dashboard/static/js/pipeline-toolbar.js
new file mode 100644
index 0000000..fbf6e3e
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/pipeline-toolbar.js
@@ -0,0 +1,53 @@
+import React from 'react';
+
+export const PipelineToolbar = ({
+  availableNormalizers,
+  availablePostprocessors,
+  onAddNormalizer,
+  onAddPostprocessor,
+}) => {
+  return (
+    <div style={{
+      padding: '10px',
+      background: '#f5f5f5',
+      borderBottom: '1px solid #ddd',
+      display: 'flex',
+      gap: '10px',
+      alignItems: 'center',
+    }}>
+      <label style={{ fontWeight: 'bold' }}>Add Normalizer:</label>
+      <select
+        onChange={(e) => {
+          if (e.target.value) {
+            onAddNormalizer(e.target.value);
+            e.target.value = '';
+          }
+        }}
+      >
+        <option value="">Select a normalizer...</option>
+        {availableNormalizers && Object.keys(availableNormalizers).map((norm) => (
+          <option key={norm} value={norm}>
+            {norm}
+          </option>
+        ))}
+      </select>
+
+      <label style={{ fontWeight: 'bold', marginLeft: '20px' }}>Add Postprocessor:</label>
+      <select
+        onChange={(e) => {
+          if (e.target.value) {
+            onAddPostprocessor(e.target.value);
+            e.target.value = '';
+          }
+        }}
+      >
+        <option value="">Select a postprocessor...</option>
+        {availablePostprocessors && Object.keys(availablePostprocessors).map((post) => (
+          <option key={post} value={post}>
+            {post}
+          </option>
+        ))}
+      </select>
+    </div>
+  );
+};
diff --git a/cellmap_flow/dashboard/static/js/react-dom.production.min.js b/cellmap_flow/dashboard/static/js/react-dom.production.min.js
new file mode 100644
index 0000000..fb4e099
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/react-dom.production.min.js
@@ -0,0 +1,267 @@
+/**
+ * @license React
+ * react-dom.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+(function(){/*
+ Modernizr 3.0.0pre (Custom Build) | MIT
+*/
+'use strict';(function(Q,zb){"object"===typeof exports&&"undefined"!==typeof module?zb(exports,require("react")):"function"===typeof define&&define.amd?define(["exports","react"],zb):(Q=Q||self,zb(Q.ReactDOM={},Q.React))})(this,function(Q,zb){function m(a){for(var b="https://reactjs.org/docs/error-decoder.html?invariant="+a,c=1;c<arguments.length;c++)b+="&args[]="+encodeURIComponent(arguments[c]);return"Minified React error #"+a+"; visit "+b+" for the full message or use the non-minified dev environment for full errors and additional helpful warnings."}
+function mb(a,b){Ab(a,b);Ab(a+"Capture",b)}function Ab(a,b){$b[a]=b;for(a=0;a<b.length;a++)cg.add(b[a])}function bj(a){if(Zd.call(dg,a))return!0;if(Zd.call(eg,a))return!1;if(cj.test(a))return dg[a]=!0;eg[a]=!0;return!1}function dj(a,b,c,d){if(null!==c&&0===c.type)return!1;switch(typeof b){case "function":case "symbol":return!0;case "boolean":if(d)return!1;if(null!==c)return!c.acceptsBooleans;a=a.toLowerCase().slice(0,5);return"data-"!==a&&"aria-"!==a;default:return!1}}function ej(a,b,c,d){if(null===
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+case ge:return"Suspense";case he:return"SuspenseList"}if("object"===typeof a)switch(a.$$typeof){case gg:return(a.displayName||"Context")+".Consumer";case hg:return(a._context.displayName||"Context")+".Provider";case ie:var b=a.render;a=a.displayName;a||(a=b.displayName||b.name||"",a=""!==a?"ForwardRef("+a+")":"ForwardRef");return a;case je:return b=a.displayName||null,null!==b?b:de(a.type)||"Memo";case Ta:b=a._payload;a=a._init;try{return de(a(b))}catch(c){}}return null}function gj(a){var b=a.type;
+switch(a.tag){case 24:return"Cache";case 9:return(b.displayName||"Context")+".Consumer";case 10:return(b._context.displayName||"Context")+".Provider";case 18:return"DehydratedFragment";case 11:return a=b.render,a=a.displayName||a.name||"",b.displayName||(""!==a?"ForwardRef("+a+")":"ForwardRef");case 7:return"Fragment";case 5:return b;case 4:return"Portal";case 3:return"Root";case 6:return"Text";case 16:return de(b);case 8:return b===fe?"StrictMode":"Mode";case 22:return"Offscreen";case 12:return"Profiler";
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diff --git a/cellmap_flow/dashboard/static/js/react-dom.production.min.js.1 b/cellmap_flow/dashboard/static/js/react-dom.production.min.js.1
new file mode 100644
index 0000000..fb4e099
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/react-dom.production.min.js.1
@@ -0,0 +1,267 @@
+/**
+ * @license React
+ * react-dom.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+(function(){/*
+ Modernizr 3.0.0pre (Custom Build) | MIT
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diff --git a/cellmap_flow/dashboard/static/js/react.production.min.js b/cellmap_flow/dashboard/static/js/react.production.min.js
new file mode 100644
index 0000000..9f467ca
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/react.production.min.js
@@ -0,0 +1,31 @@
+/**
+ * @license React
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
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diff --git a/cellmap_flow/dashboard/static/js/react.production.min.js.1 b/cellmap_flow/dashboard/static/js/react.production.min.js.1
new file mode 100644
index 0000000..9f467ca
--- /dev/null
+++ b/cellmap_flow/dashboard/static/js/react.production.min.js.1
@@ -0,0 +1,31 @@
+/**
+ * @license React
+ * react.production.min.js
+ *
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
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+ia=0<a?Math.floor(1E3/a):5},unstable_Profiling:null}};c.Children={map:C,forEach:function(a,b,c){C(a,function(){b.apply(this,arguments)},c)},count:function(a){var b=0;C(a,function(){b++});return b},toArray:function(a){return C(a,function(a){return a})||[]},only:function(a){if(!M(a))throw Error("React.Children.only expected to receive a single React element child.");return a}};c.Component=w;c.Fragment=sa;c.Profiler=ua;c.PureComponent=K;c.StrictMode=ta;c.Suspense=ya;c.__SECRET_INTERNALS_DO_NOT_USE_OR_YOU_WILL_BE_FIRED=
+t;c.act=ka;c.cloneElement=function(a,b,c){if(null===a||void 0===a)throw Error("React.cloneElement(...): The argument must be a React element, but you passed "+a+".");var e=la({},a.props),d=a.key,k=a.ref,h=a._owner;if(null!=b){void 0!==b.ref&&(k=b.ref,h=L.current);void 0!==b.key&&(d=""+b.key);if(a.type&&a.type.defaultProps)var l=a.type.defaultProps;for(f in b)aa.call(b,f)&&!ba.hasOwnProperty(f)&&(e[f]=void 0===b[f]&&void 0!==l?l[f]:b[f])}var f=arguments.length-2;if(1===f)e.children=c;else if(1<f){l=
+Array(f);for(var g=0;g<f;g++)l[g]=arguments[g+2];e.children=l}return{$$typeof:y,type:a.type,key:d,ref:k,props:e,_owner:h}};c.createContext=function(a){a={$$typeof:wa,_currentValue:a,_currentValue2:a,_threadCount:0,Provider:null,Consumer:null,_defaultValue:null,_globalName:null};a.Provider={$$typeof:va,_context:a};return a.Consumer=a};c.createElement=Z;c.createFactory=function(a){var b=Z.bind(null,a);b.type=a;return b};c.createRef=function(){return{current:null}};c.forwardRef=function(a){return{$$typeof:xa,
+render:a}};c.isValidElement=M;c.lazy=function(a){return{$$typeof:Aa,_payload:{_status:-1,_result:a},_init:ra}};c.memo=function(a,b){return{$$typeof:za,type:a,compare:void 0===b?null:b}};c.startTransition=function(a,b){b=J.transition;J.transition={};try{a()}finally{J.transition=b}};c.unstable_act=ka;c.useCallback=function(a,b){return g.current.useCallback(a,b)};c.useContext=function(a){return g.current.useContext(a)};c.useDebugValue=function(a,b){};c.useDeferredValue=function(a){return g.current.useDeferredValue(a)};
+c.useEffect=function(a,b){return g.current.useEffect(a,b)};c.useId=function(){return g.current.useId()};c.useImperativeHandle=function(a,b,c){return g.current.useImperativeHandle(a,b,c)};c.useInsertionEffect=function(a,b){return g.current.useInsertionEffect(a,b)};c.useLayoutEffect=function(a,b){return g.current.useLayoutEffect(a,b)};c.useMemo=function(a,b){return g.current.useMemo(a,b)};c.useReducer=function(a,b,c){return g.current.useReducer(a,b,c)};c.useRef=function(a){return g.current.useRef(a)};
+c.useState=function(a){return g.current.useState(a)};c.useSyncExternalStore=function(a,b,c){return g.current.useSyncExternalStore(a,b,c)};c.useTransition=function(){return g.current.useTransition()};c.version="18.3.1"});
+})();
diff --git a/cellmap_flow/dashboard/templates/index.html b/cellmap_flow/dashboard/templates/index.html
index e147c9b..3f98d4d 100644
--- a/cellmap_flow/dashboard/templates/index.html
+++ b/cellmap_flow/dashboard/templates/index.html
@@ -18,6 +18,7 @@
       <button class="btn btn-outline-light" type="button" onclick="toggleDashboard()">
         Toggle Dashboard
       </button>
+      <a class="btn btn-outline-light ms-2" href="/pipeline-builder" role="button">Pipeline Builder</a>
     </div>
   </nav>
 
diff --git a/cellmap_flow/dashboard/templates/pipeline_builder_v2.html b/cellmap_flow/dashboard/templates/pipeline_builder_v2.html
new file mode 100644
index 0000000..00d7918
--- /dev/null
+++ b/cellmap_flow/dashboard/templates/pipeline_builder_v2.html
@@ -0,0 +1,2088 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>CellMapFlow - Pipeline Builder</title>
+    <link rel="icon" href="{{ url_for('static', filename='img/favicon.ico') }}" type="image/x-icon" />
+    <link rel="stylesheet" href="{{ url_for('static', filename='css/pipeline_builder.css') }}" />
+</head>
+<body>
+    <div class="app-container">
+        <div class="header">
+            <h1>CellMapFlow Pipeline</h1>
+            <div class="header-actions">
+                <button class="action-btn" onclick="exportYAML()">📥 Export YAML</button>
+                <!-- <button class="action-btn" onclick="exportJSON()">📥 Export JSON</button> -->
+                <label class="import-btn">
+                    📂 Import YAML
+                    <input type="file" class="file-input" accept=".yaml,.json" id="import-file" onchange="importFile()">
+                </label>
+                <button class="apply-btn" onclick="applyPipeline()">✓ Apply to Flow</button>
+                <button class="action-btn" onclick="toggleBlockwiseBar()" title="Show/Hide blockwise configuration section" style="background-color: rgba(255, 111, 0, 0.799);">Blockwise</button>
+            </div>
+        </div>
+
+        <!-- Blockwise Configuration Section -->
+        <div id="blockwise-bar" class="blockwise-bar" style="background: var(--bg-secondary); border-bottom: 1px solid var(--border-color); padding: 12px 24px; display: none; gap: 12px; align-items: center;">
+            <span style="font-weight: 600; color: var(--text-secondary);"> Blockwise:</span>
+            <button id="btn-validate" class="action-btn" onclick="validateBlockwise()" title="Step 1: Validate if pipeline is ready for blockwise processing">✓ 1- Validate</button>
+            <button id="btn-generate" class="action-btn" onclick="generateBlockwiseTask()" title="Step 2: Generate blockwise task YAML files" disabled>📋 2- Generate Task</button>
+            <button id="btn-precheck" class="action-btn" onclick="precheckBlockwiseTask()" title="Step 3: Precheck blockwise task configuration" disabled>🔍 3- Precheck</button>
+            <button id="btn-submit" class="action-btn" onclick="submitBlockwiseTask()" title="Step 4: Submit blockwise task to LSF" disabled>🚀 4- Submit Task</button>
+            
+            <!-- Model Merge Mode Selector -->
+            <div style="margin-left: auto; display: flex; gap: 8px; align-items: center;">
+                <label for="model-merge-mode" style="color: var(--text-secondary); font-size: 12px; white-space: nowrap;">Model Merge:</label>
+                <select id="model-merge-mode" style="background: var(--bg-tertiary); color: var(--text-primary); border: 1px solid var(--border-color); padding: 6px 8px; border-radius: 4px; font-size: 12px; cursor: pointer;">
+                    <option value="">None (Single Model)</option>
+                </select>
+            </div>
+            
+            <div id="blockwise-status" style="font-size: 12px; color: var(--text-secondary); white-space: nowrap;"></div>
+
+        </div>
+
+        <div class="main">
+            <div class="sidebar">
+                <div class="library-section">
+                    <div class="section-title">Flow Control</div>
+                    <button class="section-toggle active" onclick="toggleSection(this, 'io')">
+                        <span>🔌 Input/Output</span>
+                        <span id="io-toggle">▲</span>
+                    </button>
+                    <div class="library-items show" id="io-items">
+                        <div class="library-item" draggable="true" data-type="input" data-name="INPUT">🟣 INPUT</div>
+                        <div class="library-item" draggable="true" data-type="output" data-name="OUTPUT">🟢 OUTPUT</div>
+                    </div>
+                </div>
+
+                <div class="library-section">
+                    <div class="section-title">Input Normalizers</div>
+                    <button class="section-toggle" onclick="toggleSection(this, 'normalizers')">
+                        <span>📥 Normalizers</span>
+                        <span id="normalizers-toggle">▼</span>
+                    </button>
+                    <div class="library-items" id="normalizers-items"></div>
+                </div>
+
+                <div class="library-section">
+                    <div class="section-title">Models</div>
+                    <button class="section-toggle" onclick="toggleSection(this, 'models')">
+                        <span>🤖 Models</span>
+                        <span id="models-toggle">▼</span>
+                    </button>
+                    <div class="library-items" id="models-items"></div>
+                </div>
+
+                <div class="library-section">
+                    <div class="section-title">Postprocessing</div>
+                    <button class="section-toggle" onclick="toggleSection(this, 'postprocessors')">
+                        <span>📤 Postprocessors</span>
+                        <span id="postprocessors-toggle">▼</span>
+                    </button>
+                    <div class="library-items" id="postprocessors-items"></div>
+                </div>
+
+                <div class="library-section">
+                    <div class="section-title">Configuration</div>
+                    <button class="section-toggle" onclick="toggleSection(this, 'config')">
+                        <span>⚙️ Blockwise Config</span>
+                        <span id="config-toggle">▼</span>
+                    </button>
+                    <div class="library-items" id="config-items">
+                        <div class="library-item" draggable="true" data-type="blockwise-config" data-name="Blockwise Configuration">⚙️ Blockwise Configuration</div>
+                    </div>
+                </div>
+            </div>
+
+            <div class="canvas">
+                <div class="canvas-content" id="canvas-content">
+                    <div class="empty-state">
+                        <div class="empty-state-icon">🎨</div>
+                        <div class="empty-state-text">Drag items from the sidebar to start building your pipeline</div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Output Channels Configuration Modal -->
+    <div id="output-channels-modal" class="modal" style="display: none;">
+        <div class="modal-content" style="width: 500px; max-height: 600px; overflow-y: auto;">
+            <div class="modal-header">
+                <h2>Configure Output Channels</h2>
+                <button class="modal-close-btn" onclick="closeOutputChannelsModal()">✕</button>
+            </div>
+            <div id="output-channels-body" class="modal-body">
+                <!-- Populated by JavaScript -->
+            </div>
+            <div class="modal-footer">
+                <button class="action-btn" onclick="closeOutputChannelsModal()">Cancel</button>
+                <button class="action-btn" style="background: var(--accent-green); color: white;" 
+                        onclick="saveOutputChannels()">✓ Save Channels</button>
+            </div>
+        </div>
+    </div>
+
+    <!-- Modal Overlay -->
+    <div id="modal-overlay" class="modal-overlay" style="display: none;" 
+         onclick="if(event.target === this) closeOutputChannelsModal()"></div>
+
+    <script>
+        // Config from Flask
+        window.APP_CONFIG = {
+            availableNormalizers: {{ input_normalizers | tojson }},
+            availableModels: {{ available_models | tojson }},
+            availablePostprocessors: {{ output_postprocessors | tojson }},
+            availableModelMergers: {{ model_mergers | tojson }},
+            datasetPath: {{ dataset_path | tojson }},
+        };
+
+        // Pipeline state - load from backend (persisted state from globals)
+        let pipeline = {
+            inputs: {{ current_inputs | tojson }},
+            outputs: {{ current_outputs | tojson }},
+            normalizers: {{ current_normalizers | tojson }},
+            models: {{ current_models | tojson }},
+            postprocessors: {{ current_postprocessors | tojson }},
+            blockwise_config: [],
+            edges: {{ current_edges | tojson }}
+        };
+
+        // Ensure all nodes have IDs and positions
+        pipeline.inputs = (pipeline.inputs || []).map((n, i) => ({
+            id: n.id || `input-${i}-${Date.now()}`,
+            name: 'INPUT',
+            params: n.params || { dataset_path: APP_CONFIG.datasetPath || '' },
+            position: n.position || { x: 20, y: 20 + (i * 180) }
+        }));
+        // Ensure first INPUT node has dataset_path if it's empty
+        if (pipeline.inputs.length === 0) {
+            pipeline.inputs.push({
+                id: `input-0-${Date.now()}`,
+                name: 'INPUT',
+                params: { dataset_path: APP_CONFIG.datasetPath || '' },
+                position: { x: 20, y: 20 }
+            });
+        } else if (!pipeline.inputs[0].params?.dataset_path) {
+            pipeline.inputs[0].params = pipeline.inputs[0].params || {};
+            pipeline.inputs[0].params.dataset_path = APP_CONFIG.datasetPath || '';
+        }
+        pipeline.outputs = (pipeline.outputs || []).map((n, i) => ({
+            id: n.id || `output-${i}-${Date.now()}`,
+            name: 'OUTPUT',
+            params: {},
+            position: n.position || { x: 900, y: 20 + (i * 180) }
+        }));
+        pipeline.normalizers = (pipeline.normalizers || []).map((n, i) => ({
+            id: n.id || `norm-${i}-${Date.now()}`,
+            name: n.name,
+            params: n.params || {},
+            position: n.position || { x: 200 + (i * 380), y: 20 }
+        }));
+        pipeline.models = (pipeline.models || []).map((m, i) => {
+            const model = {
+                id: m.id || `model-${i}-${Date.now()}`,
+                name: m.name,
+                params: m.params || {},
+                position: m.position || { x: 400 + (i * 380), y: 20 }
+            };
+            // Preserve config if it exists (from backend ModelConfig.to_dict())
+            if (m.config && typeof m.config === 'object') {
+                model.config = m.config;
+                // Normalize channels field to always be an array
+                if (model.config.channels) {
+                    if (typeof model.config.channels === 'string') {
+                        try {
+                            model.config.channels = JSON.parse(model.config.channels);
+                            if (!Array.isArray(model.config.channels)) {
+                                model.config.channels = [model.config.channels];
+                            }
+                        } catch {
+                            model.config.channels = [model.config.channels];
+                        }
+                    }
+                }
+                // Also populate params from config for UI display (exclude 'name')
+                if (!m.params || Object.keys(m.params).length === 0) {
+                    model.params = {};
+                    Object.entries(m.config).forEach(([key, value]) => {
+                        if (key !== 'name') {
+                            model.params[key] = value;
+                        }
+                    });
+                }
+            }
+            return model;
+        });
+        pipeline.postprocessors = (pipeline.postprocessors || []).map((p, i) => ({
+            id: p.id || `post-${i}-${Date.now()}`,
+            name: p.name,
+            params: p.params || {},
+            position: p.position || { x: 600 + (i * 380), y: 20 }
+        }));
+        pipeline.edges = pipeline.edges || [];
+
+        // Auto-connect nodes based on pipeline structure
+        function autoConnectNodes() {
+            // Clear existing edges
+            pipeline.edges = [];
+
+            // Helper to create edge
+            function addEdge(fromId, toId) {
+                if (fromId && toId) {
+                    const exists = pipeline.edges.some(e => e.from === fromId && e.to === toId);
+                    if (!exists) {
+                        pipeline.edges.push({
+                            id: `edge-${Date.now()}-${Math.random()}`,
+                            from: fromId,
+                            to: toId
+                        });
+                    }
+                }
+            }
+
+            // 1. INPUT → first normalizer (if any)
+            if (pipeline.inputs.length > 0 && pipeline.normalizers.length > 0) {
+                addEdge(pipeline.inputs[0].id, pipeline.normalizers[0].id);
+            }
+
+            // 2. Chain normalizers: norm1 → norm2 → norm3 → ...
+            for (let i = 0; i < pipeline.normalizers.length - 1; i++) {
+                addEdge(pipeline.normalizers[i].id, pipeline.normalizers[i + 1].id);
+            }
+
+            // 3. Last normalizer → ALL models (or INPUT → models if no normalizers)
+            const sourceForModels = pipeline.normalizers.length > 0 
+                ? pipeline.normalizers[pipeline.normalizers.length - 1].id 
+                : (pipeline.inputs.length > 0 ? pipeline.inputs[0].id : null);
+            
+            if (sourceForModels) {
+                pipeline.models.forEach(model => {
+                    addEdge(sourceForModels, model.id);
+                });
+            }
+
+            // 4. Chain postprocessors: each model → first postprocessor
+            if (pipeline.postprocessors.length > 0) {
+                pipeline.models.forEach(model => {
+                    addEdge(model.id, pipeline.postprocessors[0].id);
+                });
+            }
+
+            // 5. Chain postprocessors: post1 → post2 → post3 → ...
+            for (let i = 0; i < pipeline.postprocessors.length - 1; i++) {
+                addEdge(pipeline.postprocessors[i].id, pipeline.postprocessors[i + 1].id);
+            }
+
+            // 6. Last postprocessor → OUTPUT (if any)
+            if (pipeline.postprocessors.length > 0 && pipeline.outputs.length > 0) {
+                addEdge(pipeline.postprocessors[pipeline.postprocessors.length - 1].id, pipeline.outputs[0].id);
+            }
+        }
+
+        // Run auto-connect if we have nodes
+        if (pipeline.inputs.length > 0 || pipeline.normalizers.length > 0 || 
+            pipeline.models.length > 0 || pipeline.postprocessors.length > 0) {
+            autoConnectNodes();
+        }
+
+        // Initialize library
+        function initializeLibrary() {
+            // Setup IO items drag events
+            const ioItems = document.querySelectorAll('#io-items .library-item');
+            ioItems.forEach(item => {
+                item.addEventListener('dragstart', handleDragStart);
+            });
+
+            const normsArray = Array.isArray(APP_CONFIG.availableNormalizers) ?
+                APP_CONFIG.availableNormalizers : Object.values(APP_CONFIG.availableNormalizers);
+            const modelsArray = Array.isArray(APP_CONFIG.availableModels) ?
+                APP_CONFIG.availableModels : Object.values(APP_CONFIG.availableModels);
+            const postsArray = Array.isArray(APP_CONFIG.availablePostprocessors) ?
+                APP_CONFIG.availablePostprocessors : Object.values(APP_CONFIG.availablePostprocessors);
+
+            const normContainer = document.getElementById('normalizers-items');
+            normsArray.forEach(item => {
+                const name = typeof item === 'string' ? item : item.name;
+                const elem = document.createElement('div');
+                elem.className = 'library-item';
+                elem.textContent = name;
+                elem.draggable = true;
+                elem.dataset.type = 'normalizer';
+                elem.dataset.name = name;
+                elem.addEventListener('dragstart', handleDragStart);
+                normContainer.appendChild(elem);
+            });
+
+            const modelContainer = document.getElementById('models-items');
+            modelsArray.forEach(item => {
+                const name = typeof item === 'string' ? item : item.name || item;
+                const elem = document.createElement('div');
+                elem.className = 'library-item';
+                elem.textContent = name;
+                elem.draggable = true;
+                elem.dataset.type = 'model';
+                elem.dataset.name = name;
+                elem.addEventListener('dragstart', handleDragStart);
+                modelContainer.appendChild(elem);
+            });
+
+            const postContainer = document.getElementById('postprocessors-items');
+            postsArray.forEach(item => {
+                const name = typeof item === 'string' ? item : item.name;
+                const elem = document.createElement('div');
+                elem.className = 'library-item';
+                elem.textContent = name;
+                elem.draggable = true;
+                elem.dataset.type = 'postprocessor';
+                elem.dataset.name = name;
+                elem.addEventListener('dragstart', handleDragStart);
+                postContainer.appendChild(elem);
+            });
+
+            // Setup drag listeners for blockwise config item
+            const blockwiseItems = document.querySelectorAll('#config-items .library-item');
+            blockwiseItems.forEach(item => {
+                item.addEventListener('dragstart', handleDragStart);
+            });
+
+            // Setup canvas drop
+            const canvas = document.querySelector('.canvas');
+            canvas.addEventListener('dragover', handleDragOver);
+            canvas.addEventListener('drop', handleDrop);
+
+            renderCanvas();
+        }
+
+        function toggleSection(btn, sectionId) {
+            const items = document.getElementById(`${sectionId}-items`);
+            const toggle = document.getElementById(`${sectionId}-toggle`);
+            items.classList.toggle('show');
+            toggle.textContent = items.classList.contains('show') ? '▲' : '▼';
+            btn.classList.toggle('active');
+        }
+
+        // Drag and drop
+        function handleDragStart(e) {
+            e.dataTransfer.effectAllowed = 'copy';
+            e.dataTransfer.setData('type', e.target.dataset.type);
+            e.dataTransfer.setData('name', e.target.dataset.name);
+        }
+
+        function handleDragOver(e) {
+            e.preventDefault();
+            e.dataTransfer.dropEffect = 'copy';
+        }
+
+        function handleDrop(e) {
+            e.preventDefault();
+            const type = e.dataTransfer.getData('type');
+            const name = e.dataTransfer.getData('name');
+            
+            // Get canvas element and calculate position relative to it
+            const canvas = document.getElementById('canvas-content');
+            const canvasRect = canvas.getBoundingClientRect();
+            
+            // Calculate position relative to canvas
+            const x = Math.max(0, e.clientX - canvasRect.left);
+            const y = Math.max(0, e.clientY - canvasRect.top);
+            
+            addNode(type, name, { x, y });
+        }
+
+        // Node management with positions
+        let draggedNode = null;
+        let dragOffset = { x: 0, y: 0 };
+
+        // Connection state
+        let connectionDrag = null;
+
+        async function addNode(type, name, dropPosition) {
+            const id = `${type}-${Date.now()}`;
+
+            // Calculate initial position based on drop position or default positions
+            let x, y;
+            
+            if (dropPosition) {
+                // Use drop position if provided
+                x = dropPosition.x;
+                y = dropPosition.y;
+            } else {
+                // Use default positions if no drop position
+                if (type === 'input') {
+                    const count = pipeline.inputs.length;
+                    x = 20;
+                    y = 20 + (count * 180);
+                } else if (type === 'output') {
+                    const count = pipeline.outputs.length;
+                    x = 900;
+                    y = 20 + (count * 180);
+                } else if (type === 'normalizer') {
+                    const count = pipeline.normalizers.length;
+                    x = 200 + (count * 380);
+                    y = 20;
+                } else if (type === 'model') {
+                    const count = pipeline.models.length;
+                    x = 400 + (count * 380);
+                    y = 20;
+                } else if (type === 'postprocessor') {
+                    const count = pipeline.postprocessors.length;
+                    x = 600 + (count * 380);
+                    y = 20;
+                } else if (type === 'blockwise-config') {
+                    x = 100;
+                    y = 400;
+                }
+            }
+
+            // Get default params from APP_CONFIG or API
+            let defaultParams = {};
+            let modelConfig = null;
+            if (type === 'blockwise-config') {
+                // Fetch blockwise config from globals via API
+                try {
+                    const response = await fetch('/api/blockwise-config');
+                    const config = await response.json();
+                    defaultParams = {
+                        queue: config.queue,
+                        charge_group: config.charge_group,
+                        nb_cores_master: config.nb_cores_master,
+                        nb_cores_worker: config.nb_cores_worker,
+                        nb_workers: config.nb_workers,
+                        tmp_dir: config.tmp_dir,
+                        blockwise_tasks_dir: config.blockwise_tasks_dir
+                    };
+                } catch (error) {
+                    console.error('Error fetching blockwise config:', error);
+                    // Fallback to empty params if API fails
+                    defaultParams = {};
+                }
+            } else if (type === 'normalizer') {
+                const normsArray = Array.isArray(APP_CONFIG.availableNormalizers) ?
+                    APP_CONFIG.availableNormalizers : Object.values(APP_CONFIG.availableNormalizers);
+                const normDef = normsArray.find(n => (typeof n === 'string' ? n : n.name) === name);
+                if (normDef && typeof normDef === 'object' && normDef.params) {
+                    defaultParams = { ...normDef.params };
+                }
+            } else if (type === 'model') {
+                const modelsArray = Array.isArray(APP_CONFIG.availableModels) ?
+                    APP_CONFIG.availableModels : Object.values(APP_CONFIG.availableModels);
+                const modelDef = modelsArray.find(m => (typeof m === 'string' ? m : m.name) === name);
+                if (modelDef && typeof modelDef === 'object') {
+                    if (modelDef.params) {
+                        defaultParams = { ...modelDef.params };
+                    }
+                    // Store the full config if available
+                    if (modelDef.config) {
+                        modelConfig = { ...modelDef.config };
+                    }
+                }
+            } else if (type === 'postprocessor') {
+                const postsArray = Array.isArray(APP_CONFIG.availablePostprocessors) ?
+                    APP_CONFIG.availablePostprocessors : Object.values(APP_CONFIG.availablePostprocessors);
+                const postDef = postsArray.find(p => (typeof p === 'string' ? p : p.name) === name);
+                if (postDef && typeof postDef === 'object' && postDef.params) {
+                    defaultParams = { ...postDef.params };
+                }
+            }
+
+            const node = { id, name, params: defaultParams, position: { x, y } };
+            
+            // Add config for models if available
+            if (type === 'model' && modelConfig) {
+                node.config = modelConfig;
+                // Also populate params from config for UI display (exclude 'name')
+                node.params = {};
+                Object.entries(modelConfig).forEach(([key, value]) => {
+                    if (key !== 'name') {
+                        node.params[key] = value;
+                    }
+                });
+            }
+
+            if (type === 'input') {
+                pipeline.inputs.push(node);
+            } else if (type === 'output') {
+                pipeline.outputs.push(node);
+            } else if (type === 'normalizer') {
+                pipeline.normalizers.push(node);
+            } else if (type === 'model') {
+                pipeline.models.push(node);
+            } else if (type === 'postprocessor') {
+                pipeline.postprocessors.push(node);
+            } else if (type === 'blockwise-config') {
+                pipeline.blockwise_config.push(node);
+            }
+
+            // Auto-connect the new node into the pipeline
+            autoConnectNodes();
+            renderCanvas();
+            showMessage(`Added ${name}`, 'success');
+        }
+
+        function removeNode(id, type) {
+            // Remove connections involving this node
+            pipeline.edges = pipeline.edges.filter(e => e.from !== id && e.to !== id);
+
+            if (type === 'input') {
+                pipeline.inputs = pipeline.inputs.filter(n => n.id !== id);
+            } else if (type === 'output') {
+                pipeline.outputs = pipeline.outputs.filter(n => n.id !== id);
+            } else if (type === 'normalizer') {
+                pipeline.normalizers = pipeline.normalizers.filter(n => n.id !== id);
+            } else if (type === 'model') {
+                pipeline.models = pipeline.models.filter(m => m.id !== id);
+            } else if (type === 'postprocessor') {
+                pipeline.postprocessors = pipeline.postprocessors.filter(p => p.id !== id);
+            } else if (type === 'blockwise-config') {
+                pipeline.blockwise_config = pipeline.blockwise_config.filter(c => c.id !== id);
+            }
+            // Re-auto-connect remaining nodes
+            autoConnectNodes();
+            renderCanvas();
+            showMessage('Node removed', 'success');
+        }
+
+        // Global state for output channels modal
+        let outputChannelsModalState = {
+            outputNodeId: null,
+            availableChannels: [],
+            selectedChannels: []
+        };
+
+        function openOutputChannelsModal(outputNodeId) {
+            // Find the output node
+            const outputNode = pipeline.outputs.find(n => n.id === outputNodeId);
+            if (!outputNode) return;
+
+            outputChannelsModalState.outputNodeId = outputNodeId;
+            
+            // Detect available channels from connected models first
+            detectAvailableChannels();
+
+            // Initialize selected channels: if output_channels is empty, select all available channels
+            const existingChannels = outputNode.params?.output_channels || [];
+            outputChannelsModalState.selectedChannels = existingChannels.length > 0 
+                ? existingChannels 
+                : [...outputChannelsModalState.availableChannels]; // Select all by default
+
+            // Populate the modal
+            populateOutputChannelsModal();
+
+            // Show modal
+            document.getElementById('modal-overlay').style.display = 'block';
+            document.getElementById('output-channels-modal').style.display = 'block';
+        }
+
+        function detectAvailableChannels() {
+            outputChannelsModalState.availableChannels = [];
+
+            // Get all connected models (traverse edges to find which models connect to OUTPUT)
+            const outputNodeId = outputChannelsModalState.outputNodeId;
+            const connectedModelIds = new Set();
+
+            // Find edges that connect TO the output node
+            pipeline.edges.forEach(edge => {
+                if (edge.to === outputNodeId) {
+                    // This edge connects FROM something TO the output
+                    const sourceNode = [
+                        ...pipeline.models,
+                        ...pipeline.postprocessors,
+                        ...pipeline.normalizers
+                    ].find(n => n.id === edge.from);
+
+                    if (sourceNode && sourceNode.type === 'model') {
+                        connectedModelIds.add(edge.from);
+                    }
+                }
+            });
+
+            // If no edges, use all models in pipeline
+            if (connectedModelIds.size === 0) {
+                pipeline.models.forEach(model => {
+                    if (model.config?.channels) {
+                        const channels = model.config.channels;
+                        // Ensure channels is an array
+                        if (Array.isArray(channels)) {
+                            outputChannelsModalState.availableChannels = [...channels];
+                        } else if (typeof channels === 'string') {
+                            // If it's a string (from parsing), try to parse it as JSON array
+                            try {
+                                const parsed = JSON.parse(channels);
+                                outputChannelsModalState.availableChannels = Array.isArray(parsed) ? parsed : [channels];
+                            } catch {
+                                outputChannelsModalState.availableChannels = [channels];
+                            }
+                        }
+                    }
+                });
+            } else {
+                // Use channels from connected models
+                connectedModelIds.forEach(modelId => {
+                    const model = pipeline.models.find(m => m.id === modelId);
+                    if (model && model.config?.channels) {
+                        const channels = model.config.channels;
+                        // Ensure channels is an array
+                        if (Array.isArray(channels)) {
+                            outputChannelsModalState.availableChannels = [...channels];
+                        } else if (typeof channels === 'string') {
+                            // If it's a string (from parsing), try to parse it as JSON array
+                            try {
+                                const parsed = JSON.parse(channels);
+                                outputChannelsModalState.availableChannels = Array.isArray(parsed) ? parsed : [channels];
+                            } catch {
+                                outputChannelsModalState.availableChannels = [channels];
+                            }
+                        }
+                    }
+                });
+            }
+
+            // Fallback: if still no channels, show default channels
+            if (outputChannelsModalState.availableChannels.length === 0) {
+                outputChannelsModalState.availableChannels = ['mito', 'nucleus', 'er', 'membrane'];
+                console.warn('No model channels found, using defaults');
+            }
+
+            console.log('Available channels:', outputChannelsModalState.availableChannels);
+            console.log('Selected channels:', outputChannelsModalState.selectedChannels);
+        }
+
+        function populateOutputChannelsModal() {
+            const body = document.getElementById('output-channels-body');
+            let available = outputChannelsModalState.availableChannels;
+            const selected = outputChannelsModalState.selectedChannels;
+
+            // Ensure available is an array
+            if (typeof available === 'string') {
+                try {
+                    available = JSON.parse(available);
+                    if (!Array.isArray(available)) {
+                        available = [available];
+                    }
+                } catch {
+                    available = [available];
+                }
+                outputChannelsModalState.availableChannels = available;
+            } else if (!Array.isArray(available)) {
+                available = Object.keys(available || {});
+            }
+
+            if (available.length === 0) {
+                body.innerHTML = '<p style="color: var(--text-secondary);">No channels detected from models</p>';
+                return;
+            }
+
+            let html = '<div style="margin-bottom: 16px;"><strong>Select output channels:</strong></div>';
+
+            available.forEach(channel => {
+                const isChecked = selected.includes(channel);
+                const checkboxId = `channel-${channel}-${Date.now()}`;
+                html += `
+                    <div class="channel-checkbox-group">
+                        <input type="checkbox" id="${checkboxId}" data-channel="${channel}" 
+                               ${isChecked ? 'checked' : ''}>
+                        <label for="${checkboxId}">
+                            ${channel}
+                        </label>
+                    </div>
+                `;
+            });
+
+            body.innerHTML = html;
+        }
+
+        function saveOutputChannels() {
+            const checkboxes = document.querySelectorAll('#output-channels-body input[type="checkbox"]');
+            const selectedChannels = [];
+
+            checkboxes.forEach(checkbox => {
+                if (checkbox.checked) {
+                    selectedChannels.push(checkbox.dataset.channel);
+                }
+            });
+
+            // Find and update the output node
+            const outputNode = pipeline.outputs.find(n => n.id === outputChannelsModalState.outputNodeId);
+            if (outputNode) {
+                outputNode.params = outputNode.params || {};
+                outputNode.params.output_channels = selectedChannels;
+                outputNode.classList?.add('dirty');
+            }
+
+            closeOutputChannelsModal();
+            renderCanvas();
+            showMessage(`✓ Output channels configured: ${selectedChannels.join(', ')}`, 'success');
+        }
+
+        function closeOutputChannelsModal() {
+            document.getElementById('modal-overlay').style.display = 'none';
+            document.getElementById('output-channels-modal').style.display = 'none';
+            outputChannelsModalState = {
+                outputNodeId: null,
+                availableChannels: [],
+                selectedChannels: []
+            };
+        }
+
+        function renderCanvas() {
+            const canvas = document.getElementById('canvas-content');
+            canvas.innerHTML = '';
+
+            const allNodes = [
+                ...pipeline.inputs.map(n => ({ ...n, type: 'input' })),
+                ...pipeline.outputs.map(n => ({ ...n, type: 'output' })),
+                ...pipeline.normalizers.map(n => ({ ...n, type: 'normalizer' })),
+                ...pipeline.models.map(m => ({ ...m, type: 'model' })),
+                ...pipeline.postprocessors.map(p => ({ ...p, type: 'postprocessor' })),
+                ...pipeline.blockwise_config.map(c => ({ ...c, type: 'blockwise-config' }))
+            ];
+
+            if (allNodes.length === 0) {
+                canvas.innerHTML = `
+                    <div class="empty-state">
+                        <div class="empty-state-icon">🎨</div>
+                        <div class="empty-state-text">Drag INPUT/OUTPUT or other items from the sidebar to start building your pipeline</div>
+                    </div>
+                `;
+                return;
+            }
+
+            // Create SVG layer for connections
+            const svg = document.createElementNS('http://www.w3.org/2000/svg', 'svg');
+            svg.classList.add('connections-svg');
+            svg.setAttribute('width', '100%');
+            svg.setAttribute('height', '100%');
+            svg.style.minHeight = '600px';
+            canvas.appendChild(svg);
+
+            // Render nodes
+            allNodes.forEach(node => {
+                const nodeEl = createNodeElement(node);
+                canvas.appendChild(nodeEl);
+                setupNodeDragging(nodeEl, node);
+            });
+
+            // Render connections
+            renderConnections();
+        }
+
+        function createNodeElement(node) {
+            const div = document.createElement('div');
+            div.className = 'node-box';
+
+            // Add special class for different node types
+            if (node.type === 'input' || node.type === 'output') {
+                div.classList.add('io-node');
+                div.classList.add(node.type + '-node');
+            } else if (node.type === 'blockwise-config') {
+                div.classList.add('config-node');
+            }
+
+            div.id = `node-${node.id}`;
+            div.dataset.nodeid = node.id;
+            div.dataset.nodetype = node.type;
+
+            // Apply saved position
+            const pos = node.position || { x: 20, y: 20 };
+            div.style.left = pos.x + 'px';
+            div.style.top = pos.y + 'px';
+
+            // For INPUT nodes, calculate width based on dataset_path length
+            if (node.type === 'input' || node.type === 'output') {
+                const datasetPath = node.params?.dataset_path || '';
+                // Measure text width more accurately using canvas
+                const canvas = document.createElement('canvas');
+                const ctx = canvas.getContext('2d');
+                ctx.font = '11px Menlo, Monaco, monospace';
+                const textMetrics = ctx.measureText(datasetPath);
+                const textWidth = textMetrics.width + 150;
+                const minWidth = 400;
+                const maxWidth = 1600;
+                const calculatedWidth = Math.min(Math.max(textWidth, minWidth), maxWidth);
+                div.style.width = calculatedWidth + 'px';
+            }
+
+            let paramsHTML = '';
+            const nodeParams = node.params || {};
+            Object.entries(nodeParams).forEach(([key, value]) => {
+                // Determine param type from current value
+                const paramType = typeof value === 'boolean' ? 'boolean' : 'text';
+                let inputHTML = '';
+                
+                // Format the display value with proper HTML escaping
+                let displayValue = '';
+                if (value === null || value === undefined) {
+                    displayValue = '';
+                } else if (typeof value === 'object') {
+                    displayValue = JSON.stringify(value).replace(/"/g, '&quot;');
+                } else {
+                    displayValue = String(value).replace(/"/g, '&quot;');
+                }
+
+                if (paramType === 'boolean') {
+                    const checked = value ? 'checked' : '';
+                    inputHTML = `
+                        <select class="param-input" data-key="${key}" data-type="boolean"
+                                onchange="handleParamChange(this, '${node.id}', '${node.type}')">
+                            <option value="true" ${value === true ? 'selected' : ''}>true</option>
+                            <option value="false" ${value === false ? 'selected' : ''}>false</option>
+                        </select>
+                    `;
+                } else if (typeof value === 'number') {
+                    inputHTML = `
+                        <input type="number" class="param-input" data-key="${key}" data-type="number"
+                               value="${displayValue}"
+                               onchange="handleParamChange(this, '${node.id}', '${node.type}')">
+                    `;
+                } else {
+                    inputHTML = `
+                        <input type="text" class="param-input" data-key="${key}" data-type="text"
+                               value="${displayValue}"
+                               onchange="handleParamChange(this, '${node.id}', '${node.type}')">
+                    `;
+                }
+
+                paramsHTML += `
+                    <div class="param-group">
+                        <label class="param-label">${key}</label>
+                        ${inputHTML}
+                    </div>
+                `;
+            });
+
+            // For INPUT/OUTPUT nodes, show minimal UI
+            if (node.type === 'input' || node.type === 'output') {
+                let inputContent = '';
+                if (node.type === 'input' || node.type === 'output') {
+                    // Show dataset_path field for INPUT and OUTPUT nodes
+                    const datasetPath = node.params?.dataset_path || '';
+                    const displayPath = datasetPath.replace(/"/g, '&quot;');
+                    const labelText = node.type === 'input' ? 'Dataset Path' : 'Output Path';
+                    inputContent = `
+                        <div class="param-group" style="margin-top: 8px;">
+                            <label class="param-label">${labelText}</label>
+                            <input type="text" class="param-input" data-key="dataset_path" data-type="text"
+                                   value="${displayPath}"
+                                   onchange="handleParamChange(this, '${node.id}', '${node.type}')">
+                        </div>
+                    `;
+                    
+                    // Add output channels configuration for OUTPUT nodes
+                    if (node.type === 'output') {
+                        const selectedChannels = node.params?.output_channels || [];
+                        const selectedDisplay = Array.isArray(selectedChannels) ? selectedChannels.join(', ') : '';
+                        inputContent += `
+                            <div class="param-group">
+                                <label class="param-label">Output Channels</label>
+                                <button class="configure-btn" onclick="openOutputChannelsModal('${node.id}')">
+                                    🔧 Configure (${selectedChannels.length || 0} selected)
+                                </button>
+                                <div class="selected-channels" style="font-size: 11px; color: var(--text-secondary); margin-top: 4px;">
+                                    ${selectedDisplay ? `${selectedDisplay}` : 'Click configure to select'}
+                                </div>
+                            </div>
+                        `;
+                    }
+                }
+                div.innerHTML = `
+                    <div class="node-input" onmousedown="startConnection(event, '${node.id}', 'input')" title="Connect from here"></div>
+                    <div class="node-output" onmousedown="startConnection(event, '${node.id}', 'output')" title="Connect to here"></div>
+                    <div class="node-header">
+                        <div>
+                            <div class="node-title">${node.name}</div>
+                        </div>
+                        <div class="node-actions">
+                            <button class="icon-btn save" onclick="saveNode('${node.id}', '${node.type}')">💾</button>
+                            <button class="icon-btn delete" onclick="removeNode('${node.id}', '${node.type}')">🗑️</button>
+                        </div>
+                    </div>
+                    ${inputContent}
+                `;
+            } else if (node.type === 'blockwise-config') {
+                // Blockwise configuration node
+                const fields = [
+                    { key: 'queue', label: 'Queue' },
+                    { key: 'charge_group', label: 'Charge Group' },
+                    { key: 'nb_cores_master', label: 'Cores Master' },
+                    { key: 'nb_cores_worker', label: 'Cores Worker' },
+                    { key: 'nb_workers', label: 'Workers' },
+                    { key: 'tmp_dir', label: 'Temp Directory' },
+                    { key: 'blockwise_tasks_dir', label: 'Tasks Directory' }
+                ];
+                
+                let blockwiseHTML = '';
+                fields.forEach(field => {
+                    const value = node.params?.[field.key] || '';
+                    const displayValue = String(value).replace(/"/g, '&quot;');
+                    const inputType = field.key.startsWith('nb_') ? 'number' : 'text';
+                    blockwiseHTML += `
+                        <div class="param-group">
+                            <label class="param-label">${field.label}</label>
+                            <input type="${inputType}" class="param-input" data-key="${field.key}" data-type="${inputType}"
+                                   value="${displayValue}"
+                                   onchange="handleParamChange(this, '${node.id}', 'blockwise-config')">
+                        </div>
+                    `;
+                });
+                
+                div.innerHTML = `
+                    <div class="node-header">
+                        <div>
+                            <div class="node-title">⚙️ Blockwise Config</div>
+                        </div>
+                        <div class="node-actions">
+                            <button class="icon-btn save" onclick="saveNode('${node.id}', 'blockwise-config')">💾</button>
+                            <button class="icon-btn delete" onclick="removeNode('${node.id}', 'blockwise-config')">🗑️</button>
+                        </div>
+                    </div>
+                    <div class="node-params">
+                        ${blockwiseHTML}
+                    </div>
+                `;
+            } else {
+                div.innerHTML = `
+                    <div class="node-input" onmousedown="startConnection(event, '${node.id}', 'input')" title="Connect from here"></div>
+                    <div class="node-output" onmousedown="startConnection(event, '${node.id}', 'output')" title="Connect to here"></div>
+                    <div class="node-header">
+                        <div>
+                            <div class="node-title">${node.name}</div>
+                            <div class="node-type">${node.type}</div>
+                        </div>
+                        <div class="node-actions">
+                            <button class="icon-btn save" onclick="saveNode('${node.id}', '${node.type}')">💾</button>
+                            <button class="icon-btn reset" onclick="resetNode('${node.id}', '${node.type}')">↻</button>
+                            <button class="icon-btn delete" onclick="removeNode('${node.id}', '${node.type}')">🗑️</button>
+                        </div>
+                    </div>
+                    <div class="node-params">
+                        ${paramsHTML || '<div class="param-group"><div class="param-label">No parameters</div></div>'}
+                    </div>
+                `;
+            }
+
+            return div;
+        }
+
+        function setupNodeDragging(nodeEl, node) {
+            const header = nodeEl.querySelector('.node-header');
+
+            header.addEventListener('mousedown', (e) => {
+                // Find the actual node in pipeline arrays (not the copy)
+                let actualNode;
+                if (node.type === 'input') {
+                    actualNode = pipeline.inputs.find(n => n.id === node.id);
+                } else if (node.type === 'output') {
+                    actualNode = pipeline.outputs.find(n => n.id === node.id);
+                } else if (node.type === 'normalizer') {
+                    actualNode = pipeline.normalizers.find(n => n.id === node.id);
+                } else if (node.type === 'model') {
+                    actualNode = pipeline.models.find(m => m.id === node.id);
+                } else if (node.type === 'postprocessor') {
+                    actualNode = pipeline.postprocessors.find(p => p.id === node.id);
+                } else if (node.type === 'blockwise-config') {
+                    actualNode = pipeline.blockwise_config.find(c => c.id === node.id);
+                }
+
+                if (!actualNode) return;
+
+                draggedNode = {
+                    element: nodeEl,
+                    node: actualNode,
+                    startX: e.clientX,
+                    startY: e.clientY,
+                    originalX: actualNode.position?.x || 0,
+                    originalY: actualNode.position?.y || 0
+                };
+
+                nodeEl.style.zIndex = 1000;
+                nodeEl.style.cursor = 'grabbing';
+                header.style.cursor = 'grabbing';
+            });
+
+            header.style.cursor = 'grab';
+        }
+
+        function handleParamChange(input, nodeId, nodeType) {
+            const nodeBox = document.getElementById(`node-${nodeId}`);
+            nodeBox.classList.add('dirty');
+        }
+
+        function saveNode(nodeId, nodeType) {
+            const nodeBox = document.getElementById(`node-${nodeId}`);
+            const inputs = nodeBox.querySelectorAll('.param-input');
+
+            let node;
+            if (nodeType === 'input') {
+                node = pipeline.inputs.find(n => n.id === nodeId);
+            } else if (nodeType === 'output') {
+                node = pipeline.outputs.find(n => n.id === nodeId);
+            } else if (nodeType === 'normalizer') {
+                node = pipeline.normalizers.find(n => n.id === nodeId);
+            } else if (nodeType === 'model') {
+                node = pipeline.models.find(m => m.id === nodeId);
+            } else if (nodeType === 'postprocessor') {
+                node = pipeline.postprocessors.find(p => p.id === nodeId);
+            } else if (nodeType === 'blockwise-config') {
+                node = pipeline.blockwise_config.find(c => c.id === nodeId);
+            }
+
+            if (node) {
+                inputs.forEach(input => {
+                    const key = input.dataset.key;
+                    const type = input.dataset.type || 'text';
+                    let value;
+
+                    if (type === 'boolean') {
+                        value = input.value === 'true';
+                    } else if (type === 'number') {
+                        value = parseFloat(input.value);
+                        if (isNaN(value)) {
+                            showMessage(`Invalid number for ${key}`, 'error');
+                            return;
+                        }
+                    } else {
+                        try {
+                            value = JSON.parse(input.value);
+                        } catch {
+                            value = input.value;
+                        }
+                    }
+
+                    node.params[key] = value;
+                    // Also update config if this is a model node
+                    if (nodeType === 'model' && node.config) {
+                        node.config[key] = value;
+                    }
+                    // Sync dataset_path to backend when updated on INPUT or OUTPUT node
+                    if ((nodeType === 'input' || nodeType === 'output') && key === 'dataset_path') {
+                        fetch('/api/dataset-path', {
+                            method: 'POST',
+                            headers: { 'Content-Type': 'application/json' },
+                            body: JSON.stringify({ dataset_path: value })
+                        }).then(r => r.json()).then(data => {
+                            console.log('Dataset path synced:', data);
+                            // Recalculate node width based on new dataset_path
+                            const nodeBox = document.getElementById(`node-${nodeId}`);
+                            if (nodeBox) {
+                                const canvas = document.createElement('canvas');
+                                const ctx = canvas.getContext('2d');
+                                ctx.font = '11px Menlo, Monaco, monospace';
+                                const textMetrics = ctx.measureText(value);
+                                const textWidth = textMetrics.width + 150;
+                                const minWidth = 400;
+                                const maxWidth = 1600;
+                                const calculatedWidth = Math.min(Math.max(textWidth, minWidth), maxWidth);
+                                nodeBox.style.width = calculatedWidth + 'px';
+                            }
+                        });
+                    }
+                });
+                
+                // Sync blockwise config to backend if this is a blockwise-config node
+                if (nodeType === 'blockwise-config') {
+                    const config = {
+                        queue: node.params.queue,
+                        charge_group: node.params.charge_group,
+                        nb_cores_master: node.params.nb_cores_master,
+                        nb_cores_worker: node.params.nb_cores_worker,
+                        nb_workers: node.params.nb_workers,
+                        tmp_dir: node.params.tmp_dir,
+                        blockwise_tasks_dir: node.params.blockwise_tasks_dir
+                    };
+                    fetch('/api/blockwise-config', {
+                        method: 'POST',
+                        headers: { 'Content-Type': 'application/json' },
+                        body: JSON.stringify(config)
+                    }).then(r => r.json()).then(data => {
+                        console.log('Blockwise config synced:', data);
+                    }).catch(err => console.error('Error syncing blockwise config:', err));
+                }
+                
+                nodeBox.classList.remove('dirty');
+                showMessage('Parameters saved', 'success');
+            }
+        }
+
+        function resetNode(nodeId, nodeType) {
+            renderCanvas();
+            showMessage('Node reset', 'success');
+        }
+
+        function showMessage(msg, type) {
+            const div = document.createElement('div');
+            div.className = `message ${type}`;
+            div.textContent = msg;
+            document.body.appendChild(div);
+            
+            // Calculate bottom position based on existing messages
+            const messages = document.querySelectorAll('.message');
+            let bottomPosition = 20;
+            messages.forEach((m, idx) => {
+                if (idx < messages.length - 1) { // All except the new one
+                    bottomPosition += m.offsetHeight + 10; // Add height + gap
+                }
+            });
+            div.style.bottom = bottomPosition + 'px';
+            
+            setTimeout(() => div.remove(), 3000);
+        }
+
+        async function applyPipeline() {
+            // Transform pipeline data to backend format - include complete state for persistence
+            const payload = {
+                input_normalizers: pipeline.normalizers.map(n => ({
+                    id: n.id,
+                    name: n.name,
+                    params: n.params,
+                    position: n.position
+                })),
+                postprocessors: pipeline.postprocessors.map(p => ({
+                    id: p.id,
+                    name: p.name,
+                    params: p.params,
+                    position: p.position
+                })),
+                models: pipeline.models.map(m => ({
+                    id: m.id,
+                    name: m.name,
+                    params: m.params || {},
+                    position: m.position
+                })),
+                inputs: pipeline.inputs.map(i => ({
+                    id: i.id,
+                    position: i.position
+                })),
+                outputs: pipeline.outputs.map(o => ({
+                    id: o.id,
+                    position: o.position
+                })),
+                edges: pipeline.edges.map(e => ({
+                    id: e.id,
+                    from: e.from,
+                    to: e.to
+                }))
+            };
+
+            try {
+                const response = await fetch('/api/pipeline/apply', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify(payload)
+                });
+
+                const result = await response.json();
+                if (response.ok) {
+                    showMessage('✓ Pipeline applied successfully!', 'success');
+                } else {
+                    showMessage('Error: ' + (result.error || 'Unknown error'), 'error');
+                }
+            } catch (err) {
+                showMessage('Failed: ' + err.message, 'error');
+            }
+        }
+
+        // Export YAML
+        function exportYAML() {
+            let yaml = '';
+
+            // Export inputs with dataset_path
+            if (pipeline.inputs.length > 0) {
+                yaml += 'inputs:\n';
+                pipeline.inputs.forEach((n, index) => {
+                    yaml += `  - id: ${n.id}\n`;
+                    // Add dataset_path to the first input node
+                    if (index === 0) {
+                        const datasetPath = n.params?.dataset_path || APP_CONFIG.datasetPath || '';
+                        if (datasetPath) {
+                            yaml += `    dataset_path: ${datasetPath}\n`;
+                        }
+                    }
+                    yaml += `    position:\n`;
+                    yaml += `      x: ${n.position.x}\n`;
+                    yaml += `      y: ${n.position.y}\n`;
+                });
+                yaml += '\n';
+            }
+
+            // Export outputs
+            if (pipeline.outputs.length > 0) {
+                yaml += 'outputs:\n';
+                pipeline.outputs.forEach(n => {
+                    yaml += `  - id: ${n.id}\n`;
+                    if (n.params && Object.keys(n.params).length > 0) {
+                        Object.entries(n.params).forEach(([k, v]) => {
+                            yaml += `    ${k}: ${JSON.stringify(v)}\n`;
+                        });
+                    }
+                    yaml += `    position:\n`;
+                    yaml += `      x: ${n.position.x}\n`;
+                    yaml += `      y: ${n.position.y}\n`;
+                });
+                yaml += '\n';
+            }
+
+            // Export normalizers
+            if (pipeline.normalizers.length > 0) {
+                yaml += 'input_normalizers:\n';
+                pipeline.normalizers.forEach(n => {
+                    yaml += `  - id: ${n.id}\n`;
+                    yaml += `    name: ${n.name}\n`;
+                    if (Object.keys(n.params).length > 0) {
+                        yaml += '    params:\n';
+                        Object.entries(n.params).forEach(([k, v]) => {
+                            yaml += `      ${k}: ${JSON.stringify(v)}\n`;
+                        });
+                    }
+                    yaml += `    position:\n`;
+                    yaml += `      x: ${n.position.x}\n`;
+                    yaml += `      y: ${n.position.y}\n`;
+                });
+                yaml += '\n';
+            }
+
+            // Export models
+            if (pipeline.models.length > 0) {
+                yaml += 'models:\n';
+                pipeline.models.forEach(m => {
+                    yaml += `  - id: ${m.id}\n`;
+                    yaml += `    name: ${m.name}\n`;
+
+                    // If we have a full config (from backend ModelConfig.to_dict()), use it
+                    if (m.config && typeof m.config === 'object') {
+                        // Export all config fields except 'name' (already exported above)
+                        Object.entries(m.config).forEach(([k, v]) => {
+                            if (k !== 'name') {
+                                // Handle arrays specially
+                                if (Array.isArray(v)) {
+                                    yaml += `    ${k}: [${v.join(', ')}]\n`;
+                                } else if (typeof v === 'object' && v !== null) {
+                                    yaml += `    ${k}: ${JSON.stringify(v)}\n`;
+                                } else {
+                                    yaml += `    ${k}: ${JSON.stringify(v)}\n`;
+                                }
+                            }
+                        });
+                    } else if (Object.keys(m.params || {}).length > 0) {
+                        // Fallback to params if no config available
+                        yaml += '    params:\n';
+                        Object.entries(m.params).forEach(([k, v]) => {
+                            yaml += `      ${k}: ${JSON.stringify(v)}\n`;
+                        });
+                    }
+
+                    yaml += `    position:\n`;
+                    yaml += `      x: ${m.position.x}\n`;
+                    yaml += `      y: ${m.position.y}\n`;
+                });
+                yaml += '\n';
+            }
+
+            // Export postprocessors
+            if (pipeline.postprocessors.length > 0) {
+                yaml += 'postprocessors:\n';
+                pipeline.postprocessors.forEach(p => {
+                    yaml += `  - id: ${p.id}\n`;
+                    yaml += `    name: ${p.name}\n`;
+                    if (Object.keys(p.params).length > 0) {
+                        yaml += '    params:\n';
+                        Object.entries(p.params).forEach(([k, v]) => {
+                            yaml += `      ${k}: ${JSON.stringify(v)}\n`;
+                        });
+                    }
+                    yaml += `    position:\n`;
+                    yaml += `      x: ${p.position.x}\n`;
+                    yaml += `      y: ${p.position.y}\n`;
+                });
+                yaml += '\n';
+            }
+
+            // Export blockwise configuration
+            if (pipeline.blockwise_config.length > 0) {
+                yaml += 'blockwise_config:\n';
+                pipeline.blockwise_config.forEach(c => {
+                    yaml += `  - id: ${c.id}\n`;
+                    yaml += `    name: ${c.name}\n`;
+                    if (Object.keys(c.params).length > 0) {
+                        yaml += '    params:\n';
+                        Object.entries(c.params).forEach(([k, v]) => {
+                            yaml += `      ${k}: ${JSON.stringify(v)}\n`;
+                        });
+                    }
+                    yaml += `    position:\n`;
+                    yaml += `      x: ${c.position.x}\n`;
+                    yaml += `      y: ${c.position.y}\n`;
+                });
+                yaml += '\n';
+            }
+
+            // Export edges (connections)
+            if (pipeline.edges.length > 0) {
+                yaml += 'edges:\n';
+                pipeline.edges.forEach(e => {
+                    yaml += `  - id: ${e.id}\n`;
+                    yaml += `    from: ${e.from}\n`;
+                    yaml += `    to: ${e.to}\n`;
+                });
+            }
+
+            downloadFile(yaml, 'pipeline.yaml', 'text/yaml');
+        }
+
+        // Export JSON
+        function exportJSON() {
+            const data = JSON.stringify(pipeline, null, 2);
+            downloadFile(data, 'pipeline.json', 'application/json');
+        }
+
+        // Download file helper
+        function downloadFile(content, filename, type) {
+            const blob = new Blob([content], { type });
+            const url = URL.createObjectURL(blob);
+            const a = document.createElement('a');
+            a.href = url;
+            a.download = filename;
+            a.click();
+            URL.revokeObjectURL(url);
+            showMessage('Exported as ' + filename, 'success');
+        }
+
+        // Import file
+        function importFile() {
+            const file = document.getElementById('import-file').files[0];
+            if (!file) return;
+
+            const reader = new FileReader();
+            reader.onload = (e) => {
+                try {
+                    const content = e.target.result;
+                    let data;
+                    if (file.name.endsWith('.yaml')) {
+                        data = parseYAML(content);
+                    } else {
+                        data = JSON.parse(content);
+                    }
+
+                    // Replace pipeline with imported data
+                    console.log('Imported data:', data);  // Debug log
+
+                    pipeline = {
+                        inputs: (data.inputs || []).map((n, i) => ({
+                            id: n.id || `input-${Date.now()}-${i}`,
+                            name: 'INPUT',
+                            params: n.params || { dataset_path: APP_CONFIG.datasetPath || '' },
+                            position: n.position || { x: 20, y: 20 + (i * 180) }
+                        })),
+                        outputs: (data.outputs || []).map((n, i) => ({
+                            id: n.id || `output-${Date.now()}-${i}`,
+                            name: 'OUTPUT',
+                            params: n.params || {},
+                            position: n.position || { x: 900, y: 20 + (i * 180) }
+                        })),
+                        normalizers: (data.input_normalizers || data.normalizers || []).map((n, i) => {
+                            console.log('Processing normalizer:', n);  // Debug log
+                            return {
+                                id: n.id || `norm-${Date.now()}-${i}`,
+                                name: typeof n === 'string' ? n : (n.name || n),
+                                params: n.params || {},
+                                position: n.position || { x: 200 + (i * 380), y: 20 }
+                            };
+                        }),
+                        models: (data.models || []).map((m, i) => {
+                            const model = {
+                                id: m.id || `model-${Date.now()}-${i}`,
+                                name: typeof m === 'string' ? m : (m.name || m),
+                                params: m.params || m.config || {},
+                                position: m.position || { x: 400 + (i * 380), y: 20 }
+                            };
+                            // Preserve config if it exists (for re-export with build_model_from_entry)
+                            if (m.config && typeof m.config === 'object') {
+                                model.config = m.config;
+                            } else if (m.type) {
+                                // Build config from individual fields if type exists
+                                model.config = { ...m };
+                                delete model.config.id;
+                                delete model.config.params;
+                                delete model.config.position;
+                                // Also ensure params includes all config fields
+                                model.params = { ...model.config };
+                            }
+                            return model;
+                        }),
+                        postprocessors: (data.postprocessors || []).map((p, i) => ({
+                            id: p.id || `post-${Date.now()}-${i}`,
+                            name: typeof p === 'string' ? p : (p.name || p),
+                            params: p.params || {},
+                            position: p.position || { x: 600 + (i * 380), y: 20 }
+                        })),
+                        blockwise_config: (data.blockwise_config || []).map((c, i) => ({
+                            id: c.id || `blockwise-${Date.now()}-${i}`,
+                            name: 'Blockwise Configuration',
+                            params: c.params,
+                            position: c.position || { x: 100, y: 400 }
+                        })),
+                        edges: data.edges || []
+                    };
+
+                    console.log('Final pipeline:', pipeline);  // Debug log
+                    renderCanvas();
+                    
+                    // If blockwise config is imported, sync it to backend globals
+                    if (pipeline.blockwise_config.length > 0) {
+                        const blockwiseNode = pipeline.blockwise_config[0];
+                        const blockwiseParams = blockwiseNode.params;
+                        if (blockwiseParams) {
+                            const config = {
+                                queue: blockwiseParams.queue,
+                                charge_group: blockwiseParams.charge_group,
+                                nb_cores_master: blockwiseParams.nb_cores_master,
+                                nb_cores_worker: blockwiseParams.nb_cores_worker,
+                                nb_workers: blockwiseParams.nb_workers,
+                                tmp_dir: blockwiseParams.tmp_dir,
+                                blockwise_tasks_dir: blockwiseParams.blockwise_tasks_dir
+                            };
+                            fetch('/api/blockwise-config', {
+                                method: 'POST',
+                                headers: { 'Content-Type': 'application/json' },
+                                body: JSON.stringify(config)
+                            }).then(r => r.json()).then(data => {
+                                console.log('Blockwise config synced from import:', data);
+                                showMessage('✓ Blockwise config updated from import', 'success');
+                            }).catch(err => {
+                                console.error('Error syncing blockwise config from import:', err);
+                            });
+                        }
+                    }
+                    
+                    showMessage('Pipeline imported successfully', 'success');
+                    document.getElementById('import-file').value = '';
+                } catch (err) {
+                    showMessage('Import failed: ' + err.message, 'error');
+                }
+            };
+            reader.readAsText(file);
+        }
+
+        // Simple YAML parser
+        function parseYAML(yaml) {
+            const result = {
+                inputs: [],
+                outputs: [],
+                input_normalizers: [],
+                models: [],
+                postprocessors: [],
+                blockwise_config: [],
+                edges: []
+            };
+            let currentSection = null;
+            let currentItem = null;
+            let inParams = false;
+            let inPosition = false;
+
+            yaml.split('\n').forEach(line => {
+                const trimmed = line.trim();
+                if (!trimmed || trimmed.startsWith('#')) return;
+
+                // Section headers
+                if (trimmed === 'inputs:') {
+                    currentSection = 'inputs';
+                    inParams = false;
+                    inPosition = false;
+                } else if (trimmed === 'outputs:') {
+                    currentSection = 'outputs';
+                    inParams = false;
+                    inPosition = false;
+                } else if (trimmed === 'input_normalizers:') {
+                    currentSection = 'input_normalizers';
+                    inParams = false;
+                    inPosition = false;
+                } else if (trimmed === 'models:') {
+                    currentSection = 'models';
+                    inParams = false;
+                    inPosition = false;
+                } else if (trimmed === 'postprocessors:') {
+                    currentSection = 'postprocessors';
+                    inParams = false;
+                    inPosition = false;
+                } else if (trimmed === 'blockwise_config:') {
+                    currentSection = 'blockwise_config';
+                    inParams = false;
+                    inPosition = false;
+                } else if (trimmed === 'edges:') {
+                    currentSection = 'edges';
+                    inParams = false;
+                    inPosition = false;
+                } else if (trimmed === 'params:') {
+                    inParams = true;
+                    inPosition = false;
+                    // Initialize params object if it doesn't exist
+                    if (currentItem && !currentItem.params) {
+                        currentItem.params = {};
+                    }
+                } else if (trimmed === 'position:') {
+                    inPosition = true;
+                    inParams = false;
+                    if (currentItem && !currentItem.position) {
+                        currentItem.position = {};
+                    }
+                } else if (trimmed.startsWith('- ')) {
+                    // New item
+                    const match = trimmed.match(/^- (\w+):\s*(.*)$/);
+                    if (match) {
+                        const [, key, value] = match;
+                        currentItem = { [key]: value.trim() };
+                        if (currentSection) result[currentSection].push(currentItem);
+                        inParams = false;
+                        inPosition = false;
+                    } else {
+                        currentItem = { name: trimmed.replace('- ', '').trim() };
+                        if (currentSection) result[currentSection].push(currentItem);
+                        inParams = false;
+                        inPosition = false;
+                    }
+                } else if (trimmed.includes(':')) {
+                    const [key, value] = trimmed.split(':').map(s => s.trim());
+                    if (!currentItem) return;
+
+                    if (inPosition) {
+                        try {
+                            currentItem.position[key] = parseFloat(value) || 0;
+                        } catch {
+                            currentItem.position[key] = 0;
+                        }
+                    } else if (inParams) {
+                        try {
+                            currentItem.params = currentItem.params || {};
+                            currentItem.params[key] = JSON.parse(value);
+                        } catch {
+                            currentItem.params = currentItem.params || {};
+                            currentItem.params[key] = value;
+                        }
+                    } else {
+                        try {
+                            currentItem[key] = JSON.parse(value);
+                        } catch {
+                            currentItem[key] = value;
+                        }
+
+                        // For inputs/outputs, preserve dataset_path
+                        if ((currentSection === 'inputs' || currentSection === 'outputs') && key === 'dataset_path') {
+                            if (!currentItem.params) currentItem.params = {};
+                            currentItem.params.dataset_path = currentItem[key];
+                        }
+
+                        // For models section, also build the config object for use with build_model_from_entry
+                        if (currentSection === 'models' && key !== 'id' && key !== 'position' && key !== 'params' && key !== 'name') {
+                            currentItem.config = currentItem.config || {};
+                            try {
+                                let parsedValue = JSON.parse(value);
+                                // Special handling for channels - ensure it's always an array
+                                if (key === 'channels' && !Array.isArray(parsedValue)) {
+                                    parsedValue = [parsedValue];
+                                }
+                                currentItem.config[key] = parsedValue;
+                            } catch {
+                                // If JSON parse fails, check if this is a channels field
+                                if (key === 'channels') {
+                                    currentItem.config[key] = [value];
+                                } else {
+                                    currentItem.config[key] = value;
+                                }
+                            }
+                            // Also add to params for display
+                            currentItem.params = currentItem.params || {};
+                            currentItem.params[key] = currentItem.config[key];
+                        }
+                    }
+                }
+            });
+
+            return result;
+        }
+
+        // Connection management
+        function startConnection(event, nodeId, portType) {
+            event.stopPropagation();
+            event.preventDefault();
+
+            // Only allow dragging from output ports
+            if (portType !== 'output') return;
+
+            const dot = event.target;
+            dot.classList.add('active');
+
+            connectionDrag = {
+                fromNode: nodeId,
+                fromPort: portType,
+                startDot: dot
+            };
+
+            // Create temporary connection line
+            const svg = document.querySelector('.connections-svg');
+            const tempPath = document.createElementNS('http://www.w3.org/2000/svg', 'path');
+            tempPath.classList.add('connection-path', 'dragging-connection');
+            tempPath.id = 'temp-connection';
+            svg.appendChild(tempPath);
+
+            showMessage('Drag to an input to create connection', 'info');
+        }
+
+        function updateConnectionDrag(event) {
+            if (!connectionDrag) return;
+
+            const tempPath = document.getElementById('temp-connection');
+            if (!tempPath) return;
+
+            // Get start position from the output dot
+            const fromNodeEl = document.getElementById(`node-${connectionDrag.fromNode}`);
+            const fromDot = fromNodeEl.querySelector('.node-output');
+            const fromRect = fromDot.getBoundingClientRect();
+            const canvasRect = document.getElementById('canvas-content').getBoundingClientRect();
+
+            const x1 = fromRect.left + fromRect.width / 2 - canvasRect.left;
+            const y1 = fromRect.top + fromRect.height / 2 - canvasRect.top;
+
+            // Mouse position as end point
+            const x2 = event.clientX - canvasRect.left;
+            const y2 = event.clientY - canvasRect.top;
+
+            // Create curved Bezier path
+            const pathData = createBezierPath(x1, y1, x2, y2);
+            tempPath.setAttribute('d', pathData);
+        }
+
+        function endConnection(event) {
+            if (!connectionDrag) return;
+
+            // Remove active state
+            if (connectionDrag.startDot) {
+                connectionDrag.startDot.classList.remove('active');
+            }
+
+            // Remove temporary connection
+            const tempPath = document.getElementById('temp-connection');
+            if (tempPath) tempPath.remove();
+
+            // Check if we're over an input dot
+            const target = document.elementFromPoint(event.clientX, event.clientY);
+            if (target && target.classList.contains('node-input')) {
+                const toNodeEl = target.closest('.node-box');
+                const toNodeId = toNodeEl.dataset.nodeid;
+
+                // Don't connect to self
+                if (toNodeId !== connectionDrag.fromNode) {
+                    // Check if connection already exists
+                    const exists = pipeline.edges.some(e =>
+                        e.from === connectionDrag.fromNode && e.to === toNodeId
+                    );
+
+                    if (!exists) {
+                        // Add new connection
+                        pipeline.edges.push({
+                            id: `edge-${Date.now()}`,
+                            from: connectionDrag.fromNode,
+                            to: toNodeId
+                        });
+                        renderConnections();
+                        showMessage('Connection created', 'success');
+                    } else {
+                        showMessage('Connection already exists', 'info');
+                    }
+                }
+            }
+
+            connectionDrag = null;
+        }
+
+        function createBezierPath(x1, y1, x2, y2) {
+            // Calculate control points for smooth curve
+            const dx = Math.abs(x2 - x1);
+            const curveStrength = Math.min(dx * 0.5, 150);
+
+            const cx1 = x1 + curveStrength;
+            const cy1 = y1;
+            const cx2 = x2 - curveStrength;
+            const cy2 = y2;
+
+            return `M ${x1} ${y1} C ${cx1} ${cy1}, ${cx2} ${cy2}, ${x2} ${y2}`;
+        }
+
+        function renderConnections() {
+            const svg = document.querySelector('.connections-svg');
+            if (!svg) return;
+
+            // Remove old connections (but keep temp connection if it exists)
+            const existingPaths = svg.querySelectorAll('.connection-path:not(.dragging-connection)');
+            existingPaths.forEach(p => p.remove());
+
+            // Render each edge
+            pipeline.edges.forEach(edge => {
+                const fromNodeEl = document.getElementById(`node-${edge.from}`);
+                const toNodeEl = document.getElementById(`node-${edge.to}`);
+
+                if (!fromNodeEl || !toNodeEl) return;
+
+                const fromDot = fromNodeEl.querySelector('.node-output');
+                const toDot = toNodeEl.querySelector('.node-input');
+
+                if (!fromDot || !toDot) return;
+
+                const canvasRect = document.getElementById('canvas-content').getBoundingClientRect();
+                const fromRect = fromDot.getBoundingClientRect();
+                const toRect = toDot.getBoundingClientRect();
+
+                const x1 = fromRect.left + fromRect.width / 2 - canvasRect.left;
+                const y1 = fromRect.top + fromRect.height / 2 - canvasRect.top;
+                const x2 = toRect.left + toRect.width / 2 - canvasRect.left;
+                const y2 = toRect.top + toRect.height / 2 - canvasRect.top;
+
+                const path = document.createElementNS('http://www.w3.org/2000/svg', 'path');
+                path.classList.add('connection-path');
+                path.setAttribute('d', createBezierPath(x1, y1, x2, y2));
+                path.dataset.edgeId = edge.id;
+                path.style.pointerEvents = 'stroke';
+
+                // Add click handler for deletion
+                path.addEventListener('click', (e) => {
+                    e.stopPropagation();
+                    deleteConnection(edge.id);
+                });
+
+                svg.appendChild(path);
+            });
+        }
+
+        function deleteConnection(edgeId) {
+            pipeline.edges = pipeline.edges.filter(e => e.id !== edgeId);
+            renderConnections();
+            showMessage('Connection deleted', 'success');
+        }
+
+        // Initialize
+        document.addEventListener('mousemove', (e) => {
+            // Handle node dragging
+            if (draggedNode) {
+                const dx = e.clientX - draggedNode.startX;
+                const dy = e.clientY - draggedNode.startY;
+
+                const newX = Math.max(0, draggedNode.originalX + dx);
+                const newY = Math.max(0, draggedNode.originalY + dy);
+
+                draggedNode.element.style.left = newX + 'px';
+                draggedNode.element.style.top = newY + 'px';
+
+                // Update connections while dragging
+                renderConnections();
+            }
+
+            // Handle connection dragging
+            if (connectionDrag) {
+                updateConnectionDrag(e);
+            }
+        });
+
+        document.addEventListener('mouseup', (e) => {
+            // Handle node dragging end
+            if (draggedNode) {
+                const dx = e.clientX - draggedNode.startX;
+                const dy = e.clientY - draggedNode.startY;
+
+                const finalX = Math.max(0, draggedNode.originalX + dx);
+                const finalY = Math.max(0, draggedNode.originalY + dy);
+
+                draggedNode.node.position = { x: finalX, y: finalY };
+                draggedNode.element.style.zIndex = 'auto';
+                draggedNode.element.style.cursor = 'auto';
+                draggedNode = null;
+
+                // Update connections after node movement
+                renderConnections();
+            }
+
+            // Handle connection dragging end
+            if (connectionDrag) {
+                endConnection(e);
+            }
+        });
+
+        function toggleBlockwiseBar() {
+            const blockwiseBar = document.getElementById('blockwise-bar');
+            const isHidden = blockwiseBar.style.display === 'none';
+            blockwiseBar.style.display = isHidden ? 'flex' : 'none';
+        }
+
+        initializeLibrary();
+
+        // Track if blockwise config has been loaded
+        let blockwiseConfigLoaded = false;
+
+        // Load blockwise configuration from globals on page load
+        async function loadBlockwiseConfig() {
+            if (blockwiseConfigLoaded) return;
+            blockwiseConfigLoaded = true;
+            
+            try {
+                const response = await fetch('/api/blockwise-config');
+                const config = await response.json();
+                
+                // Update existing blockwise config nodes with fetched values from globals
+                if (pipeline.blockwise_config.length > 0) {
+                    pipeline.blockwise_config.forEach(node => {
+                        if (!node.params) {
+                            node.params = {};
+                        }
+                        node.params.queue = config.queue;
+                        node.params.charge_group = config.charge_group;
+                        node.params.nb_cores_master = config.nb_cores_master;
+                        node.params.nb_cores_worker = config.nb_cores_worker;
+                        node.params.nb_workers = config.nb_workers;
+                        node.params.tmp_dir = config.tmp_dir;
+                        node.params.blockwise_tasks_dir = config.blockwise_tasks_dir;
+                    });
+                    renderCanvas();
+                    canvas.appendChild(svg);
+                    
+                }
+            } catch (error) {
+                console.error('Error loading blockwise config:', error);
+            }
+        }
+        
+        loadBlockwiseConfig();
+
+        // Blockwise workflow state tracking
+        let blockwiseState = {
+            validated: false,
+            generated: false,
+            prechecked: false,
+            model_mode: ""  // Stores the selected model merge mode
+        };
+
+        // Initialize Model Merger dropdown
+        function initializeModelMergerDropdown() {
+            const dropdown = document.getElementById('model-merge-mode');
+            const mergers = window.APP_CONFIG.availableModelMergers || [];
+            
+            // Clear existing options except the first one
+            while (dropdown.options.length > 1) {
+                dropdown.remove(1);
+            }
+            
+            // Add all available mergers
+            mergers.forEach(merger => {
+                const option = document.createElement('option');
+                option.value = merger.class_name;  // e.g., "AndModelMerger"
+                option.textContent = `${merger.name} - ${merger.description}`;
+                dropdown.appendChild(option);
+            });
+            
+            // Add change listener to store selection in blockwiseState
+            dropdown.addEventListener('change', (e) => {
+                blockwiseState.model_mode = e.target.value;
+                console.log('Selected model merge mode:', blockwiseState.model_mode);
+            });
+        }
+        
+        // Initialize on page load
+        initializeModelMergerDropdown();
+
+        // Blockwise Operations
+        async function validateBlockwise() {
+            const status = document.getElementById('blockwise-status');
+            status.textContent = 'Validating...';
+            
+            try {
+                // Add model_mode to pipeline
+                const pipelineWithConfig = { 
+                    ...pipeline,
+                    model_mode: blockwiseState.model_mode
+                };
+                
+                const response = await fetch('/api/blockwise/validate', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ pipeline: pipelineWithConfig })
+                });
+                
+                const data = await response.json();
+                if (data.valid) {
+                    showMessage('✓ Pipeline valid for blockwise processing', 'success');
+                    status.textContent = '✓ Step 1: Validated';
+                    status.style.color = 'var(--accent-green)';
+                    blockwiseState.validated = true;
+                    blockwiseState.generated = false;
+                    blockwiseState.prechecked = false;
+                    // Enable generate button
+                    document.getElementById('btn-generate').disabled = false;
+                    // Disable precheck and submit
+                    document.getElementById('btn-precheck').disabled = true;
+                    document.getElementById('btn-submit').disabled = true;
+                } else {
+                    showMessage('✗ ' + data.error, 'error');
+                    status.textContent = '✗ Validation failed';
+                    status.style.color = 'var(--accent-red)';
+                    blockwiseState.validated = false;
+                    // Disable all other buttons
+                    document.getElementById('btn-generate').disabled = true;
+                    document.getElementById('btn-precheck').disabled = true;
+                    document.getElementById('btn-submit').disabled = true;
+                }
+            } catch (error) {
+                showMessage('Error validating: ' + error.message, 'error');
+                status.textContent = '✗ Error';
+                blockwiseState.validated = false;
+                // Disable all other buttons
+                document.getElementById('btn-generate').disabled = true;
+                document.getElementById('btn-precheck').disabled = true;
+                document.getElementById('btn-submit').disabled = true;
+            }
+        }
+
+        async function generateBlockwiseTask() {
+            if (!blockwiseState.validated) {
+                showMessage('⚠️ Please validate first', 'info');
+                return;
+            }
+            const status = document.getElementById('blockwise-status');
+            status.textContent = 'Generating...';
+            
+            try {
+                // Add model_mode to pipeline if multiple models are present
+                const pipelineWithConfig = { 
+                    ...pipeline,
+                    model_mode: blockwiseState.model_mode  // Include the selected merge mode
+                };
+                
+                const response = await fetch('/api/blockwise/generate', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ pipeline: pipelineWithConfig })
+                });
+                
+                const data = await response.json();
+                if (data.success) {
+                    showMessage(`✓ Task generated: ${data.task_path}`, 'success');
+                    status.textContent = `✓ Step 2: Generated`;
+                    status.style.color = 'var(--accent-green)';
+                    blockwiseState.generated = true;
+                    blockwiseState.prechecked = false;
+                    // Enable precheck
+                    document.getElementById('btn-precheck').disabled = false;
+                    // Disable submit until prechecked
+                    document.getElementById('btn-submit').disabled = true;
+                    console.log('Task path:', data.task_path);
+                    console.log('Task YAML:', data.task_yaml);
+                } else {
+                    showMessage('✗ ' + data.error, 'error');
+                    status.textContent = '✗ Generation failed';
+                    status.style.color = 'var(--accent-red)';
+                    blockwiseState.generated = false;
+                    // Disable precheck and submit
+                    document.getElementById('btn-precheck').disabled = true;
+                    document.getElementById('btn-submit').disabled = true;
+                }
+            } catch (error) {
+                showMessage('Error generating task: ' + error.message, 'error');
+                status.textContent = '✗ Error';
+                blockwiseState.generated = false;
+                // Disable precheck and submit
+                document.getElementById('btn-precheck').disabled = true;
+                document.getElementById('btn-submit').disabled = true;
+            }
+        }
+
+        async function precheckBlockwiseTask() {
+            if (!blockwiseState.generated) {
+                showMessage('⚠️ Please generate task first', 'info');
+                return;
+            }
+            const status = document.getElementById('blockwise-status');
+            status.textContent = 'Prechecking...';
+            
+            try {
+                // Add model_mode to pipeline
+                const pipelineWithConfig = { 
+                    ...pipeline,
+                    model_mode: blockwiseState.model_mode
+                };
+                
+                const response = await fetch('/api/blockwise/precheck', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ pipeline: pipelineWithConfig })
+                });
+                
+                const data = await response.json();
+                if (data.success) {
+                    showMessage(`✓ Precheck passed: ${data.message}`, 'success');
+                    status.textContent = `✓ Step 3: Prechecked`;
+                    status.style.color = 'var(--accent-green)';
+                    blockwiseState.prechecked = true;
+                    // Enable submit
+                    document.getElementById('btn-submit').disabled = false;
+                    console.log('Precheck result:', data.message);
+                } else {
+                    showMessage(`✗ Precheck failed: ${data.error}`, 'error');
+                    status.textContent = '✗ Precheck failed';
+                    status.style.color = 'var(--accent-red)';
+                    blockwiseState.prechecked = false;
+                    // Disable submit
+                    document.getElementById('btn-submit').disabled = true;
+                    console.error('Precheck error:', data.error);
+                }
+            } catch (error) {
+                showMessage('Error prechecking task: ' + error.message, 'error');
+                status.textContent = '✗ Error';
+                blockwiseState.prechecked = false;
+                // Disable submit
+                document.getElementById('btn-submit').disabled = true;
+            }
+        }
+
+        async function submitBlockwiseTask() {
+            if (!blockwiseState.prechecked) {
+                showMessage('⚠️ Please precheck first', 'info');
+                return;
+            }
+            const status = document.getElementById('blockwise-status');
+            status.textContent = 'Submitting...';
+            
+            const jobName = prompt('Enter job name:', 'cellmap_flow_' + Date.now());
+            if (!jobName) {
+                status.textContent = '';
+                return;
+            }
+            
+            try {
+                // Add model_mode to pipeline
+                const pipelineWithConfig = { 
+                    ...pipeline,
+                    model_mode: blockwiseState.model_mode
+                };
+                
+                const response = await fetch('/api/blockwise/submit', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({ pipeline: pipelineWithConfig, job_name: jobName })
+                });
+                
+                const data = await response.json();
+                if (data.success) {
+                    showMessage('✓ Task submitted: ' + data.job_id, 'success');
+                    status.textContent = '✓ Submitted - Job: ' + data.job_id;
+                    status.style.color = 'var(--accent-green)';
+                } else {
+                    showMessage('✗ ' + data.error, 'error');
+                    status.textContent = '✗ Submission failed';
+                    status.style.color = 'var(--accent-red)';
+                }
+            } catch (error) {
+                showMessage('Error submitting task: ' + error.message, 'error');
+                status.textContent = '✗ Error';
+            }
+        }
+    </script>
+</body>
+</html>
diff --git a/cellmap_flow/globals.py b/cellmap_flow/globals.py
index b8186af..4ede2e4 100644
--- a/cellmap_flow/globals.py
+++ b/cellmap_flow/globals.py
@@ -1,25 +1,51 @@
-from cellmap_flow.norm.input_normalize import MinMaxNormalizer
-from cellmap_flow.post.postprocessors import DefaultPostprocessor
+from cellmap_flow.norm.input_normalize import MinMaxNormalizer, LambdaNormalizer
+from cellmap_flow.post.postprocessors import DefaultPostprocessor, ThresholdPostprocessor
 
 import os
 import yaml
 import threading
 import numpy as np
 import logging
+from typing import Any, List, Optional
 
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO)
 
-# input_norms = [MinMaxNormalizer()]
-# postprocess = [DefaultPostprocessor(0,200,0,1)]
+input_norms = [MinMaxNormalizer(), LambdaNormalizer("x*2-1")]
+postprocess = [DefaultPostprocessor(), ThresholdPostprocessor(threshold=0.5)]
 
-input_norms = []
-postprocess = []
+# input_norms = []
+# postprocess = []
 viewer = None
 
 
 class Flow:
-    _instance = None
+    _instance: Optional["Flow"] = None
+    
+    # Class-level type annotations for all instance attributes
+    jobs: List[Any]
+    models_config: List[Any]
+    servers: List[Any]
+    raw: Optional[Any]
+    input_norms: List[Any]
+    postprocess: List[Any]
+    viewer: Optional[Any]
+    dataset_path: Optional[str]
+    model_catalog: dict
+    queue: str
+    charge_group: str
+    nb_cores_master: int
+    nb_cores_worker: int
+    nb_workers: int
+    tmp_dir: Optional[str]
+    blockwise_tasks_dir: Optional[str]
+    neuroglancer_thread: Optional[Any]
+    pipeline_inputs: List[Any]
+    pipeline_outputs: List[Any]
+    pipeline_edges: List[Any]
+    pipeline_normalizers: List[Any]
+    pipeline_models: List[Any]
+    pipeline_postprocessors: List[Any]
 
     def __new__(cls):
         if cls._instance is None:
@@ -28,11 +54,11 @@ def __new__(cls):
             cls._instance.models_config = []
             cls._instance.servers = []
             cls._instance.raw = None
-            cls._instance.input_norms = []  # or [MinMaxNormalizer(0, 255)]
-            cls._instance.postprocess = []
+            cls._instance.input_norms = input_norms
+            cls._instance.postprocess = postprocess
             cls._instance.viewer = None
             cls._instance.dataset_path = None
-            # cls._instance.model_catalog = {}
+            cls._instance.model_catalog = {}
             # Uncomment and adjust if you want to load the model catalog:
             models_path = os.path.normpath(
                 os.path.join(
@@ -44,7 +70,20 @@ def __new__(cls):
 
             cls._instance.queue = "gpu_h100"
             cls._instance.charge_group = "cellmap"
+            cls._instance.nb_cores_master = 4
+            cls._instance.nb_cores_worker = 12
+            cls._instance.nb_workers = 14
+            cls._instance.tmp_dir = os.path.expanduser("~/.cellmap_flow/blockwise_tmp")
+            cls._instance.blockwise_tasks_dir = os.path.expanduser("~/.cellmap_flow/blockwise_tasks")
             cls._instance.neuroglancer_thread = None
+
+            # Pipeline visual state storage
+            cls._instance.pipeline_inputs = []
+            cls._instance.pipeline_outputs = []
+            cls._instance.pipeline_edges = []
+            cls._instance.pipeline_normalizers = []
+            cls._instance.pipeline_models = []
+            cls._instance.pipeline_postprocessors = []
         return cls._instance
 
     def to_dict(self):
diff --git a/cellmap_flow/models/model_merger.py b/cellmap_flow/models/model_merger.py
new file mode 100644
index 0000000..8bb020c
--- /dev/null
+++ b/cellmap_flow/models/model_merger.py
@@ -0,0 +1,160 @@
+"""
+Model Merger classes for combining outputs from multiple models.
+
+Similar to PostProcessor pattern, provides subclasses for different merge strategies
+that can be looked up by their __name__ attribute.
+"""
+
+import logging
+import numpy as np
+import inspect
+
+logger = logging.getLogger(__name__)
+
+
+class ModelMerger:
+    """Base class for model merging strategies."""
+
+    def merge(self, model_outputs):
+        """
+        Merge outputs from multiple models.
+
+        Args:
+            model_outputs: List of numpy arrays from different models
+
+        Returns:
+            Merged numpy array
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} must implement merge() method"
+        )
+
+
+class AndModelMerger(ModelMerger):
+    """
+    AND merge strategy: Element-wise minimum.
+    For binary data: logical AND. For continuous data: minimum value.
+    """
+
+    def merge(self, model_outputs):
+        """
+        Apply AND operation: element-wise minimum across all model outputs.
+
+        Args:
+            model_outputs: List of numpy arrays
+
+        Returns:
+            Merged array with minimum values at each position
+        """
+        merged = model_outputs[0].copy()
+        for output in model_outputs[1:]:
+            merged = np.minimum(merged, output)
+        return merged
+
+
+class OrModelMerger(ModelMerger):
+    """
+    OR merge strategy: Element-wise maximum.
+    For binary data: logical OR. For continuous data: maximum value.
+    """
+
+    def merge(self, model_outputs):
+        """
+        Apply OR operation: element-wise maximum across all model outputs.
+
+        Args:
+            model_outputs: List of numpy arrays
+
+        Returns:
+            Merged array with maximum values at each position
+        """
+        merged = model_outputs[0].copy()
+        for output in model_outputs[1:]:
+            merged = np.maximum(merged, output)
+        return merged
+
+
+class SumModelMerger(ModelMerger):
+    """
+    SUM merge strategy: Average of all outputs.
+    Computes the mean across all model outputs.
+    """
+
+    def merge(self, model_outputs):
+        """
+        Apply SUM operation: average all model outputs.
+
+        Args:
+            model_outputs: List of numpy arrays
+
+        Returns:
+            Merged array with average values at each position
+        """
+        merged = np.sum(model_outputs, axis=0) / len(model_outputs)
+        return merged
+
+
+def get_model_mergers_list() -> list[dict]:
+    """
+    Returns a list of dictionaries containing information about all ModelMerger subclasses.
+
+    Returns:
+        List of dicts with keys: 'class_name', 'name', 'description'
+    """
+    merger_classes = ModelMerger.__subclasses__()
+    mergers = []
+    for merger_cls in merger_classes:
+        merger_name = merger_cls.__name__
+        # Extract description from docstring
+        description = (
+            merger_cls.__doc__.strip().split("\n")[0]
+            if merger_cls.__doc__
+            else "No description available"
+        )
+        mergers.append(
+            {
+                "class_name": merger_name,
+                "name": merger_name.replace("ModelMerger", ""),  # e.g., "And"
+                "description": description,
+            }
+        )
+    return mergers
+
+
+def get_model_merger(merger_name: str) -> ModelMerger:
+    """
+    Get a ModelMerger instance by name.
+
+    Args:
+        merger_name: Name of the merger class (e.g., 'AndModelMerger', 'And', 'AND')
+
+    Returns:
+        Instance of the requested ModelMerger subclass
+
+    Raises:
+        ValueError: If merger_name doesn't match any available merger class
+    """
+    merger_classes = ModelMerger.__subclasses__()
+
+    # Normalize the input name for comparison
+    normalized_name = merger_name.upper().strip()
+
+    for merger_cls in merger_classes:
+        class_name = merger_cls.__name__
+        short_name = class_name.replace("ModelMerger", "").upper()
+
+        if normalized_name in [class_name.upper(), short_name]:
+            return merger_cls()
+
+    available = [cls.__name__ for cls in merger_classes]
+    raise ValueError(
+        f"Unknown merger: {merger_name}. Available mergers: {available}"
+    )
+
+
+# Convenience mapping for common merge mode strings
+MERGE_MODE_MAP = {
+    "AND": "AndModelMerger",
+    "OR": "OrModelMerger",
+    "SUM": "SumModelMerger",
+}
diff --git a/cellmap_flow/models/models_config.py b/cellmap_flow/models/models_config.py
index 7bf1713..ccd2c59 100644
--- a/cellmap_flow/models/models_config.py
+++ b/cellmap_flow/models/models_config.py
@@ -73,6 +73,15 @@ def output_dtype(self):
         )
         return np.float32
 
+    def to_dict(self):
+        """
+        Export model configuration as a dict that can be used with build_model_from_entry.
+
+        Returns:
+            Dictionary containing model type and all init parameters.
+        """
+        raise NotImplementedError("Subclasses must implement to_dict()")
+
 
 class ScriptModelConfig(ModelConfig):
 
@@ -104,6 +113,15 @@ def _get_config(self):
             )
         return config
 
+    def to_dict(self):
+        """Export configuration for use with build_model_from_entry."""
+        result = {"type": "script", "script_path": self.script_path}
+        if self.name is not None:
+            result["name"] = self.name
+        if self.scale is not None:
+            result["scale"] = self.scale
+        return result
+
 
 class DaCapoModelConfig(ModelConfig):
 
@@ -165,6 +183,17 @@ def _get_channels(task):
             return ["x", "y", "z"]
         return ["membrane"]
 
+    def to_dict(self):
+        """Export configuration for use with build_model_from_entry."""
+        result = {
+            "type": "dacapo",
+            "run_name": self.run_name,
+            "iteration": self.iteration,
+        }
+        if self.name is not None:
+            result["name"] = self.name
+        return result
+
 
 class FlyModelConfig(ModelConfig):
 
@@ -240,6 +269,23 @@ def _get_config(self):
         config.axes_names = ["x", "y", "z", "c^"]
         return config
 
+    def to_dict(self):
+        """Export configuration for use with build_model_from_entry."""
+        result = {
+            "type": "fly",
+            "checkpoint_path": self.checkpoint_path,
+            "channels": self.channels,
+            "input_voxel_size": list(self.input_voxel_size),
+            "output_voxel_size": list(self.output_voxel_size),
+        }
+        if self.name is not None:
+            result["name"] = self.name
+        if self.input_size is not None:
+            result["input_size"] = list(self.input_size)
+        if self.output_size is not None:
+            result["output_size"] = list(self.output_size)
+        return result
+
 
 class BioModelConfig(ModelConfig):
     def __init__(
@@ -393,6 +439,21 @@ def get_input_slicer(self, input_axes):
             for a in input_axes
         )
 
+    def to_dict(self):
+        """Export configuration for use with build_model_from_entry."""
+        result = {
+            "type": "bioimage",
+            "model_name": self.model_name,
+            "voxel_size": list(self.voxel_size) if hasattr(self.voxel_size, '__iter__') else self.voxel_size,
+        }
+        if self.name is not None:
+            result["name"] = self.name
+        if self.voxels_to_process is not None:
+            # Reconstruct edge_length_to_process from voxels_to_process
+            edge_length = round(self.voxels_to_process ** (1/3))
+            result["edge_length_to_process"] = edge_length
+        return result
+
 
 def concat_along_c(arrs, axes_list, channel_axis_name="c"):
     """Concatenate arrays along the channel axis, adding channel dim if missing."""
@@ -518,3 +579,15 @@ def _get_config(self) -> Config:
         config.model.to(_get_device())
         config.model.eval()
         return config
+
+    def to_dict(self):
+        """Export configuration for use with build_model_from_entry."""
+        result = {
+            "type": "cellmap-model",
+            "folder_path": self.cellmap_model.folder_path,
+        }
+        if self.name is not None:
+            result["name"] = self.name
+        if self.scale is not None:
+            result["scale"] = self.scale
+        return result
diff --git a/cellmap_flow/utils/bsub_utils.py b/cellmap_flow/utils/bsub_utils.py
index 3d19f15..796e3ee 100644
--- a/cellmap_flow/utils/bsub_utils.py
+++ b/cellmap_flow/utils/bsub_utils.py
@@ -520,7 +520,7 @@ def start_hosts(
                 command,
                 queue,
                 charge_group,
-                job_name=f"{job_name}_server"
+                job_name=f"{job_name}"
             )
             
             if wait_for_host:
diff --git a/cellmap_flow/utils/config_utils.py b/cellmap_flow/utils/config_utils.py
index 2e71442..b83ee4e 100644
--- a/cellmap_flow/utils/config_utils.py
+++ b/cellmap_flow/utils/config_utils.py
@@ -69,7 +69,7 @@ def load_config(path: str) -> Dict[str, Any]:
         if not config["models"]:
             logger.error("YAML 'models' list is empty")
             sys.exit(1)
-        logger.warning("Using deprecated list format for models. Consider using dict format with model names as keys.")
+        # logger.warning("Using deprecated list format for models. Consider using dict format with model names as keys.")
     else:
         logger.error("YAML 'models' must be either a dict or list")
         sys.exit(1)
@@ -220,6 +220,15 @@ def build_models(model_entries: Dict[str, Dict[str, Any]]) -> List[ModelConfig]:
     """
     models = []
     
+    if isinstance(model_entries, list):
+        entries = {}
+        for entry in model_entries:
+            if "name" not in entry:
+                raise ValueError("Each model entry in the list must have a 'name' field.")      
+            entries[entry["name"]] = entry
+        model_entries = entries
+
+    
     for model_name, entry in model_entries.items():
         model = build_model_from_entry(entry, model_name=model_name)
         models.append(model)
diff --git a/cellmap_flow/utils/ds.py b/cellmap_flow/utils/ds.py
index 19389b0..bf8dc30 100644
--- a/cellmap_flow/utils/ds.py
+++ b/cellmap_flow/utils/ds.py
@@ -45,8 +45,8 @@ def generate_singlescale_metadata(
     z_attrs["multiscales"][0]["datasets"] = [
         {
             "coordinateTransformations": [
-                {"scale": voxel_size, "type": "scale"},
-                {"translation": translation, "type": "translation"},
+                {"scale": list(voxel_size), "type": "scale"},
+                {"translation": list(translation), "type": "translation"},
             ],
             "path": arr_name,
         }
diff --git a/cellmap_flow/utils/generate_neuroglancer.py b/cellmap_flow/utils/generate_neuroglancer.py
new file mode 100644
index 0000000..fbd621a
--- /dev/null
+++ b/cellmap_flow/utils/generate_neuroglancer.py
@@ -0,0 +1,39 @@
+import os
+import zarr
+import yaml
+import neuroglancer
+from cellmap_flow.utils.scale_pyramid import get_raw_layer
+
+neuroglancer.set_server_bind_address("0.0.0.0")
+
+def generate_neuroglancer(yaml_path):
+    with open(yaml_path, "r") as f:
+        config = yaml.safe_load(f)
+    predictions = [f for f in os.listdir(config["output_path"]) if os.path.isdir(os.path.join(config["output_path"], f))]
+    output_path = os.path.join(config["output_path"])
+    raw_path = config["data_path"]
+
+    viewer = neuroglancer.Viewer()
+    with viewer.txn() as s:
+        s.layers["raw"] = get_raw_layer(raw_path)
+        for pred in predictions:
+            s.layers[pred] = get_raw_layer(os.path.join(output_path, pred))
+
+    viewer_url = str(viewer)
+    print("viewer", viewer_url)
+    input("Press Enter to continue...")
+    return viewer_url
+
+
+def fix_all(yaml_path):
+    with open(yaml_path, "r") as f:
+        config = yaml.safe_load(f)
+    predictions = [os.path.join(config["output_path"], f) for f in os.listdir(config["output_path"]) if os.path.isdir(os.path.join(config["output_path"], f))]
+    for pred_path in predictions:
+        c_path = pred_path
+        while ".zarr" in c_path:
+            print("Fixing ", c_path)
+            zarr.open(c_path, mode="a") 
+            c_path = os.path.dirname(c_path)
+
+    
\ No newline at end of file
diff --git a/check_app.sh b/check_app.sh
new file mode 100644
index 0000000..8bbdfff
--- /dev/null
+++ b/check_app.sh
@@ -0,0 +1,2 @@
+python -m cellmap_flow.dashboard.app
+# conda activate flow_v2 && python -m cellmap_flow.dashboard.app 2>&1 | head -30 &
\ No newline at end of file
diff --git a/example/example_pipeline.yaml b/example/example_pipeline.yaml
new file mode 100644
index 0000000..8dc8434
--- /dev/null
+++ b/example/example_pipeline.yaml
@@ -0,0 +1,26 @@
+# Example CellMapFlow Pipeline
+# This demonstrates a typical normalization -> inference -> postprocessing workflow
+
+input_normalizers:
+  - name: StandardNormalizer
+    params:
+      mean: 0.5
+      std: 0.1
+  
+  - name: MinMaxNormalizer
+    params:
+      min_val: 0.0
+      max_val: 255.0
+
+postprocessors:
+  - name: DefaultPostprocessor
+    params:
+      clip_min: -1.0
+      clip_max: 1.0
+      bias: 1.0
+      multiplier: 127.5
+  
+  - name: InstanceSegmentation
+    params:
+      threshold: 0.5
+      min_size: 100
diff --git a/example/model_spec.py b/example/model_spec.py
index 0a1633a..1078e99 100644
--- a/example/model_spec.py
+++ b/example/model_spec.py
@@ -25,6 +25,7 @@ def load_eval_model(num_labels, checkpoint_path):
     model.eval()
     return model
 
+# classes = ["mito","er","nuc"," pm"," ves","ld"]
 CHECKPOINT_PATH = "/nrs/saalfeld/heinrichl/fly_organelles/run08/model_checkpoint_438000"
 output_channels = 8  # 0:all_mem,1:organelle,2:mito,3:er,4:nucleus,5:pm,6:vs,7:ld
 model = load_eval_model(output_channels, CHECKPOINT_PATH)
diff --git a/example/model_spec_run07.py b/example/model_spec_run07.py
index 756d809..2728289 100644
--- a/example/model_spec_run07.py
+++ b/example/model_spec_run07.py
@@ -25,8 +25,7 @@ def load_eval_model(num_labels, checkpoint_path):
     model.eval()
     return model
 
-
-classes = ["mito","er","nuc"," pm"," ves","ld"]
+classes = ["all_mem","organelle","mito","er","nuc","pm","vesicle","ld"]
 CHECKPOINT_PATH = "/nrs/saalfeld/heinrichl/fly_organelles/run07/model_checkpoint_432000"
 # output_channels = len(classes) 
 output_channels = 8
diff --git a/example/model_spec_run07_7hk.py b/example/model_spec_run07_7hk.py
index 6e29478..8404b77 100644
--- a/example/model_spec_run07_7hk.py
+++ b/example/model_spec_run07_7hk.py
@@ -26,7 +26,7 @@ def load_eval_model(num_labels, checkpoint_path):
     return model
 
 
-classes = ["mito","er","nuc"," pm"," ves","ld"]
+classes = ["all_mem","organelle","mito","er","nuc","pm","vesicle","ld"]
 CHECKPOINT_PATH = "/nrs/saalfeld/heinrichl/fly_organelles/run07/model_checkpoint_700000"
 # output_channels = len(classes) 
 output_channels = 8
diff --git a/jrc_mus-salivary-1_mito.yaml b/jrc_mus-salivary-1_mito.yaml
new file mode 100644
index 0000000..5c7c1c8
--- /dev/null
+++ b/jrc_mus-salivary-1_mito.yaml
@@ -0,0 +1,32 @@
+data_path: /nrs/cellmap/data/jrc_mus-salivary-1/jrc_mus-salivary-1.zarr/recon-1/em/fibsem-uint8
+queue: gpu_h100
+charge_group: cellmap
+json_data:
+  input_norm:
+    MinMaxNormalizer:
+      min_value: 0
+      max_value: 250
+      invert: false
+    LambdaNormalizer:
+      expression: x*2-1
+  postprocess:
+    DefaultPostprocessor:
+      clip_min: 0
+      clip_max: 1.0
+      bias: 0.0
+      multiplier: 127.5
+    ThresholdPostprocessor:
+      threshold: 127.5
+models:
+  model_tmp1:
+    type: fly
+    checkpoint: /groups/cellmap/cellmap/zouinkhim/exp_c-elegen/v3/train/runs/20250806_mito_mouse_distance_16nm/model_checkpoint_362000
+    resolution: 16
+    classes:
+    - mito
+  # mito_mouse_16_362000:
+  #   type: fly
+  #   checkpoint: /groups/cellmap/cellmap/zouinkhim/exp_c-elegen/v3/train/runs/20250806_mito_mouse_distance_16nm/model_checkpoint_362000
+  #   resolution: 16
+  #   classes:
+  #   - mito
\ No newline at end of file
diff --git a/neuroglancer_utils.py b/neuroglancer_utils.py
index 9d27822..ad9e052 100644
--- a/neuroglancer_utils.py
+++ b/neuroglancer_utils.py
@@ -70,7 +70,7 @@ def generate_neuroglancer_url(dataset_path, extras=[]):
             host = job.host
             color = next(color_cycle)
             s.layers[model] = neuroglancer.ImageLayer(
-                source=f"n5://{host}/{model}{ARGS_KEY}{st_data}{ARGS_KEY}",
+                source=f"zarr://{host}/{model}{ARGS_KEY}{st_data}{ARGS_KEY}",
                 shader="""#uicontrol int rChan slider(min=0, max=7, step=1, default=0);
                         #uicontrol int gChan slider(min=0, max=7, step=1, default=1);
                         #uicontrol int bChan slider(min=0, max=7, step=1, default=2);
diff --git a/pyproject.toml b/pyproject.toml
index 3a8539d..b60b817 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,6 +27,7 @@ dependencies = [
     "numcodecs==0.15.0",
     "zarr==2.18.4",
     "xarray==2024.7.0",
+    # "xarray==2023.12.0",
     "h5py",
     "s3fs",
     "torch",    
@@ -36,6 +37,7 @@ dependencies = [
     "neuroglancer",
     "pydantic",
     "funlib.persistence @ git+https://github.com/funkelab/funlib.persistence.git",
+    "daisy"
     ]
 
 # extras
@@ -88,4 +90,5 @@ pytest = "^8.4.1"
 cellmap_flow = "cellmap_flow.cli.cli:main"
 cellmap_flow_server = "cellmap_flow.cli.server_cli:cli"
 cellmap_flow_yaml = "cellmap_flow.cli.yaml_cli:main"
-cellmap_flow_blockwise = "cellmap_flow.blockwise.cli:cli"
\ No newline at end of file
+cellmap_flow_blockwise = "cellmap_flow.blockwise.cli:cli"
+cellmap_flow_app = "cellmap_flow.dashboard.app"
\ No newline at end of file