remove cloud mode ui

README-PYPI.md

@@ -8,12 +8,10 @@ DP Wizard demonstrates how to calculate DP statistics or create a synthetic data
 
 (If differential privacy is new to you, [these slides](https://opendp.github.io/dp-wizard/) provide some background, and explain how DP Wizard works.)
 
-You can run DP Wizard locally and upload your own CSV,
-or use the [cloud deployment](https://mccalluc-dp-wizard.share.connect.posit.cloud/) and only provide column names to protect your private data.
-In either case, you'll be prompted to describe your privacy budget and the analysis you need.
-With that information, DP Wizard provides:
-
-- A Jupyter notebook which demonstrates how to use the [OpenDP Library](https://docs.opendp.org/).
+After selecting a local CSV, you'll be prompted to describe the analysis you need.
+Output options include:
+- A Jupyter notebook which demonstrates how to use
+the [OpenDP Library](https://docs.opendp.org/).
 - A plain Python script.
 - Text and CSV reports.
 
@@ -39,17 +37,16 @@ The exact upgrade process will depend on your environment and operating system.
 Install with `pip install 'dp_wizard[app]'` and you can start DP Wizard from the command line.
 
 ```
-usage: dp-wizard [-h] [--sample | --cloud]
+usage: dp-wizard [-h] [--sample]
 
 DP Wizard makes it easier to get started with Differential Privacy.
 
 options:
   -h, --help  show this help message and exit
   --sample    Generate a sample CSV: See how DP Wizard works without providing
               your own data
-  --cloud     Prompt for column names instead of CSV upload
 
-Unless you have set "--sample" or "--cloud", you will specify a CSV
+Unless you have set "--sample", you will specify a CSV
 inside the application.
 
 Provide a "Private CSV" if you only have a private data set, and want to

README.md

@@ -8,12 +8,10 @@ DP Wizard demonstrates how to calculate DP statistics or create a synthetic data
 
 (If differential privacy is new to you, [these slides](https://opendp.github.io/dp-wizard/) provide some background, and explain how DP Wizard works.)
 
-You can run DP Wizard locally and upload your own CSV,
-or use the [cloud deployment](https://mccalluc-dp-wizard.share.connect.posit.cloud/) and only provide column names to protect your private data.
-In either case, you'll be prompted to describe your privacy budget and the analysis you need.
-With that information, DP Wizard provides:
-
-- A Jupyter notebook which demonstrates how to use the [OpenDP Library](https://docs.opendp.org/).
+After selecting a local CSV, you'll be prompted to describe the analysis you need.
+Output options include:
+- A Jupyter notebook which demonstrates how to use
+the [OpenDP Library](https://docs.opendp.org/).
 - A plain Python script.
 - Text and CSV reports.
 
@@ -39,17 +37,16 @@ The exact upgrade process will depend on your environment and operating system.
 Install with `pip install 'dp_wizard[app]'` and you can start DP Wizard from the command line.
 
 ```
-usage: dp-wizard [-h] [--sample | --cloud]
+usage: dp-wizard [-h] [--sample]
 
 DP Wizard makes it easier to get started with Differential Privacy.
 
 options:
   -h, --help  show this help message and exit
   --sample    Generate a sample CSV: See how DP Wizard works without providing
               your own data
-  --cloud     Prompt for column names instead of CSV upload
 
-Unless you have set "--sample" or "--cloud", you will specify a CSV
+Unless you have set "--sample", you will specify a CSV
 inside the application.
 
 Provide a "Private CSV" if you only have a private data set, and want to

docs/index.html

@@ -645,7 +645,7 @@ <h1>Return to the class grades example ✋</h1>
 <tr>
 <td>
 <p><strong><a
-href="https://pypi.org/project/dp_wizard/"><code>pip install 'dp_wizard[app]'</code></a><br><code>dp_wizard --cloud</code><br><small>(requires
+href="https://pypi.org/project/dp_wizard/"><code>pip install 'dp_wizard[app]'</code></a><br><code>dp_wizard --sample</code><br><small>(requires
 Python&gt;=3.10)</small></strong></p>
 </td>
 <td>

docs/index.md

@@ -388,7 +388,7 @@ Divide into four teams, and on one computer either:
 <tr>
 <td>
 
-**[`pip install 'dp_wizard[app]'`](https://pypi.org/project/dp_wizard/)<br>`dp_wizard --cloud`<br><small>(requires Python>=3.10)</small>**
+**[`pip install 'dp_wizard[app]'`](https://pypi.org/project/dp_wizard/)<br>`dp_wizard --sample`<br><small>(requires Python>=3.10)</small>**
 
 </td>
 <td>

dp_wizard/shiny/__init__.py

@@ -111,7 +111,6 @@ def server(input: Inputs, output: Outputs, session: Session):  # pragma: no cove
         state = AppState(
             # CLI options:
             is_sample_csv=cli_info.is_sample_csv,
-            in_cloud=cli_info.is_cloud_mode,
             qa_mode=cli_info.is_qa_mode,
             # Reactive bools:
             is_tutorial_mode=reactive.value(cli_info.get_is_tutorial_mode()),

dp_wizard/shiny/components/summaries.py

@@ -19,8 +19,6 @@ def dataset_summary(state: AppState):  # pragma: no cover
         sources.append("Private CSV")
     if state.public_csv_path():
         sources.append("Public CSV")
-    if state.in_cloud:
-        sources.append("Field List")
 
     contributions = state.contributions()
     entity = state.contributions_entity()

dp_wizard/shiny/panels/analysis_panel/__init__.py

@@ -147,7 +147,6 @@ def analysis_server(
 ):  # pragma: no cover
     # CLI options:
     is_sample_csv = state.is_sample_csv
-    # in_cloud = state.in_cloud
 
     # Reactive bools:
     is_tutorial_mode = state.is_tutorial_mode

dp_wizard/shiny/panels/dataset_panel/__init__.py

@@ -118,7 +118,6 @@ def dataset_server(
 ):  # pragma: no cover
     # CLI options:
     is_sample_csv = state.is_sample_csv
-    in_cloud = state.in_cloud
 
     # Reactive bools:
     is_tutorial_mode = state.is_tutorial_mode
@@ -169,12 +168,6 @@ def _on_private_csv_path_change():
         private_csv_path.set(path)
         csv_info.set(CsvInfo(Path(path)))
 
-    @reactive.effect
-    @reactive.event(input.all_column_names)
-    def _on_column_names_change():
-        # Only used when the user is supplying column names in cloud mode.
-        csv_info.set(infer_csv_info(input.all_column_names()))
-
     @reactive.calc
     def csv_column_mismatch_calc() -> Optional[tuple[set, set]]:
         public = public_csv_path()
@@ -223,7 +216,6 @@ def welcome_ui():
     @render.ui
     def csv_or_columns_ui():
         return data_source.csv_or_columns_ui(
-            in_cloud=in_cloud,
             is_tutorial_mode=is_tutorial_mode,
             csv_info=csv_info,
         )
@@ -359,7 +351,7 @@ def set_is_dataset_selected():
             and not info.get_is_error()
             and len(info.get_all_column_names()) > 0
             and not get_row_count_errors(max_rows())
-            and (in_cloud or not csv_column_mismatch_calc())
+            and not csv_column_mismatch_calc()
         )
 
     @reactive.calc
@@ -388,26 +380,13 @@ def contributions_validation_ui():
 
     @render.ui
     def python_tutorial_ui():
-        cloud_extra_markdown = (
-            """
-            Because this instance of DP Wizard is running in the cloud,
-            we don't allow private data to be uploaded.
-            When run locally, DP Wizard can also run an analysis
-            on your data and return results,
-            and not just an unexecuted notebook.
-            """
-            if in_cloud
-            else ""
-        )
         return tutorial_box(
             is_tutorial_mode(),
-            f"""
+            """
             Along the way, code samples demonstrate
             how the information you provide is used in the
             OpenDP Library, and at the end you can download
             a notebook for the entire calculation.
-
-            {cloud_extra_markdown}
             """,
             responsive=False,
         )
@@ -465,7 +444,7 @@ def define_analysis_button_ui():
         return [
             button,
             f"""
-            Specify {'columns' if in_cloud else 'CSV'}, unit of privacy,
+            Specify CSV, unit of privacy,
             and maximum row count before proceeding.
             """,
         ]

dp_wizard/shiny/panels/dataset_panel/data_source.py

@@ -19,57 +19,21 @@
 
 
 def csv_or_columns_ui(
-    in_cloud: bool,
     is_tutorial_mode: reactive.Value[bool],
     csv_info: reactive.Value[CsvInfo],
 ):  # pragma: no cover
-    if in_cloud:
-        content = [
-            ui.markdown(
-                """
-                Provide the names of columns you'll use in your analysis,
-                one per line, with a sample value for each. For example:
-
-                ```
-                name: Chuck
-                age: 48
-                ```
-                """
-            ),
-            tutorial_box(
-                is_tutorial_mode(),
-                """
-                When [installed and run
-                locally](https://pypi.org/project/dp_wizard/),
-                DP Wizard allows you to specify a private and public CSV,
-                but for the safety of your data, in the cloud
-                DP Wizard only accepts column names.
-
-                If you don't have other ideas, we can imagine
-                a CSV of student quiz grades: Enter `student_id`,
-                `quiz_id`, `grade`, and `class_year_str` below,
-                each on a separate line.
-                """,
-                responsive=False,
-            ),
-            ui.input_text_area("all_column_names", "CSV Column Names", rows=5),
-        ]
-    else:
-        content = [
-            ui.markdown(
-                f"""
+    return [
+        ui.markdown(
+            f"""
 Choose **Private CSV** {PRIVATE_TEXT}
 
 Choose **Public CSV** {PUBLIC_TEXT}
 
 Choose both **Private CSV** and **Public CSV** {PUBLIC_PRIVATE_TEXT}
-                """
-            ),
-            ui.output_ui("input_files_ui"),
-            ui.output_ui("csv_message_ui"),
-        ]
-
-    content += [
+            """
+        ),
+        ui.output_ui("input_files_ui"),
+        ui.output_ui("csv_message_ui"),
         code_sample(
             "Context",
             Template(
@@ -98,7 +62,6 @@ def csv_or_columns_ui(
         ),
         ui.output_ui("python_tutorial_ui"),
     ]
-    return content
 
 
 def input_files_ui(

dp_wizard/shiny/panels/results_panel/__init__.py

@@ -99,7 +99,6 @@ def results_server(
 ):  # pragma: no cover
     # CLI options:
     # is_sample_csv = state.is_sample_csv
-    in_cloud = state.in_cloud
     qa_mode = state.qa_mode
 
     # Reactive bools:
@@ -209,66 +208,47 @@ def download_results_ui():
         ]
         if product() == Product.SYNTHETIC_DATA:
             downloads.append("Contingency Table")
-        return (
-            ui.markdown(
-                """
-                When [installed and run
-                locally](https://pypi.org/project/dp_wizard/),
-                there are more download options because DP Wizard
-                can read your private CSV and release differentially
-                private statistics.
+        return [
+            tutorial_box(
+                is_tutorial_mode(),
                 """
-            )
-            if in_cloud
-            else [
-                tutorial_box(
-                    is_tutorial_mode(),
-                    """
                     Now you can download a notebook for your analysis.
                     The Jupyter notebook could be used locally or on Colab,
                     but the HTML version can be viewed in the brower.
                     """,
-                    responsive=False,
-                ),
-                download_button(
-                    "Package",
-                    primary=True,
-                    disabled=disabled,
-                ),
-                ui.br(),
-                "Contains:",
-                ui.tags.ul(
-                    *[
-                        ui.tags.li(
-                            download_link(
-                                download,
-                                disabled=disabled,
-                            )
+                responsive=False,
+            ),
+            download_button(
+                "Package",
+                primary=True,
+                disabled=disabled,
+            ),
+            ui.br(),
+            "Contains:",
+            ui.tags.ul(
+                *[
+                    ui.tags.li(
+                        download_link(
+                            download,
+                            disabled=disabled,
                         )
-                        for download in downloads
-                    ]
-                ),
-            ]
-        )
+                    )
+                    for download in downloads
+                ]
+            ),
+        ]
 
     @render.ui
     def download_code_ui():
         disabled = not weights()
         return [
             tutorial_box(
                 is_tutorial_mode(),
-                (
-                    """
-                    In the cloud, DP Wizard only provides unexecuted
-                    notebooks and scripts.
-                    """
-                    if in_cloud
-                    else """
-                    Alternatively, you can download a script or unexecuted
-                    notebook that demonstrates the steps of your analysis,
-                    but does not contain any data or analysis results.
-                    """
-                ),
+                """
+                Alternatively, you can download a script or unexecuted
+                notebook that demonstrates the steps of your analysis,
+                but does not contain any data or analysis results.
+                """,
                 responsive=False,
             ),
             download_button("Notebook (unexecuted)", disabled=disabled),

dp_wizard/types.py

@@ -212,7 +212,6 @@ def get_is_error(self) -> bool:
 class AppState:
     # CLI options:
     is_sample_csv: bool
-    in_cloud: bool
     qa_mode: bool
 
     # Reactive bools: