With the analytics Agent

.gitignore

@@ -7,6 +7,7 @@ env/
 venv/
 ENV/
 .env
+*.env
 .venv
 *.log
 .DS_Store

app.py

@@ -338,6 +338,7 @@ def agent_chat():
         request_data = request.get_json()
         question = request_data.get('question', '')
         location_context = request_data.get('location_context', None)
+        time_frame = request_data.get('time_frame', None)
 
         if not question:
             return jsonify({
@@ -446,8 +447,8 @@ def agent_chat():
                 traceback.print_exc()
                 # Fall through to normal chat
 
-        # Normal chat flow
-        response = call_adk_agent(question)
+        # Normal chat flow - pass context to agent
+        response = call_adk_agent(question, location_context=location_context, time_frame=time_frame)
 
         return jsonify({
             'success': True,

docs/ANALYTICS_AGENT.md

@@ -0,0 +1,61 @@
+# Analytics Agent Documentation
+
+## Overview
+
+The **Analytics Agent** is a specialized sub-agent that performs cross-dataset analysis on air quality and infectious disease data using Python code execution. It can handle complex analytical queries that span multiple data sources.
+
+## Capabilities
+
+### Data Sources
+1. **Historical Air Quality** - EPA Historical Air Quality dataset (PM2.5, AQI)
+2. **Live Air Quality** - Real-time AirNow API data
+3. **Infectious Disease** - CDC BEAM disease surveillance data
+
+### Analysis Types
+- Cross-dataset correlations
+- Temporal pattern analysis
+- Geographic comparisons
+- Trend analysis and forecasting
+- Statistical modeling
+- Data visualization
+
+## Architecture
+
+### Tools
+The agent has access to three data retrieval tools:
+- `get_air_quality()` - Historical EPA data
+- `get_live_air_quality()` - Current air quality readings
+- `get_infectious_disease_data()` - CDC disease data
+
+### Code Execution
+Uses `VertexAiCodeExecutor` to:
+- Execute Python code in a stateful environment
+- Run data analysis and visualizations
+- Maintain state across multiple code executions
+
+## Example Queries
+
+### Cross-Dataset Analysis
+```
+"Analyze the correlation between air quality and respiratory diseases in California"
+```
+
+### Temporal Analysis
+```
+"Show me trends in air quality vs disease rates over the past year"
+```
+
+### Geographic Comparison
+```
+"Compare air quality and disease patterns across different states"
+```
+
+## Files Created
+
+1. `multi_tool_agent_bquery_tools/agents/analytics_agent.py` - Main agent definition
+2. `multi_tool_agent_bquery_tools/agents/analytics_prompts.py` - Instruction prompts
+3. `multi_tool_agent_bquery_tools/agent.py` - Updated to include analytics agent
+
+## Integration
+
+The analytics agent is automatically integrated into the root agent's sub-agents list and will be routed to when users ask analytical questions spanning multiple datasets.

multi_tool_agent_bquery_tools/agent.py

@@ -1,6 +1,8 @@
 # ./agent.py
+# -*- coding: utf-8 -*-
 import os
 import asyncio
+from datetime import datetime
 from google.adk.agents import Agent
 from google.adk.runners import Runner
 from google.adk.sessions import InMemorySessionService
@@ -16,53 +18,126 @@
 from .agents.psa_video import create_psa_video_agents
 from .tools.health_tools import get_health_faq
 
+# Try to import analytics agent, use None if it fails
+try:
+    from .agents.analytics_agent import analytics_agent
+except Exception as e:
+    print(f"[WARNING] Analytics agent not available: {e}")
+    analytics_agent = None
+
 # === Model configuration ===
 GEMINI_MODEL = "gemini-2.0-flash"
 
+def get_current_time_context():
+    """Generate current time context for the agent"""
+    now = datetime.now()
+    current_time = now.strftime("%A, %B %d, %Y at %I:%M %p")
+    current_date = now.strftime("%Y-%m-%d")
+    current_year = now.year
+
+    return f"""
+CURRENT TIME CONTEXT:
+- Current Date & Time: {current_time}
+- Current Date (ISO): {current_date}
+- Current Year: {current_year}
+
+IMPORTANT: Always reference the current time when providing health advice, especially for:
+- Seasonal health recommendations
+- Time-sensitive health alerts
+- Current weather conditions affecting health
+- Recent data trends and patterns
+"""
+
+def create_root_agent_with_context(location_context=None, time_frame=None):
+    """Create the root agent with dynamic context including current time, location, and time frame"""
+
+    # Get current time context
+    time_context = get_current_time_context()
+
+    # Build location context if provided
+    location_info = ""
+    if location_context:
+        location_parts = []
+        if location_context.get('city'):
+            location_parts.append(f"City: {location_context['city']}")
+        if location_context.get('state'):
+            location_parts.append(f"State: {location_context['state']}")
+        if location_context.get('county'):
+            location_parts.append(f"County: {location_context['county']}")
+        if location_context.get('zipCode'):
+            location_parts.append(f"ZIP Code: {location_context['zipCode']}")
+        if location_context.get('formattedAddress'):
+            location_parts.append(f"Address: {location_context['formattedAddress']}")
+
+        if location_parts:
+            location_info = f"""
+USER LOCATION CONTEXT:
+- {', '.join(location_parts)}
+- Coordinates: {location_context.get('coordinates', {}).get('lat', 'N/A')}, {location_context.get('coordinates', {}).get('lng', 'N/A')}
+"""
+
+    # Build time frame context if provided
+    time_frame_info = ""
+    if time_frame:
+        time_frame_info = f"""
+DATA TIME FRAME CONTEXT:
+- Start Date: {time_frame.get('start_date', 'Not specified')}
+- End Date: {time_frame.get('end_date', 'Not specified')}
+- Analysis Period: {time_frame.get('period', 'Not specified')}
+"""
+
+    # Combine all context
+    global_context = f"{time_context}{location_info}{time_frame_info}"
+
+    return Agent(
+        name="community_health_assistant",
+        model=GEMINI_MODEL,
+        description="Main community health assistant that routes queries to specialized sub-agents.",
+        global_instruction=global_context,
+        instruction=(
+            "You are a friendly Community Health & Wellness Assistant. "
+            "When a user greets you, respond warmly with this menu:\n\n"
+            "\"Welcome to the Community Health & Wellness Assistant!\n\n"
+            "I can help you with:\n"
+            "1. [LIVE AIR QUALITY] Check current air quality via the AirNow API\n"
+            "2. [HISTORICAL AIR QUALITY] View past PM2.5 data from EPA BigQuery\n"
+            "3. [DISEASES] Infectious Disease Tracking - County-level CDC data\n"
+            "4. [CLINICS] Find nearby clinics or doctors using Google Search\n"
+            "5. [HEALTH] General wellness, hygiene, and preventive care advice\n"
+            "6. [ANALYTICS] Cross-dataset analysis across air quality and disease data\n"
+            "7. [PSA VIDEOS] Generate and share public health announcement videos\n\n"
+            "What would you like to know about today?\"\n\n"
+            "Routing Rules:\n"
+            "- Mentions of 'live', 'today', 'current', or 'now' → live_air_quality_agent.\n"
+            "- Questions mentioning years, months, or historical data → air_quality_agent.\n"
+            "- Mentions of infections, outbreaks, or diseases → infectious_diseases_agent.\n"
+            "- If the user describes symptoms or feeling unwell "
+            "(e.g., 'I have a rash', 'I feel dizzy', 'my tooth hurts', 'I cut my hand', "
+            "'my child is sick'), route to clinic_finder_agent."
+            "- General health, hygiene, prevention, wellness, or safety advice → health_faq_agent.\n"
+            "- Analytical questions spanning multiple datasets, correlations, trends, or complex analysis → analytics_agent.\n"
+            "- Requests to create PSA videos, announcements, or post to social media → PSA video agents.\n\n"
+            "Process:\n"
+            "1. If clinic_finder_agent provides a search phrase (e.g., 'dermatologist near San Jose'), "
+            "use google_search with that phrase.\n"
+            "2. Summarize the top 3–5 results clearly with clinic names and addresses.\n\n"
+            "After any response (from you or a sub-agent), always end with: "
+            "'Is there anything else I can help you with today?'"
+        ),
+        sub_agents=[
+            air_quality_agent,
+            live_air_quality_agent,
+            infectious_diseases_agent,
+            clinic_finder_agent,
+            health_faq_agent,
+        ] + ([analytics_agent] if analytics_agent else []) + psa_agents  # Add PSA video agents (ActionLine, VeoPrompt, Twitter)
+    )
+
 # === Create PSA Video Agents ===
 psa_agents = create_psa_video_agents(model=GEMINI_MODEL, tools_module=None)
 
-# === Root Agent Definition ===
-root_agent = Agent(
-    name="community_health_assistant",
-    model=GEMINI_MODEL,
-    description="Main community health assistant that routes queries to specialized sub-agents.",
-    instruction=(
-        "You are a friendly Community Health & Wellness Assistant. "
-        "When a user greets you, respond warmly with this menu:\n\n"
-        "\"Welcome to the Community Health & Wellness Assistant!\n\n"
-        "I can help you with:\n"
-        "1. [LIVE AIR QUALITY] Check current air quality via the AirNow API\n"
-        "2. [HISTORICAL AIR QUALITY] View past PM2.5 data from EPA BigQuery\n"
-        "3. [DISEASES] Infectious Disease Tracking - County-level CDC data\n"
-        "4. [CLINICS] Find nearby clinics or doctors using Google Search\n"
-        "5. [HEALTH] General wellness, hygiene, and preventive care advice\n"
-        "6. [PSA VIDEOS] Generate and share public health announcement videos\n\n"
-        "What would you like to know about today?\"\n\n"
-        "Routing Rules:\n"
-        "- Mentions of 'live', 'today', 'current', or 'now' → live_air_quality_agent.\n"
-        "- Questions mentioning years, months, or historical data → air_quality_agent.\n"
-        "- Mentions of infections, outbreaks, or diseases → infectious_diseases_agent.\n"
-        "- If the user describes symptoms or feeling unwell "
-        "(e.g., 'I have a rash', 'I feel dizzy', 'my tooth hurts', 'I cut my hand', "
-        "'my child is sick'), route to clinic_finder_agent."
-        "- General health, hygiene, prevention, wellness, or safety advice → health_faq_agent.\n"
-        "- Requests to create PSA videos, announcements, or post to social media → PSA video agents.\n\n"
-        "Process:\n"
-        "1. If clinic_finder_agent provides a search phrase (e.g., 'dermatologist near San Jose'), "
-        "use google_search with that phrase.\n"
-        "2. Summarize the top 3–5 results clearly with clinic names and addresses.\n\n"
-        "After any response (from you or a sub-agent), always end with: "
-        "'Is there anything else I can help you with today?'"
-    ),
-    sub_agents=[
-        air_quality_agent,
-        live_air_quality_agent,
-        infectious_diseases_agent,
-        clinic_finder_agent,
-        health_faq_agent,
-    ] + psa_agents,  # Add PSA video agents (ActionLine, VeoPrompt, Twitter)
-)
+# === Default Root Agent (for backward compatibility) ===
+root_agent = create_root_agent_with_context()
 
 # === Runner & Session Setup ===
 APP_NAME = "community_health_app"
@@ -85,11 +160,21 @@ def _initialize_session_and_runner():
         )
         _runner = Runner(agent=root_agent, app_name=APP_NAME, session_service=_session_service)
 
-def call_agent(query: str) -> str:
+def call_agent(query: str, location_context=None, time_frame=None) -> str:
     """Helper function to call the agent with a query and return the response."""
     _initialize_session_and_runner()
+
+    # Create agent with context if provided
+    if location_context or time_frame:
+        agent_with_context = create_root_agent_with_context(location_context, time_frame)
+        # Create a new runner with the context-aware agent
+        context_runner = Runner(agent=agent_with_context, app_name=APP_NAME, session_service=_session_service)
+        runner_to_use = context_runner
+    else:
+        runner_to_use = _runner
+
     content = types.Content(role="user", parts=[types.Part(text=query)])
-    events = _runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
+    events = runner_to_use.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)
 
     for event in events:
         if event.is_final_response():

multi_tool_agent_bquery_tools/agents/analytics_agent.py

@@ -0,0 +1,41 @@
+import os
+from google.adk.agents import Agent
+from .analytics_prompts import return_instructions_analytics
+
+# Import tools from other agents to get data
+from ..tools.air_quality_tool import get_air_quality
+from ..tools.live_air_quality_tool import get_live_air_quality
+from ..tools.disease_tools import get_infectious_disease_data
+
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Try to use VertexAI code executor, fall back to None if not available
+# (Agent will still work without code executor for basic data retrieval)
+code_executor = None
+try:
+    # Check if running with Google AI Studio (no VertexAI needed)
+    use_vertex_ai = os.getenv('GOOGLE_GENAI_USE_VERTEXAI', '').lower() == 'true'
+
+    if use_vertex_ai:
+        from google.adk.code_executors import VertexAiCodeExecutor
+        code_executor = VertexAiCodeExecutor(
+            optimize_data_file=True,
+            stateful=True,
+        )
+        print("[OK] Analytics agent using VertexAI code executor")
+    else:
+        print("[INFO] Running with Google AI Studio - code executor disabled")
+        print("[INFO] Analytics agent will work without code execution (data retrieval only)")
+except Exception as e:
+    print(f"[WARNING] VertexAI code executor not available: {e}")
+    print("[INFO] Analytics agent will work without code execution (data retrieval only)")
+    code_executor = None
+
+analytics_agent = Agent(
+    name="analytics_agent",
+    model=GEMINI_MODEL,
+    description="Analytics agent that performs cross-dataset analysis across air quality and disease data. Provides statistical analysis, correlations, and insights.",
+    instruction=return_instructions_analytics(),
+    code_executor=code_executor,
+    tools=[get_air_quality, get_live_air_quality, get_infectious_disease_data],
+)