make the app work locally

Author: seligj95

README.md

@@ -33,11 +33,41 @@ A FastAPI-based to-do list application with Azure AI Agents integration and Mode
    ```bash
    python main.py
    ```
+   
+   > **Note**: The app will start successfully and provide basic to-do functionality without any environment variables. AI chat features require Azure resource deployment and configuration (see below).
 
-3. **Access the app**:
-   - To-do List: http://localhost:8000
-   - AI Chat: http://localhost:8000/chat
-   - Health Check: http://localhost:8000/health
+3. **Configure Azure AI (Optional)**:
+   For AI chat features in your local environment, if you have AI Foundry resources already deployed, set these environment variables. Ensure you're logged into the Azure CLI using `az login` as that credential is required to access the chat.
+   
+   **Option A: Create a `.env` file in the project root:**
+   ```bash
+   # .env file - Optional for local AI Chat functionality
+   AZURE_AI_PROJECT_ENDPOINT=https://my-ai-project-abc123.westus.ai.azure.com
+   AZURE_AI_PROJECT_NAME=my-todo-project
+   AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o
+   AZURE_APP_SERVICE_URL=http://localhost:8000
+   ```
+   
+   **Option B: Set environment variables in PowerShell:**
+   ```powershell
+   $env:AZURE_AI_PROJECT_ENDPOINT="https://my-ai-project-abc123.westus.ai.azure.com"
+   $env:AZURE_AI_PROJECT_NAME="my-todo-project"
+   $env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o"
+   $env:AZURE_APP_SERVICE_URL="http://localhost:8000"
+   ```
+   
+   **Option C: Set environment variables in Command Prompt:**
+   ```cmd
+   set AZURE_AI_PROJECT_ENDPOINT=https://my-ai-project-abc123.eastus2.ai.azure.com
+   set AZURE_AI_PROJECT_NAME=my-todo-project
+   set AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o
+   set AZURE_APP_SERVICE_URL=http://localhost:8000
+   ```
+
+4. **Access the app**:
+   - To-do List: http://localhost:8000 ✅ (always works)
+   - AI Chat: http://localhost:8000/chat ⚠️ (requires Azure setup)
+   - Health Check: http://localhost:8000/health ✅ (always works)
 
 ### Azure Deployment
 
@@ -148,22 +178,6 @@ curl -X POST http://localhost:8000/mcp/stream \
 - `GET /mcp/stream` - MCP server info
 - `POST /mcp/stream` - MCP JSON-RPC endpoint
 
-## Configuration
-
-Environment variables are automatically configured during Azure deployment:
-
-- `AZURE_AI_PROJECT_ENDPOINT` - AI Foundry endpoint
-- `AZURE_OPENAI_DEPLOYMENT_NAME` - GPT-4o deployment name
-- `AZURE_APP_SERVICE_URL` - App service URL for MCP
-
-## Development
-
-The app uses:
-- **FastAPI** for the web framework
-- **Azure AI Agents** for AI functionality
-- **Bootstrap 5** for UI styling
-- **In-memory storage** for simplicity (easily upgradeable)
-
 ## License
 
 MIT License

main.py

@@ -63,18 +63,27 @@ def get_azure_ai_project_endpoint():
     raise ValueError(f"Invalid AZURE_AI_PROJECT_ENDPOINT format: {azure_ai_endpoint}")
 
 
-PROJECT_ENDPOINT = get_azure_ai_project_endpoint()
-MODEL_DEPLOYMENT = os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME')
-if not MODEL_DEPLOYMENT:
-    raise ValueError("AZURE_OPENAI_DEPLOYMENT_NAME environment variable is required")
-
-# MCP server URL from Bicep deployment
-azure_app_service_url = os.getenv('AZURE_APP_SERVICE_URL')
-if not azure_app_service_url:
-    raise ValueError("AZURE_APP_SERVICE_URL environment variable is required")
-
-MCP_SERVER_URL = azure_app_service_url + "/mcp/stream"
-MCP_SERVER_LABEL = "todolist"
+# Make Azure AI configuration optional for local development
+try:
+    PROJECT_ENDPOINT = get_azure_ai_project_endpoint()
+    MODEL_DEPLOYMENT = os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME')
+    if not MODEL_DEPLOYMENT:
+        raise ValueError("AZURE_OPENAI_DEPLOYMENT_NAME environment variable is required")
+    
+    # MCP server URL from Bicep deployment or default to localhost
+    azure_app_service_url = os.getenv('AZURE_APP_SERVICE_URL', 'http://localhost:8000')
+    MCP_SERVER_URL = azure_app_service_url + "/mcp/stream"
+    MCP_SERVER_LABEL = "todolist"
+    AI_CONFIG_AVAILABLE = True
+    logger.info("✓ Azure AI configuration loaded successfully")
+except Exception as e:
+    PROJECT_ENDPOINT = None
+    MODEL_DEPLOYMENT = None
+    MCP_SERVER_URL = None
+    MCP_SERVER_LABEL = None
+    AI_CONFIG_AVAILABLE = False
+    logger.warning(f"⚠️ Azure AI not configured: {e}")
+    logger.info("ℹ️ Running in local mode - AI chat features disabled")
 
 
 # Pydantic Models
@@ -312,18 +321,22 @@ async def chat_with_agent(self, message: str) -> ChatResponse:
             logger.error(f"Full traceback: {traceback.format_exc()}")
             return ChatResponse(response=f"Error: {str(e)}")
 
-# Initialize Azure AI service
-ai_service = AzureAIAgentService()
+# Initialize Azure AI service only if configuration is available
+ai_service = AzureAIAgentService() if AI_CONFIG_AVAILABLE else None
 
 # Template configuration
 templates = Jinja2Templates(directory="templates")
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    logger.info("Starting Todo MCP FastAPI Server with Azure AI Agents")
+    logger.info("Starting To-do MCP FastAPI Server")
 
-    # Initialize Azure AI service
-    await ai_service.initialize()
+    # Initialize Azure AI service if available
+    if ai_service and AI_CONFIG_AVAILABLE:
+        await ai_service.initialize()
+        logger.info("✓ Azure AI service initialized")
+    else:
+        logger.info("ℹ️ Running without Azure AI - basic to-do features only")
 
     yield
     logger.info("Shutting down Todo MCP FastAPI Server")
@@ -365,7 +378,9 @@ async def health():
         "status": "healthy", 
         "todos_count": len(todos_storage),
         "azure_ai_available": AZURE_AI_AVAILABLE,
-        "ai_service_initialized": ai_service.is_initialized,
+        "ai_service_initialized": (
+            ai_service.is_initialized if ai_service else False
+        ),
         "project_endpoint": PROJECT_ENDPOINT,
         "mcp_server_url": MCP_SERVER_URL
     }
@@ -444,10 +459,10 @@ async def delete_todo_api(todo_id: int):
 @app.post("/api/chat/session", response_model=ChatSession)
 async def create_chat_session():
     """Create a new chat session"""
-    if not ai_service.is_initialized:
+    if not ai_service or not ai_service.is_initialized:
         raise HTTPException(
             status_code=503, 
-            detail="Azure AI service is not available"
+            detail="Azure AI service is not available. Please configure environment variables for AI chat features."
         )
 
     session_id = generate_session_id()
@@ -486,10 +501,10 @@ async def chat_with_ai_session(chat_message: ChatSessionMessage):
 @app.post("/api/chat", response_model=ChatResponse)
 async def chat_with_ai(chat_message: ChatMessage):
     """Send a message to the AI agent with MCP tools (legacy endpoint)"""
-    if not ai_service.is_initialized:
+    if not ai_service or not ai_service.is_initialized:
         raise HTTPException(
             status_code=503, 
-            detail="Azure AI service is not available"
+            detail="Azure AI service is not available. Please configure environment variables for AI chat features."
         )
 
     response = await ai_service.chat_with_agent(chat_message.message)
@@ -499,8 +514,9 @@ async def chat_with_ai(chat_message: ChatMessage):
 async def chat_status():
     """Get the status of the Azure AI chat service"""
     return {
-        "available": ai_service.is_initialized,
+        "available": ai_service.is_initialized if ai_service else False,
         "azure_ai_packages_available": AZURE_AI_AVAILABLE,
+        "ai_config_available": AI_CONFIG_AVAILABLE,
         "project_endpoint": PROJECT_ENDPOINT,
         "model_deployment": MODEL_DEPLOYMENT,
         "mcp_server_configured": bool(MCP_SERVER_URL)