api/app.py

import logging
from contextlib import asynccontextmanager
from fastapi import FastAPI, HTTPException
from fastapi.exceptions import RequestValidationError
from starlette.exceptions import HTTPException as StarletteHTTPException

# Import routers
from api.routers import medical, health, export, auth
from api.middleware import (
    ProcessTimeMiddleware, 
    LoggingMiddleware, 
    RateLimitMiddleware,
    AuthenticationMiddleware,
    get_cors_middleware_config
)
from fastapi.middleware.cors import CORSMiddleware
from api.exceptions import (
    http_exception_handler,
    validation_exception_handler,
    general_exception_handler,
    starlette_exception_handler
)

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


@asynccontextmanager
async def lifespan(app: FastAPI):
    """Application lifespan management with background initialization"""
    # Startup
    logger.info("Starting Lung Cancer AI Advisor API...")
    
    # Start background initialization of heavy components
    try:
        from core.background_init import start_background_initialization
        logger.info("🚀 Starting background initialization of components...")
        start_background_initialization()
        logger.info("API started successfully (components loading in background)")
    except Exception as e:
        logger.error(f"Failed to start background initialization: {e}")
        logger.info("API started with lazy loading fallback")
    
    yield
    
    # Shutdown
    logger.info("Shutting down Lung Cancer AI Advisor API...")


# Create FastAPI application
app = FastAPI(
    title="Lung Cancer AI Advisor API",
    description="""AI-Powered Lung Cancer Information & Support API

This API provides intelligent responses to lung cancer-related queries using advanced AI and medical knowledge retrieval.

**Key Features:**
- Intelligent Query Processing: AI agent automatically selects appropriate tools and data sources
- Session Management: Maintains conversation context across multiple queries
- Streaming Support: Real-time response streaming for better UX
- Medical Knowledge Base: Access to comprehensive lung cancer information

**Main Endpoints:**
- POST /ask - Get complete AI response for a query
- POST /ask/stream - Stream AI response in real-time (recommended for better UX)
- GET /health - Check API health and initialization status
- POST /export/{format} - Export conversation history

""",
    version="1.0.0",
    docs_url="/docs",
    redoc_url="/redoc",
    lifespan=lifespan
)

# Add middleware
app.add_middleware(CORSMiddleware, **get_cors_middleware_config())
app.add_middleware(ProcessTimeMiddleware)
app.add_middleware(LoggingMiddleware)
app.add_middleware(RateLimitMiddleware, calls_per_minute=100)  # Adjust as needed
app.add_middleware(AuthenticationMiddleware)  # Protect API endpoints

# Add exception handlers
app.add_exception_handler(HTTPException, http_exception_handler)
app.add_exception_handler(RequestValidationError, validation_exception_handler)
app.add_exception_handler(StarletteHTTPException, starlette_exception_handler)
app.add_exception_handler(Exception, general_exception_handler)

# Include routers
app.include_router(auth.router)
app.include_router(health.router)
app.include_router(medical.router)
app.include_router(export.router)

# Root endpoint
@app.get("/")
async def root():
    """Root endpoint with API information"""
    return {
        "name": "Lung Cancer AI Advisor API",
        "version": "1.0.0",
        "description": "AI-powered advisor for lung cancer information and support",
        "docs": "/docs",
        "health": "/health",
        "endpoints": {
            "ask": "/ask",
            "ask_stream": "/ask/stream",
            "initialization_status": "/health/initialization",
            "export": "/export/{format}?session_id={session_id}"
        }
    }




if __name__ == "__main__":
    import uvicorn
    uvicorn.run(
        "api.app:app",
        host="127.0.0.1",
        port=8000,
        reload=True,
        log_level="info"
    )