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FastAPI-Backend-Models

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"""Main FastAPI application entry point"""

import sys
from pathlib import Path

# Add the app directory to Python path to ensure imports work
app_dir = Path(__file__).parent
if str(app_dir) not in sys.path:
    sys.path.insert(0, str(app_dir))

from contextlib import asynccontextmanager
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import logging

from config import (
    API_TITLE,
    API_DESCRIPTION,
    API_VERSION,
    STANCE_MODEL_ID,
    LABEL_MODEL_ID,
    HUGGINGFACE_API_KEY,
    HUGGINGFACE_STANCE_MODEL_ID,
    HUGGINGFACE_LABEL_MODEL_ID,
    HOST,
    PORT,
    RELOAD,
    CORS_ORIGINS,
    CORS_CREDENTIALS,
    CORS_METHODS,
    CORS_HEADERS,
)
from services import stance_model_manager, kpa_model_manager
from routes import api_router

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


@asynccontextmanager
async def lifespan(app: FastAPI):
    """Load models on startup and cleanup on shutdown"""
    # Startup: Load all models
    logger.info("Loading models on startup...")
    
    # Load stance detection model
    try:
        logger.info(f"Loading stance model from Hugging Face: {HUGGINGFACE_STANCE_MODEL_ID}")
        stance_model_manager.load_model(HUGGINGFACE_STANCE_MODEL_ID, HUGGINGFACE_API_KEY)
    except Exception as e:
        logger.error(f"✗ Failed to load stance model: {str(e)}")
        logger.error("⚠️  Stance detection endpoints will not work!")
    
    # Load KPA (label) model
    try:
        logger.info(f"Loading KPA model from Hugging Face: {HUGGINGFACE_LABEL_MODEL_ID}")
        kpa_model_manager.load_model(HUGGINGFACE_LABEL_MODEL_ID, HUGGINGFACE_API_KEY)
    except Exception as e:
        logger.error(f"✗ Failed to load KPA model: {str(e)}")
        logger.error("⚠️  KPA/Label prediction endpoints will not work!")
    
    logger.info("✓ API startup complete")
    logger.info("https://nlp-debater-project-fastapi-backend-models.hf.space/docs")
    
    yield  # Application runs here
    
    # Shutdown: Cleanup (if needed)
    # Currently no cleanup needed, but you can add it here if necessary

# Create FastAPI application
app = FastAPI(
    title=API_TITLE,
    description=API_DESCRIPTION,
    version=API_VERSION,
    docs_url="/docs",
    redoc_url="/redoc",
    lifespan=lifespan,
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=CORS_ORIGINS,
    allow_credentials=CORS_CREDENTIALS,
    allow_methods=CORS_METHODS,
    allow_headers=CORS_HEADERS,
)

# Include API routes
app.include_router(api_router)


if __name__ == "__main__":
    # Run the API server
    # Access at: http://localhost:8000
    # API docs at: http://localhost:8000/docs
    
    # Run the API server
    uvicorn.run(
        "main:app",
        host=HOST,
        port=PORT,
        reload=RELOAD,
        log_level="info"
    )

	"""Main FastAPI application entry point"""

	import sys
	from pathlib import Path

	# Add the app directory to Python path to ensure imports work
	app_dir = Path(__file__).parent
	if str(app_dir) not in sys.path:
	sys.path.insert(0, str(app_dir))

	from contextlib import asynccontextmanager
	from fastapi import FastAPI
	from fastapi.middleware.cors import CORSMiddleware
	import uvicorn
	import logging

	from config import (
	API_TITLE,
	API_DESCRIPTION,
	API_VERSION,
	STANCE_MODEL_ID,
	LABEL_MODEL_ID,
	HUGGINGFACE_API_KEY,
	HUGGINGFACE_STANCE_MODEL_ID,
	HUGGINGFACE_LABEL_MODEL_ID,
	HOST,
	PORT,
	RELOAD,
	CORS_ORIGINS,
	CORS_CREDENTIALS,
	CORS_METHODS,
	CORS_HEADERS,
	)
	from services import stance_model_manager, kpa_model_manager
	from routes import api_router

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


	@asynccontextmanager
	async def lifespan(app: FastAPI):
	"""Load models on startup and cleanup on shutdown"""
	# Startup: Load all models
	logger.info("Loading models on startup...")

	# Load stance detection model
	try:
	logger.info(f"Loading stance model from Hugging Face: {HUGGINGFACE_STANCE_MODEL_ID}")
	stance_model_manager.load_model(HUGGINGFACE_STANCE_MODEL_ID, HUGGINGFACE_API_KEY)
	except Exception as e:
	logger.error(f"✗ Failed to load stance model: {str(e)}")
	logger.error("⚠️ Stance detection endpoints will not work!")

	# Load KPA (label) model
	try:
	logger.info(f"Loading KPA model from Hugging Face: {HUGGINGFACE_LABEL_MODEL_ID}")
	kpa_model_manager.load_model(HUGGINGFACE_LABEL_MODEL_ID, HUGGINGFACE_API_KEY)
	except Exception as e:
	logger.error(f"✗ Failed to load KPA model: {str(e)}")
	logger.error("⚠️ KPA/Label prediction endpoints will not work!")

	logger.info("✓ API startup complete")
	logger.info("https://nlp-debater-project-fastapi-backend-models.hf.space/docs")

	yield # Application runs here

	# Shutdown: Cleanup (if needed)
	# Currently no cleanup needed, but you can add it here if necessary

	# Create FastAPI application
	app = FastAPI(
	title=API_TITLE,
	description=API_DESCRIPTION,
	version=API_VERSION,
	docs_url="/docs",
	redoc_url="/redoc",
	lifespan=lifespan,
	)

	# Add CORS middleware
	app.add_middleware(
	CORSMiddleware,
	allow_origins=CORS_ORIGINS,
	allow_credentials=CORS_CREDENTIALS,
	allow_methods=CORS_METHODS,
	allow_headers=CORS_HEADERS,
	)

	# Include API routes
	app.include_router(api_router)


	if __name__ == "__main__":
	# Run the API server
	# Access at: http://localhost:8000
	# API docs at: http://localhost:8000/docs

	# Run the API server
	uvicorn.run(
	"main:app",
	host=HOST,
	port=PORT,
	reload=RELOAD,
	log_level="info"
	)