| import logging
|
| from contextlib import asynccontextmanager
|
|
|
| from fastapi import FastAPI, HTTPException, Request
|
|
|
| from api.schemas import SentimentRequest, SentimentResponse
|
| from core.model_loader import get_irony_model, get_model
|
| from core.service import analyze_text
|
|
|
| logger = logging.getLogger(__name__)
|
|
|
|
|
| @asynccontextmanager
|
| async def lifespan(app: FastAPI):
|
| """
|
| Pre-loads both heads so the first caller does not pay for them.
|
|
|
| A failure is recorded rather than raised: the service still answers the
|
| health check, which is what lets an orchestrator report a degraded state
|
| instead of restarting the container in a loop. Every response carries an
|
| irony verdict, so a half-loaded service is not a usable one and readiness
|
| covers both models rather than reporting them separately.
|
| """
|
| logger.info("Server starting up: pre-loading models.")
|
| try:
|
| get_model()
|
| get_irony_model()
|
| app.state.model_ready = True
|
| logger.info("Models loaded successfully.")
|
| except Exception:
|
| logger.exception("Failed to load models on startup.")
|
| app.state.model_ready = False
|
|
|
| yield
|
|
|
| logger.info("Server shutting down.")
|
|
|
|
|
| app = FastAPI(
|
| title="NLP Sentiment Analysis API",
|
| description=(
|
| "Real-time text classification with two DistilBERT heads: sentiment from a "
|
| "pre-trained checkpoint, irony from a head fine-tuned on TweetEval and served "
|
| "as a quantised ONNX graph."
|
| ),
|
| version="2.0.0",
|
| lifespan=lifespan,
|
| )
|
|
|
|
|
| app.state.model_ready = False
|
|
|
|
|
| @app.get("/")
|
| def health_check(request: Request):
|
| """Health check. Reports whether the model finished loading."""
|
| return {
|
| "status": "ok",
|
| "message": "Sentiment Analysis API is running",
|
| "model_ready": request.app.state.model_ready,
|
| }
|
|
|
|
|
| @app.post("/predict", response_model=SentimentResponse)
|
| def predict_sentiment(request: SentimentRequest):
|
| """
|
| Receives text, runs both heads and returns the sentiment with its irony verdict.
|
| """
|
| try:
|
| return analyze_text(request.text)
|
| except Exception:
|
|
|
| logger.exception("Prediction failed.")
|
| raise HTTPException(
|
| status_code=500, detail="Internal server error during prediction"
|
| ) from None
|
|
|
|
|
| if __name__ == "__main__":
|
| import os
|
|
|
| import uvicorn
|
|
|
| uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", "8000")))
|
|
|
