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| from fastapi import FastAPI | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from pydantic import BaseModel | |
| from transformers import pipeline | |
| import logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| app = FastAPI( | |
| title="Sentiment Analysis API", | |
| description="Analyze sentiment of text using BERT fine-tuned on IMDB dataset", | |
| version="1.0.0", | |
| docs_url="/docs", | |
| redoc_url="/redoc" | |
| ) | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| logger.info("Loading sentiment analysis model...") | |
| try: | |
| classifier = pipeline("sentiment-analysis", model="Adedayo2000/sentiment-model") | |
| logger.info("Model loaded successfully!") | |
| except Exception as e: | |
| logger.error(f"Failed to load model: {e}") | |
| classifier = None | |
| LABEL_MAP = { | |
| "LABEL_0": "NEGATIVE", | |
| "LABEL_1": "POSITIVE" | |
| } | |
| class TextInput(BaseModel): | |
| text: str | |
| class Config: | |
| json_schema_extra = { | |
| "example": { | |
| "text": "I love this product! It's amazing!" | |
| } | |
| } | |
| def home(): | |
| return { | |
| "message": "Sentiment Analysis API", | |
| "status": "live", | |
| "model": "Adedayo2000/sentiment-model", | |
| "endpoints": { | |
| "GET /": "API information", | |
| "POST /predict": "Analyze sentiment of text", | |
| "GET /docs": "Interactive API documentation (Swagger UI)", | |
| "GET /redoc": "Alternative API documentation (ReDoc)", | |
| "GET /health": "Health check" | |
| } | |
| } | |
| def health_check(): | |
| return { | |
| "status": "healthy", | |
| "model_loaded": classifier is not None | |
| } | |
| def predict(input: TextInput): | |
| if not input.text or not input.text.strip(): | |
| return { | |
| "error": "Text cannot be empty", | |
| "example": {"text": "I love this product!"} | |
| } | |
| if classifier is None: | |
| return { | |
| "error": "Model not loaded", | |
| "status": "unavailable" | |
| } | |
| try: | |
| result = classifier(input.text)[0] | |
| raw_label = result["label"] | |
| sentiment = LABEL_MAP.get(raw_label, raw_label) | |
| confidence_score = result["score"] | |
| confidence_percentage = f"{confidence_score * 100:.2f}%" | |
| return { | |
| "text": input.text, | |
| "sentiment": sentiment, | |
| "confidence": confidence_percentage, | |
| "confidence_score": round(confidence_score, 4) | |
| } | |
| except Exception as e: | |
| logger.error(f"Prediction error: {e}") | |
| return { | |
| "error": "Prediction failed", | |
| "details": str(e) | |
| } | |