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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+from transformers import pipeline
+from typing import Dict, List
+from contextlib import asynccontextmanager
+import torch
+import logging
+import time
+from datetime import datetime, timezone
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+logger = logging.getLogger("sentiment-api")
+
+MODEL_ID = "Rameen191/sentiment-tutorial"  # <-- apna HF username dalen
+
+ml_models: Dict[str, object] = {}
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    logger.info(f"Loading model: {MODEL_ID} ...")
+    try:
+        ml_models["classifier"] = pipeline(
+            "sentiment-analysis",
+            model=MODEL_ID,
+            device=0 if torch.cuda.is_available() else -1,
+        )
+        logger.info("✅ Model loaded successfully.")
+    except Exception as e:
+        logger.error(f"❌ Failed to load model: {e}")
+        ml_models["classifier"] = None
+    yield
+    ml_models.clear()
+
+app = FastAPI(
+    title="Sentiment Analysis API",
+    description="A DistilBERT-based sentiment classifier.",
+    version="1.0.0",
+    lifespan=lifespan,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class TextRequest(BaseModel):
+    text: str = Field(..., min_length=1, max_length=5000)
+
+class BatchRequest(BaseModel):
+    texts: List[str] = Field(..., min_length=1, max_length=50)
+
+class SentimentResponse(BaseModel):
+    text: str
+    label: str
+    confidence: float
+    probabilities: Dict[str, float]
+    timestamp: str
+
+@app.middleware("http")
+async def log_requests(request, call_next):
+    start = time.time()
+    response = await call_next(request)
+    duration = time.time() - start
+    logger.info(f'{request.method} {request.url.path} -> {response.status_code} ({duration:.3f}s)')
+    return response
+
+@app.get("/")
+async def root():
+    return {"message": "Sentiment Analysis API", "docs": "/docs", "health": "/health"}
+
+@app.get("/health")
+async def health_check():
+    return {
+        "status": "healthy" if ml_models.get("classifier") is not None else "degraded",
+        "model_loaded": ml_models.get("classifier") is not None,
+        "model_id": MODEL_ID,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+    }
+
+@app.post("/predict", response_model=SentimentResponse)
+async def predict_sentiment(request: TextRequest):
+    classifier = ml_models.get("classifier")
+    if classifier is None:
+        raise HTTPException(status_code=503, detail="Model not loaded.")
+    try:
+        result = classifier(request.text)[0]
+        probs = {result['label']: round(result['score'], 4)}
+        other_label = 'NEGATIVE' if result['label'] == 'POSITIVE' else 'POSITIVE'
+        probs[other_label] = round(1 - result['score'], 4)
+        return SentimentResponse(
+            text=request.text[:200],
+            label=result['label'],
+            confidence=round(result['score'], 4),
+            probabilities=probs,
+            timestamp=datetime.now(timezone.utc).isoformat(),
+        )
+    except Exception as e:
+        logger.error(f"Prediction error: {e}")
+        raise HTTPException(status_code=500, detail="Internal error during prediction.")
+
+@app.post("/predict/batch")
+async def predict_batch(request: BatchRequest):
+    classifier = ml_models.get("classifier")
+    if classifier is None:
+        raise HTTPException(status_code=503, detail="Model not loaded.")
+    try:
+        results = classifier(request.texts)
+        return {
+            "results": [
+                {"text": text[:100], "label": r['label'], "confidence": round(r['score'], 4)}
+                for text, r in zip(request.texts, results)
+            ],
+            "total": len(results),
+        }
+    except Exception as e:
+        logger.error(f"Batch prediction error: {e}")
+        raise HTTPException(status_code=500, detail="Internal error during batch prediction.")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi==0.111.0
+uvicorn[standard]==0.30.1
+transformers==4.41.2
+torch==2.3.1
+pydantic==2.7.4
+huggingface_hub==0.23.4