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main.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline
+
+app = FastAPI()
+
+# Initialize the sentiment analysis pipeline
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+class SentimentRequest(BaseModel):
+    text: str
+
+class SentimentResponse(BaseModel):
+    label: str
+    score: float
+
+@app.post("/sentiment", response_model=SentimentResponse)
+async def analyze_sentiment(request: SentimentRequest):
+    text = request.text
+    results = sentiment_pipeline(text)
+    best_result = max(results, key=lambda x: x["score"])
+    return SentimentResponse(label=best_result["label"], score=best_result["score"])
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+annotated-types==0.5.0
+anyio==3.7.1
+certifi==2023.7.22
+charset-normalizer==3.2.0
+click==8.1.7
+colorama==0.4.6
+fastapi==0.103.1
+filelock==3.12.4
+fsspec==2023.9.2
+h11==0.14.0
+huggingface-hub==0.17.3
+idna==3.4
+Jinja2==3.1.2
+MarkupSafe==2.1.3
+mpmath==1.3.0
+networkx==3.1
+numpy==1.26.0
+packaging==23.1
+pydantic==2.4.1
+pydantic_core==2.10.1
+PyYAML==6.0.1
+regex==2023.8.8
+requests==2.31.0
+safetensors==0.3.3
+sniffio==1.3.0
+starlette==0.27.0
+sympy==1.12
+tokenizers==0.13.3
+torch==2.0.1
+tqdm==4.66.1
+transformers==4.33.2
+typing_extensions==4.8.0
+urllib3==2.0.5
+uvicorn==0.23.2