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Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application
+COPY app.py .
+
+# Create a non-root user (good practice for HF Spaces)
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+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 pydantic import BaseModel
+from sentence_transformers import CrossEncoder
+from typing import List, Dict, Any, Union
+import uvicorn
+import os
+
+app = FastAPI(
+    title="Cross-Encoder Reranking API",
+    description="Reranking service using cross-encoder/ms-marco-MiniLM-L-6-v2",
+    version="1.0.0"
+)
+
+# Load model once at startup
+MODEL_NAME = os.getenv("MODEL_NAME", "cross-encoder/ms-marco-MiniLM-L-6-v2")
+try:
+    model = CrossEncoder(MODEL_NAME)
+    print(f"Loaded CrossEncoder model: {MODEL_NAME}")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    model = None
+
+class RerankRequest(BaseModel):
+    query: str
+    documents: List[str]  # List of document texts to rerank
+    
+class RerankResponse(BaseModel):
+    scores: List[float]
+    ranked_indices: List[int]
+
+@app.get("/")
+def root():
+    return {
+        "message": "Cross-Encoder Reranking API",
+        "model": MODEL_NAME,
+        "status": "active" if model else "error"
+    }
+
+@app.get("/health")
+def health_check():
+    return {"status": "healthy", "model_loaded": model is not None}
+
+@app.post("/rerank", response_model=RerankResponse)
+def rerank_documents(request: RerankRequest):
+    if not model:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+    
+    try:
+        if not request.documents:
+            return RerankResponse(scores=[], ranked_indices=[])
+            
+        # Create pairs [query, doc]
+        pairs = [[request.query, doc] for doc in request.documents]
+        
+        # Predict scores
+        scores = model.predict(pairs)
+        
+        # Convert numpy floats to python floats
+        scores_list = scores.tolist() if hasattr(scores, 'tolist') else list(scores)
+        
+        # Get sorted indices (descending score)
+        # Using enumerate to keep track of original index
+        indexed_scores = list(enumerate(scores_list))
+        indexed_scores.sort(key=lambda x: x[1], reverse=True)
+        
+        ranked_indices = [idx for idx, score in indexed_scores]
+        
+        return RerankResponse(
+            scores=scores_list,
+            ranked_indices=ranked_indices
+        )
+            
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi
+uvicorn
+sentence-transformers
+pydantic
+torch --index-url https://download.pytorch.org/whl/cpu