unzip database

app.py

@@ -1,5 +1,6 @@
-# app.py - Hugging Face Spaces version
+# app.py - Hugging Face Spaces version with auto-extract
 import os
+import zipfile
 from fastapi import FastAPI, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
@@ -9,11 +10,20 @@ import gradio as gr
 
 # Database path
 DB_PATH = "./medqa_db"
+ZIP_PATH = "./medqa_db.zip"
+
+# Extract database if needed
+if not os.path.exists(DB_PATH) and os.path.exists(ZIP_PATH):
+    print("Extracting database from zip file...")
+    with zipfile.ZipFile(ZIP_PATH, 'r') as zip_ref:
+        zip_ref.extractall(".")
+    print("Database extracted successfully!")
 
 # Initialize
 print(f"Loading database from: {DB_PATH}")
 client = chromadb.PersistentClient(path=DB_PATH)
 collection = client.get_collection("medqa")
+print(f"Collection loaded with {collection.count()} items")
 print(f"Loading MedCPT model...")
 model = SentenceTransformer('ncbi/MedCPT-Query-Encoder')
 print("Initialization complete!")
@@ -34,77 +44,4 @@ class SearchRequest(BaseModel):
     num_results: int = 3
 
 class SearchResponse(BaseModel):
-    results: list[dict]
-
-@app.get("/")
-async def root():
-    return {
-        "message": "MedQA Search API - Hugging Face Version", 
-        "status": "running",
-        "collection_count": collection.count()
-    }
-
-@app.post("/search_medqa", response_model=SearchResponse)
-async def search_medqa(request: SearchRequest):
-    """Search MedQA database for similar USMLE questions"""
-    try:
-        embedding = model.encode(request.query).tolist()
-        results = collection.query(
-            query_embeddings=[embedding], 
-            n_results=request.num_results
-        )
-        
-        formatted_results = []
-        for i in range(len(results['documents'][0])):
-            formatted_results.append({
-                "example_number": i + 1,
-                "question": results['documents'][0][i],
-                "answer": results['metadatas'][0][i].get('answer', 'N/A'),
-                "distance": results['distances'][0][i] if 'distances' in results else None
-            })
-        
-        return SearchResponse(results=formatted_results)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-# Gradio interface (optional - gives you a web UI)
-def search_interface(query: str, num_results: int = 3):
-    """Simple web interface for testing"""
-    try:
-        embedding = model.encode(query).tolist()
-        results = collection.query(
-            query_embeddings=[embedding], 
-            n_results=num_results
-        )
-        
-        output = ""
-        for i in range(len(results['documents'][0])):
-            output += f"\n{'='*60}\n"
-            output += f"Example {i+1}\n"
-            output += f"{'='*60}\n"
-            output += results['documents'][0][i] + "\n"
-            output += f"\nAnswer: {results['metadatas'][0][i].get('answer', 'N/A')}\n"
-            output += f"Similarity: {1 - results['distances'][0][i]:.3f}\n"
-        
-        return output
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# Create Gradio interface
-demo = gr.Interface(
-    fn=search_interface,
-    inputs=[
-        gr.Textbox(label="Medical Topic or Clinical Scenario", placeholder="e.g., hyponatremia"),
-        gr.Slider(1, 5, value=3, step=1, label="Number of Examples")
-    ],
-    outputs=gr.Textbox(label="Similar USMLE Questions", lines=20),
-    title="MedQA Search - USMLE Question Database",
-    description="Search for similar USMLE Step 1 questions using semantic similarity"
-)
-
-# Mount Gradio app and FastAPI
-app = gr.mount_gradio_app(app, demo, path="/")
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    results: list