# app.py - Hugging Face Spaces version
import os
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import chromadb
from sentence_transformers import SentenceTransformer
import gradio as gr

# Database path
DB_PATH = "./medqa_db"

# Initialize
print(f"Loading database from: {DB_PATH}")
client = chromadb.PersistentClient(path=DB_PATH)
collection = client.get_collection("medqa")
print(f"Loading MedCPT model...")
model = SentenceTransformer('ncbi/MedCPT-Query-Encoder')
print("Initialization complete!")

# FastAPI app
app = FastAPI(title="MedQA Search API")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

class SearchRequest(BaseModel):
    query: str
    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)