from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse, RedirectResponse
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer, util
from transformers import pipeline
from typing import List
import numpy as np

app = FastAPI()

# Load models
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")


# API endpoints
@app.post("/modify_query")
async def modify_query(request: Request):
    try:
        raw_data = await request.json()
        binary_embeddings = model.encode([raw_data['query_string']], precision="binary")
        return JSONResponse(content={'embeddings':binary_embeddings[0].tolist()})
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/modify_query_v3")
async def modify_query_v3(request: Request):
    try:
        # Generate embeddings for a list of query strings
        raw_data = await request.json()
        embeddings = model.encode(raw_data['query_string_list'])
        return JSONResponse(content={'embeddings':[emb.tolist() for emb in embeddings]})
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error in modifying query v3: {str(e)}")


@app.post("/makeanswer")
async def makeAnswer(request: Request):
    try:
        # Summarize the context
        raw_data = await request.json()
        response = summarizer(raw_data['context'], max_length=130, min_length=30, do_sample=False)
        return JSONResponse(content={'answer':response[0]["summary_text"]})
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error in T5 summarization: {str(e)}")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)