app.py

import pickle
import torch
from sentence_transformers import SentenceTransformer, util
import gradio as gr
from fastapi import FastAPI, Query
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import threading
import uvicorn
import nest_asyncio

# --------------- Load model & embeddings ---------------
with open("chatbot.pkl", "rb") as f:
    data = pickle.load(f)

questions = data["questions"]
answers = data["answers"]
question_embeddings = data["embeddings"]

model = SentenceTransformer('all-MiniLM-L6-v2')

def chat(user_question, threshold=0.4, top_k=1):
    """Return best answer or fallback if none found."""
    user_embedding = model.encode(user_question, convert_to_tensor=True)
    cos_scores = util.cos_sim(user_embedding, question_embeddings)[0]
    top_results = torch.topk(cos_scores, k=top_k)

    for score, idx in zip(top_results.values, top_results.indices):
        if score.item() >= threshold:
            return {"matched_question": questions[idx], "answer": answers[idx], "score": score.item()}

    return {"matched_question": None, "answer": "Sorry, I am not able to answer that.", "score": None}

# --------------- FastAPI API ---------------
api = FastAPI()
api.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

@api.get("/")
def get_chat(question: str = Query(..., description="Your question here")):
    response = chat(question)
    return JSONResponse(response)

# --------------- Gradio UI ---------------
iface = gr.Interface(
    fn=lambda q: chat(q)["answer"],
    inputs=gr.Textbox(lines=2, placeholder="Ask a question..."),
    outputs=gr.Textbox(),
    title="FAQ Chatbot",
    description="Ask any question and get answers from the FAQ."
)

def run_gradio():
    iface.launch(server_name="0.0.0.0", server_port=7860)

# --------------- Run both FastAPI + Gradio ---------------
nest_asyncio.apply()
threading.Thread(target=run_gradio, daemon=True).start()

# Run FastAPI on port 8000
uvicorn.run(api, host="0.0.0.0", port=8000)