app.py

import os
os.environ["HF_HOME"] = "/tmp/huggingface"
from fastapi.responses import HTMLResponse
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer
from transformers import pipeline
import faiss
import pickle

app = FastAPI(title="RAG Chatbot API")

# === Enable CORS (optional but recommended for browser-based Razor apps) ===
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Replace with your domain in production
    allow_methods=["*"],
    allow_headers=["*"],
)

# === Load once at startup ===
chunks_path = "chunks.pkl"
index_path = "index.faiss"

with open(chunks_path, "rb") as f:
    chunks = pickle.load(f)

index = faiss.read_index(index_path)
#embedder = SentenceTransformer("all-MiniLM-L6-v2")
embedder = SentenceTransformer("local_model")
#generator = pipeline("text2text-generation", model="google/flan-t5-base")
#generator = pipeline("text2text-generation", model="flan-t5-base-local")
generator = pipeline("text2text-generation", model="google/flan-t5-base", cache_dir="/tmp/huggingface")

# === Retrieval and Generation ===
def retrieve(query, top_k=3):
    query_embedding = embedder.encode([query], convert_to_numpy=True)
    distances, indices = index.search(query_embedding, top_k)
    return [chunks[i] for i in indices[0]]

def generate_answer(context, query):
    prompt = f"Answer the question based on the context and give meaningfull ending.\n\nContext:\n{context}\n\nQuestion: {query}"
    response = generator(prompt, max_new_tokens=150)[0]["generated_text"]
    return response.strip()


# === API Endpoint ===
class QueryRequest(BaseModel):
    query: str  # Must match Razor payload field

@app.post("/query")
def ask_question(request: QueryRequest):
    print(f"✅ Received query: {request.query}")  # Debug log
    retrieved = retrieve(request.query)
    context = "\n".join(retrieved)
    answer = generate_answer(context, request.query)
    return {
        "UserQuery": request.query,
        "RetrievedContext": context,
        "answer": answer
    }

from fastapi import Form

@app.post("/querystr")
def ask_question(query: str = Form(...)):
    retrieved = retrieve(query)
    context = "\n".join(retrieved)
    answer = generate_answer(context, query)
    return {
        "UserQuery": query,
        "RetrievedContext": context,
        "answer": answer
    }



@app.get("/", response_class=HTMLResponse)
def home():
    return """
    <h2>🤖 RAG Chatbot</h2>
    <form method="post" action="/querystr">
        <input name="query" placeholder="Ask a question" style="width:300px;" />
        <button type="submit">Ask</button>
    </form>
    """