main.py

from fastapi import FastAPI, File, UploadFile, HTTPException
from pydantic import BaseModel
import fitz  # PyMuPDF
import faiss
from sentence_transformers import SentenceTransformer
import numpy as np
from phi.agent import Agent
from phi.model.groq import Groq

app = FastAPI()

# Load embedding model
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")

# Global storage
pdf_text_chunks = []
index = None

def agent_response(question, retrieved_text):
    """Generate response using AI model based on retrieved text."""
    agent = Agent(
        model=Groq(id="llama-3.3-70b-versatile"),
        markdown=True,
        description="You are an AI assistant that provides the answer based on the provided document.",
        instructions=[
            f"First read the question carefully. The question is: **{question}**",
            f"Then read the document provided to you as a text. The document is: \n**{retrieved_text}**\n",
            "Finally answer the question based on the provided document only. Don't try to give random responses."
        ]
    )
    response = agent.run(question + '\n' + retrieved_text).content
    return response

@app.post("/upload-pdf/")
async def upload_pdf(file: UploadFile = File(...)):
    """Extract text from PDF, create FAISS index."""
    global pdf_text_chunks, index
    pdf_text_chunks = []

    # Read the uploaded file into memory
    pdf_data = await file.read()
    with fitz.open("pdf", pdf_data) as doc:
        for page in doc:
            pdf_text_chunks.append(page.get_text("text"))

    # Embed the chunks
    embeddings = embedding_model.encode(pdf_text_chunks, convert_to_numpy=True)

    # Create FAISS index
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(embeddings)

    return {"message": "PDF processed successfully!"}

class QueryRequest(BaseModel):
    question: str

@app.post("/chat/")
async def chat(request: QueryRequest):
    """Retrieve the most relevant chunk and generate a response."""
    global index, pdf_text_chunks
    if index is None:
        raise HTTPException(status_code=400, detail="No PDF uploaded yet.")

    # Search for relevant text
    query_embedding = embedding_model.encode([request.question], convert_to_numpy=True)
    _, indices = index.search(query_embedding, 5)  # Get top 5 matches
    retrieved_texts = [pdf_text_chunks[idx] for idx in indices[0]]
    retrieved_text_combined = "\n\n".join(retrieved_texts)

    response = agent_response(request.question, retrieved_text_combined)
    return {"user": request.question, "response": response}