app.py

import os
import gradio as gr
import fitz  # PyMuPDF
from sentence_transformers import SentenceTransformer
import chromadb
from chromadb.utils import embedding_functions
import openai

# Load GROQ API Key
openai.api_key = os.getenv("GROQ_API_KEY")
openai.api_base = "https://api.groq.com/openai/v1"

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

# Set up ChromaDB with persistence
persist_path = "./chroma_db"
db = chromadb.Client(chromadb.config.Settings(persist_directory=persist_path))
collection = db.get_or_create_collection("papers")

# Extract text from uploaded PDF
def extract_text_from_pdf(file):
    text = ""
    doc = fitz.open(stream=file.read(), filetype="pdf")
    for page in doc:
        text += page.get_text()
    return text

# Chunk and store in vector DB
def chunk_and_store(text):
    chunks = [text[i:i+500] for i in range(0, len(text), 500)]
    embeddings = embedder.encode(chunks).tolist()

    for i, chunk in enumerate(chunks):
        collection.add(documents=[chunk], ids=[f"id_{len(collection.get()['ids']) + i}"], embeddings=[embeddings[i]])
    db.persist()

# Retrieve relevant chunks and send to LLaMA3 via Groq
def retrieve_and_ask(query):
    if len(collection.get()["documents"]) == 0:
        return "Please upload a paper first."

    query_embedding = embedder.encode([query]).tolist()[0]
    results = collection.query(query_embeddings=[query_embedding], n_results=3)
    context = "\n".join(results["documents"][0])

    system_prompt = "You are an academic assistant helping students understand research papers."
    user_prompt = f"Based on the following context:\n{context}\n\nAnswer the question:\n{query}"

    try:
        response = openai.ChatCompletion.create(
            model="llama3-70b-8192",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ]
        )
        return response['choices'][0]['message']['content']
    except Exception as e:
        return f"Error: {str(e)}"

# Gradio UI
def handle_upload(file):
    if file is None:
        return "Upload a valid PDF file."
    text = extract_text_from_pdf(file)
    chunk_and_store(text)
    return "✅ Paper uploaded and processed."

def handle_query(query):
    return retrieve_and_ask(query)

with gr.Blocks() as demo:
    gr.Markdown("### 📘 RAG Academic Assistant\nUpload a paper and ask questions.")
    
    with gr.Row():
        file = gr.File(label="Upload PDF", type="binary")
        upload_btn = gr.Button("Process")
        upload_output = gr.Textbox()

    with gr.Row():
        query = gr.Textbox(label="Ask a question")
        response = gr.Textbox(label="Answer")
        ask_btn = gr.Button("Ask")

    upload_btn.click(handle_upload, inputs=[file], outputs=[upload_output])
    ask_btn.click(handle_query, inputs=[query], outputs=[response])

demo.launch()