app.py

import gradio as gr
from sentence_transformers import SentenceTransformer
from transformers import pipeline
import faiss
import os

# Load models
embedder = SentenceTransformer("all-MiniLM-L6-v2")
#qa_pipeline = pipeline("text-generation", model="gpt2")  # Replace with a better QA model if desired
qa_pipeline = pipeline("text-generation", model="tiiuae/falcon-rw-1b")

# In-memory vector index and docs
index = None
docs = []

def process_docs(files):
    global index, docs
    docs = []

    # Read files and compute embeddings
    texts = []
    for file in files:
        with open(file.name, 'r', encoding='utf-8') as f:
            content = f.read()

        texts.append(content)
        docs.append(content)

    embeddings = embedder.encode(texts, convert_to_tensor=False)
    dim = embeddings[0].shape[0]

    # Create FAISS index
    index = faiss.IndexFlatL2(dim)
    index.add(embeddings)

    return "Documents uploaded and indexed!"

def chat(query):
    if not index or not docs:
        return "Please upload documents first."

    # Embed query and search
    q_embedding = embedder.encode([query])
    D, I = index.search(q_embedding, k=3)

    # Get top documents
    context = "\n".join([docs[i] for i in I[0]])
    prompt = f"Context: {context}\nQuestion: {query}\nAnswer:"
    
    response = qa_pipeline(prompt, max_new_tokens=100, do_sample=False)
    return response[0]['generated_text'].split("Answer:")[-1].strip()

# Gradio UI
with gr.Blocks() as demo:
    with gr.Row():
        file_upload = gr.File(file_types=[".txt"], file_count="multiple")
        upload_btn = gr.Button("Upload Documents")
        upload_output = gr.Textbox(label="Status")

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

    upload_btn.click(fn=process_docs, inputs=[file_upload], outputs=[upload_output])
    ask_btn.click(fn=chat, inputs=[question], outputs=[answer])

demo.launch()