app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from sentence_transformers import SentenceTransformer
import fitz  # PyMuPDF for PDF handling
import faiss
import numpy as np

# Load models for embeddings and QA
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")

# Global variables to store documents and index
documents, passages, embeddings, file_names, indexes, index = {}, [], None, [], [], None

# Function to extract text from uploaded PDFs
def upload_and_extract_text(files):
    global documents
    documents = {}
    
    for file in files:
        with fitz.open(file.name) as pdf:
            text = ""
            for page in pdf:
                text += page.get_text("text")
            documents[file.name] = text
    return "PDF content extracted and indexed successfully."

# Function to embed documents and create FAISS index
def embed_and_index_documents(chunk_size=300):
    global passages, embeddings, file_names, indexes, index
    
    passages, file_names, indexes = [], [], []
    
    for file_name, text in documents.items():
        chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
        passages.extend(chunks)
        file_names.extend([file_name] * len(chunks))
        indexes.extend(range(len(chunks)))
    
    # Create embeddings
    embeddings = embedding_model.encode(passages, convert_to_tensor=False)
    embedding_matrix = np.array(embeddings)
    
    # Build FAISS index
    index = faiss.IndexFlatL2(embedding_matrix.shape[1])
    index.add(embedding_matrix)
    return "Documents embedded and indexed successfully."

# Function to retrieve relevant passages
def retrieve_relevant_passages(question, top_k=3):
    question_embedding = embedding_model.encode([question])
    distances, retrieved_indices = index.search(np.array(question_embedding), top_k)
    retrieved_passages = [passages[i] for i in retrieved_indices[0]]
    return retrieved_passages

# Function to answer questions using retrieved passages
def answer_question(question, top_k=3):
    retrieved_passages = retrieve_relevant_passages(question, top_k)
    context = " ".join(retrieved_passages)
    input_text = f"Answer the question based on this content: {context}. Question: {question}"
    
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    output_ids = qa_model.generate(input_ids, max_length=150)
    answer = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    
    return answer

# Gradio interface functions
def handle_file_upload(files):
    message = upload_and_extract_text(files)
    indexing_message = embed_and_index_documents()
    return f"{message}\n{indexing_message}"

def chat_with_pdfs(question):
    answer = answer_question(question)
    return answer

# Define Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# PDF Chatbot using RAG (Retrieval-Augmented Generation)")
    
    with gr.Tab("Upload PDF(s)"):
        file_upload = gr.File(label="Upload PDF files", file_types=[".pdf"], file_count="multiple")
        upload_button = gr.Button("Process PDFs")
        upload_output = gr.Textbox(label="Status")
        upload_button.click(fn=handle_file_upload, inputs=file_upload, outputs=upload_output)
    
    with gr.Tab("Chat with PDFs"):
        question_input = gr.Textbox(label="Ask a question about the uploaded PDFs")
        answer_output = gr.Textbox(label="Answer")
        question_input.submit(fn=chat_with_pdfs, inputs=question_input, outputs=answer_output)

# Launch the app
demo.launch()