import os import time import streamlit as st from extract import extract_text_from_pdfs from generate import generate_response from preprocess import preprocess_text from retrieve import create_vectorizer, retrieve # Streamlit UI st.title("RAG-based PDF Query System") uploaded_files = st.file_uploader("Upload PDFs", type=["pdf"], accept_multiple_files=True) if uploaded_files: st.write("Processing the uploaded PDFs...") # Initialize progress bar progress_bar = st.progress(0) status_text = st.empty() # Save uploaded files to disk pdf_files = [] for uploaded_file in uploaded_files: with open(uploaded_file.name, "wb") as f: f.write(uploaded_file.getbuffer()) pdf_files.append(uploaded_file.name) # Extract text from PDFs with progress updates num_files = len(pdf_files) texts = [] for i, pdf_file in enumerate(pdf_files): status_text.text(f"Extracting text from file {i + 1} of {num_files}...") text = extract_text_from_pdfs([pdf_file]) texts.extend(text) progress_bar.progress((i + 1) / num_files) time.sleep(0.1) # Simulate time taken for processing # Preprocess text with progress updates status_text.text("Preprocessing text...") progress_bar.progress(0.5) processed_texts = preprocess_text(texts) time.sleep(0.1) # Simulate time taken for processing # Create vectorizer and transform texts status_text.text("Creating vectorizer and transforming texts...") progress_bar.progress(0.75) vectorizer, X = create_vectorizer(processed_texts) time.sleep(0.1) # Simulate time taken for processing # Finalize progress progress_bar.progress(1.0) status_text.text("Processing complete!") query = st.text_input("Enter your query:") if query: # Retrieve relevant texts top_indices = retrieve(query, X, vectorizer) retrieved_texts = [texts[i] for i in top_indices] # Generate response response = generate_response(retrieved_texts, query) st.write("Response:") st.write(response) # Clean up uploaded files for pdf_file in pdf_files: os.remove(pdf_file)