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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)
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