| import streamlit as st |
| import tempfile |
| import os |
| from llm import load_and_process_pdf, create_vectorstore, create_rag_chain, get_response |
|
|
| st.set_page_config(page_title="PDF Q&A Chatbot", page_icon="📚") |
|
|
| st.title("PDF Q&A Chatbot") |
|
|
| |
| if 'vectorstore' not in st.session_state: |
| st.session_state.vectorstore = None |
| if 'rag_chain' not in st.session_state: |
| st.session_state.rag_chain = None |
|
|
| |
| uploaded_file = st.file_uploader("Choose a PDF file", type="pdf") |
|
|
| if uploaded_file is not None and st.session_state.vectorstore is None: |
| |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file: |
| tmp_file.write(uploaded_file.getvalue()) |
| tmp_file_path = tmp_file.name |
|
|
| |
| with st.spinner("Processing PDF..."): |
| splits = load_and_process_pdf(tmp_file_path) |
| st.session_state.vectorstore = create_vectorstore(splits) |
| st.session_state.rag_chain = create_rag_chain() |
|
|
| st.success("PDF processed successfully! Now you can ask questions.") |
|
|
| |
| os.unlink(tmp_file_path) |
|
|
| |
| if st.session_state.vectorstore is not None: |
| question = st.text_input("Ask a question about the PDF:") |
|
|
| if question: |
| with st.spinner("Generating answer..."): |
| answer = get_response(st.session_state.rag_chain, st.session_state.vectorstore, question) |
| st.write("Answer:", answer) |
|
|
| else: |
| st.info("Please upload a PDF file to get started.") |
|
|
