| import streamlit as st |
| import os |
| import json |
| from model import load_vectorstore, ask_question |
|
|
| st.set_page_config(page_title="Simple RAG Q&A", layout="centered") |
| st.title("RAG Q&A with Mistral AI") |
| st.write("Upload a PDF and ask questions about its content.") |
|
|
| |
| pdf_path = "/app/data/document.pdf" |
| uploaded_file = st.file_uploader("Upload PDF", type=["pdf"]) |
|
|
| if uploaded_file: |
| os.makedirs("/app/data", exist_ok=True) |
| try: |
| with open(pdf_path, "wb") as f: |
| f.write(uploaded_file.read()) |
| st.success("PDF uploaded!") |
| |
| with st.spinner("Indexing document..."): |
| load_vectorstore(pdf_path) |
| st.success("Document indexed!") |
| except Exception as e: |
| st.error(f"Failed to upload/index PDF: {str(e)}") |
|
|
| |
| query = st.text_input("Enter your question", |
| "How many articles are there in the Selenium webdriver python course?") |
| if st.button("Ask") and query: |
| if not os.path.exists(pdf_path): |
| st.error("Please upload a PDF first.") |
| else: |
| with st.spinner("Generating answer..."): |
| try: |
| result = ask_question(query, pdf_path) |
| st.subheader("Answer") |
| st.write(result["answer"]) |
| |
| st.subheader("Retrieved Contexts") |
| for i, context in enumerate(result["contexts"], 1): |
| with st.expander(f"Context {i}"): |
| st.write(context) |
| except Exception as e: |
| st.error(f"Failed to generate answer: {str(e)}") |
|
|
| |
| if "query" in st.experimental_get_query_params(): |
| query = st.experimental_get_query_params().get("query", [""])[0] |
| if query and os.path.exists(pdf_path): |
| try: |
| result = ask_question(query, pdf_path) |
| st.json(result) |
| except Exception as e: |
| st.json({"error": str(e)}) |
