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import streamlit as st
import os
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 upload
uploaded_file = st.file_uploader("Upload PDF", type=["pdf"])
pdf_path = "/app/data/document.pdf"
if uploaded_file:
os.makedirs("/app/data", exist_ok=True)
with open(pdf_path, "wb") as f:
f.write(uploaded_file.read())
st.success("PDF uploaded!")
with st.spinner("Indexing document..."):
try:
load_vectorstore(pdf_path)
st.success("Document indexed!")
except Exception as e:
st.error(f"Indexing failed: {str(e)}")
# Query input
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)}") |