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