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import streamlit as st |
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import os |
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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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uploaded_file = st.file_uploader("Upload PDF", type=["pdf"]) |
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pdf_path = "/app/data/document.pdf" |
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if uploaded_file: |
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os.makedirs("/app/data", exist_ok=True) |
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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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try: |
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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"Indexing failed: {str(e)}") |
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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)}") |
