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Update app.py
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app.py
CHANGED
@@ -1,11 +1,12 @@
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import streamlit as st
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import os
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from utils.ingestion import DocumentProcessor
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from utils.llm import LLMProcessor
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from utils.qa import QAEngine
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st.
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# Initialize processors
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document_processor = DocumentProcessor()
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@@ -15,7 +16,6 @@ qa_engine = QAEngine()
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# Ensure temp directory exists
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os.makedirs("temp", exist_ok=True)
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# Sidebar - File Upload
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st.sidebar.header("Upload a PDF")
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uploaded_file = st.sidebar.file_uploader("Choose a PDF file", type=["pdf"])
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@@ -27,40 +27,23 @@ if uploaded_file:
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st.sidebar.success("β
File uploaded successfully!")
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with st.spinner(""):
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document_processor.process_document(pdf_path)
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st.sidebar.success("β
Document processed successfully!")
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#
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st.session_state.chat_history = []
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with chat_container:
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for message in st.session_state.chat_history:
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role, text = message
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if role == "user":
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st.markdown(f"**π§βπ» You:** {text}")
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else:
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st.
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# User Input at the bottom
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question = st.text_input("Ask a question:", placeholder="Type your question and press Enter...", key="user_input")
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if question:
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# Append user question to history
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st.session_state.chat_history.append(("user", question))
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with st.spinner(""):
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answer = qa_engine.query(question)
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# Append AI answer to history
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st.session_state.chat_history.append(("ai", answer))
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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 utils.ingestion import DocumentProcessor
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from utils.llm import LLMProcessor
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from utils.qa import QAEngine
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st.set_page_config(page_title="AI-Powered Document QA", layout="wide")
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st.title("π AI-Powered Document QA")
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# Initialize processors
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document_processor = DocumentProcessor()
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# Ensure temp directory exists
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os.makedirs("temp", exist_ok=True)
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st.sidebar.header("Upload a PDF")
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uploaded_file = st.sidebar.file_uploader("Choose a PDF file", type=["pdf"])
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st.sidebar.success("β
File uploaded successfully!")
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with st.spinner("π Processing document..."):
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document_processor.process_document(pdf_path)
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st.sidebar.success("β
Document processed successfully!")
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# Query input
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question = st.text_input("Ask a question from the document:", placeholder="What are the key insights?")
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if st.button("π Search & Answer"):
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if question:
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with st.spinner("π§ Searching for relevant context..."):
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answer = qa_engine.query(question)
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st.subheader("π Answer:")
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st.write(answer)
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else:
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st.warning("β οΈ Please enter a question.")
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st.markdown("---")
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st.caption("π€ Powered by ChromaDB + Groq LLM | Built with β€οΈ using Streamlit")
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