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Update app.py
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app.py
CHANGED
@@ -1,4 +1,5 @@
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import streamlit as st
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from PyPDF2 import PdfReader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_groq import ChatGroq
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@@ -71,38 +72,41 @@ def main():
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st.markdown("<h1 style='font-size:20px;'>ChatBot by Muhammad Huzaifa</h1>", unsafe_allow_html=True)
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api_key = st.secrets["inference_api_key"]
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# Sidebar column for file upload
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with st.sidebar:
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st.header("Chat with PDF")
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pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True, type=["pdf"])
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# Main column for displaying extracted text and user interaction
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col1, col2 = st.columns([1, 2])
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if pdf_docs:
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with col1:
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if st.button("Submit"):
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with st.spinner("Processing..."):
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raw_text = get_pdf_text(pdf_docs)
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text_chunks = get_text_chunks(raw_text)
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get_vector_store(text_chunks, api_key)
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st.success("Processing Complete")
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# Check if PDF documents are uploaded and processing is complete
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if pdf_docs and raw_text:
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with col1:
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user_question = st.text_input("Ask a question from the Docs")
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if user_question:
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user_input(user_question, api_key)
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# Display extracted text if available
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if raw_text is not None:
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with col2:
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st.subheader("Extracted Text from PDF:")
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st.text(raw_text)
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# Show message if no PDF documents are uploaded
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if not pdf_docs:
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with col1:
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st.write("Please upload a document first to proceed.")
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import streamlit as st
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from streamlit.state.session_state import SessionState
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from PyPDF2 import PdfReader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_groq import ChatGroq
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st.markdown("<h1 style='font-size:20px;'>ChatBot by Muhammad Huzaifa</h1>", unsafe_allow_html=True)
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api_key = st.secrets["inference_api_key"]
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session_state = SessionState.get(pdf_docs=None, raw_text=None, processing_complete=False)
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# Sidebar column for file upload
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with st.sidebar:
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st.header("Chat with PDF")
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session_state.pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True, type=["pdf"])
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# Main column for displaying extracted text and user interaction
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col1, col2 = st.columns([1, 2])
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if session_state.pdf_docs:
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with col1:
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if st.button("Submit"):
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with st.spinner("Processing..."):
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session_state.raw_text = get_pdf_text(session_state.pdf_docs)
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text_chunks = get_text_chunks(session_state.raw_text)
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get_vector_store(text_chunks, api_key)
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st.success("Processing Complete")
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session_state.processing_complete = True
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# Check if PDF documents are uploaded and processing is complete
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if session_state.pdf_docs and session_state.raw_text and session_state.processing_complete:
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with col1:
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user_question = st.text_input("Ask a question from the Docs")
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if user_question:
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user_input(user_question, api_key)
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# Display extracted text if available
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if session_state.raw_text is not None:
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with col2:
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st.subheader("Extracted Text from PDF:")
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st.text(session_state.raw_text)
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# Show message if no PDF documents are uploaded
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if not session_state.pdf_docs:
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with col1:
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st.write("Please upload a document first to proceed.")
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