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Chandranshu Jain
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Update app.py
Browse files
app.py
CHANGED
@@ -15,7 +15,7 @@ from langchain_cohere import CohereEmbeddings
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st.set_page_config(page_title="Document Genie", layout="wide")
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st.markdown("""
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##
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This chatbot is built using the Retrieval-Augmented Generation (RAG) framework, leveraging Google's Generative AI model Gemini-PRO. It processes uploaded PDF documents by breaking them down into manageable chunks, creates a searchable vector store, and generates accurate answers to user queries. This advanced approach ensures high-quality, contextually relevant responses for an efficient and effective user experience.
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@@ -23,9 +23,9 @@ This chatbot is built using the Retrieval-Augmented Generation (RAG) framework,
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Follow these simple steps to interact with the chatbot:
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1. **Upload Your
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2. **Ask a Question**: After processing the
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""")
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#def get_pdf(pdf_docs):
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@@ -95,7 +95,7 @@ def main():
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st.header("Chat with your pdf💁")
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st.title("Menu:")
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pdf_docs = st.file_uploader("Upload your PDF File and Click on the Submit & Process Button", accept_multiple_files=False, key="pdf_uploader")
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query = st.text_input("Ask a Question from the PDF
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if st.button("Submit & Process", key="process_button"):
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with st.spinner("Processing..."):
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raw_text = get_pdf(pdf_docs)
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st.set_page_config(page_title="Document Genie", layout="wide")
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st.markdown("""
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## PDFChat: Get instant insights from your PDF
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This chatbot is built using the Retrieval-Augmented Generation (RAG) framework, leveraging Google's Generative AI model Gemini-PRO. It processes uploaded PDF documents by breaking them down into manageable chunks, creates a searchable vector store, and generates accurate answers to user queries. This advanced approach ensures high-quality, contextually relevant responses for an efficient and effective user experience.
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Follow these simple steps to interact with the chatbot:
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1. **Upload Your Document**: The system accepts a PDF file at one time, analyzing the content to provide comprehensive insights.
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2. **Ask a Question**: After processing the document, ask any question related to the content of your uploaded document for a precise answer.
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""")
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#def get_pdf(pdf_docs):
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st.header("Chat with your pdf💁")
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st.title("Menu:")
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pdf_docs = st.file_uploader("Upload your PDF File and Click on the Submit & Process Button", accept_multiple_files=False, key="pdf_uploader")
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query = st.text_input("Ask a Question from the PDF File", key="query")
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if st.button("Submit & Process", key="process_button"):
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with st.spinner("Processing..."):
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raw_text = get_pdf(pdf_docs)
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