main()

import streamlit as st
import pandas as pd
import tempfile
import os
from PDFSectionExtractor import PDFSectionExtractor, convert_pdf_to_excel
# Add page config
st.set_page_config(
    page_title="PDF Section and Table Extractor",
    page_icon=":page_facing_up:",
    layout="wide"
)
# Add custom CSS to improve the interface
st.markdown("""
    <style>
    .stButton>button {
        width: 100%;
    }
    .css-1v0mbdj.e115fcil1 {
        max-width: 100%;
    }
    </style>
    """, unsafe_allow_html=True)
def main():
    st.title(":page_facing_up: PDF Section and Table Extractor")
    st.write("Upload a PDF file to extract sections and tables into an Excel workbook.")
    # Add some usage instructions
    with st.expander(":information_source: How to use"):
        st.write("""
        1. Upload a PDF file using the file uploader below
        2. Wait for the processing to complete
        3. Preview the extracted sections
        4. Download the results as an Excel file
        """)
    # File uploader with additional information
    uploaded_file = st.file_uploader(
        "Choose a PDF file",
        type=['pdf'],
        help="Upload a PDF file containing sections and tables to extract"
    )
    if uploaded_file is not None:
        # Create a temporary directory to store the files
        with tempfile.TemporaryDirectory() as temp_dir:
            # Save the uploaded PDF
            pdf_path = os.path.join(temp_dir, uploaded_file.name)
            with open(pdf_path, "wb") as f:
                f.write(uploaded_file.getvalue())
            # Create the Excel output path
            excel_filename = os.path.splitext(uploaded_file.name)[0] + "_extracted.xlsx"
            excel_path = os.path.join(temp_dir, excel_filename)
            try:
                with st.spinner(":arrows_counterclockwise: Processing PDF... This may take a moment."):
                    # Create extractor instance
                    extractor = PDFSectionExtractor(pdf_path)
                    # Get document info
                    doc_name, doc_id = extractor.get_document_info()
                    # Display document info in a nice format
                    col1, col2 = st.columns(2)
                    with col1:
                        st.info(f":memo: Document Name: {doc_name}")
                    with col2:
                        st.info(f":key: Document ID: {doc_id}")
                    # Extract tables and sections
                    tables = extractor.extract_tables()
                    sections = extractor.extract_sections()
                    # Create Excel file
                    convert_pdf_to_excel(pdf_path, excel_path)
                    # Show summary
                    st.success(":white_check_mark: Successfully processed PDF!")
                    st.write(f":bar_chart: Found {len(tables)} tables and :bookmark_tabs: {len(sections)} sections.")
                    # Display sections preview
                    if sections:
                        st.subheader("Sections Preview")
                        df_sections = pd.DataFrame(sections)
                        st.dataframe(df_sections, use_container_width=True)
                    else:
                        st.warning("No sections found in the document.")
                    # Provide download button for Excel file
                    with open(excel_path, "rb") as f:
                        excel_data = f.read()
                    st.download_button(
                        label=":inbox_tray: Download Excel File",
                        data=excel_data,
                        file_name=excel_filename,
                        mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
                        key="download-excel"
                    )
            except Exception as e:
                st.error(f":x: An error occurred: {str(e)}")
                st.write("Please make sure your PDF file is valid and try again.")
if __name__ == "__main__":
    main()