Spaces:
Runtime error
Runtime error
File size: 7,062 Bytes
cae67f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | # import streamlit as st
# import pandas as pd
# import tempfile
# import os
# from PDFSectionExtractor import PDFSectionExtractor, convert_pdf_to_excel
# def main():
# st.title("PDF Section and Table Extractor")
# st.write("Upload a PDF file to extract sections and tables into an Excel workbook.")
# # File uploader
# uploaded_file = st.file_uploader("Choose a PDF file", type=['pdf'])
# 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("Processing PDF..."):
# # Create extractor instance
# extractor = PDFSectionExtractor(pdf_path)
# # Get document info
# doc_name, doc_id = extractor.get_document_info()
# st.write(f"Document Name: {doc_name}")
# st.write(f"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(f"Successfully processed PDF!")
# st.write(f"Found {len(tables)} tables and {len(sections)} sections.")
# # Preview sections
# if sections:
# st.subheader("Sections Preview")
# df_sections = pd.DataFrame(sections)
# st.dataframe(df_sections)
# # Preview tables
# if tables:
# st.subheader("Tables Preview")
# for i, table in enumerate(tables, 1):
# with st.expander(f"Table {i}"):
# st.dataframe(table)
# # Provide download button for Excel file
# with open(excel_path, "rb") as f:
# excel_data = f.read()
# st.download_button(
# label="Download Excel File",
# data=excel_data,
# file_name=excel_filename,
# mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
# )
# except Exception as e:
# st.error(f"An error occurred: {str(e)}")
# if __name__ == "__main__":
# 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()
|