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import gradio as gr
import fitz # PyMuPDF
import pandas as pd
# Function to convert PDF to DataFrame
def pdf_to_dataframe(pdf_path):
# Open the PDF document
doc = fitz.open(pdf_path)
# Initialize an empty list to store text blocks
text_blocks = []
# Iterate through each page in the PDF
for page_num in range(len(doc)):
page = doc.load_page(page_num)
text = page.get_text("text")
text_blocks.append(text)
# Join all text blocks into a single string
full_text = "\n".join(text_blocks)
# Split the text into lines
lines = full_text.split('\n')
# Create a DataFrame from the lines
df = pd.DataFrame(lines, columns=['Text'])
return df
# Function to save DataFrame to Excel
def dataframe_to_excel(df, excel_path):
# Save the DataFrame to an Excel file
df.to_excel(excel_path, index=False)
# Main function
def main():
def pdf_to_excel_function(pdf_file):
# Save the uploaded PDF to a temporary file
pdf_path = "temp.pdf"
with open(pdf_path, "wb") as f:
f.write(pdf_file.read())
# Convert PDF to DataFrame
df = pdf_to_dataframe(pdf_path)
# Save DataFrame to Excel
excel_path = "output.xlsx"
dataframe_to_excel(df, excel_path)
return excel_path
# Create the Gradio interface
iface = gr.Interface(
fn=pdf_to_excel_function,
inputs=gr.File(label="Upload PDF File"),
outputs=gr.File[label="Download Excel File"],
title="PDF to Excel Converter",
description="Convert a PDF file to an Excel file."
)
# Launch the interface
iface.launch()
if __name__ == "__main__":
main() |