app.py

import gradio as gr
import pandas as pd
from utils import create_docs

def process_invoices(file_list):
    if not file_list:
        return "No files uploaded. Please upload at least one PDF invoice.", None

    try:
        df = create_docs(file_list)
        if not isinstance(df, pd.DataFrame):
            return "Error: The extracted data is not in the expected format.", None
        if df.empty:
            return "No data extracted from the PDFs.", None
        return "Data extraction completed! 🎉", df
    except Exception as e:
        return f"Error processing PDFs: {str(e)}", None

demo = gr.Interface(
    fn=process_invoices,
    inputs=gr.File(
        file_types=[".pdf"],
        file_count="multiple",
        label="Upload PDF invoices"
    ),
    outputs=[
        gr.Textbox(label="Status"),
        gr.Dataframe(label="Extracted Invoice Data")
    ],
    title="Invoice Extraction Bot 🤖",
    description="Upload your PDF invoices to extract key information like invoice number, date, items, totals, and contact info.",
    allow_flagging="never"
)

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",  # Bind to all interfaces for local deployment
        server_port=7860,       # Default Gradio port
        show_error=True         # Show detailed errors in the UI
    )