app.py

import gradio as gr
import pandas as pd

def data_pre_processing(file_responses):
    # Financial Weights are in per decas and NOT per cents
    try:
        # Define the columns to be processed
        columns = [
            '''Your financial allocation for Problem 1:
            Mention the percentage of your Tax Amount which you wish the Government would allocate through their annual budget, to implement a specific solution for your 1st problem.''',
            '''Your financial allocation for Problem 2:
            Mention the percentage of your Tax Amount which you wish the Government would allocate through their annual budget, to implement a solution specifically to your 2nd problem.''',
            '''Your financial allocation for Problem 3:
            Mention the percentage of your Tax Amount which you wish the Government would allocate through their annual budget, to implement a solution specifically to your 3rd problem.'''
        ]
        
        # # Convert columns to numeric and fill NaN values with 0
        # for col in columns:
        #     file_responses[col] = pd.to_numeric(file_responses[col], errors='coerce').fillna(0)
        
        # # Calculate the Total Allocation
        # file_responses['Total Allocation'] = file_responses[columns].sum(axis=1)
        
        # # Convert the Tax Payment column to numeric
        # tax_payment_col = '''How much was your latest Tax payment (in U$D) ?
        
        # Please try to be as accurate as possible:
        # Eg.: If your last tax amount was INR 25,785/-; then convert it in U$D and enter only the amount as: 310.
        
        # If you have never paid tax, consider putting in a realistic donation amount which wish to contribute towards helping yourself obtain the desired relief.'''
        
        # file_responses[tax_payment_col] = pd.to_numeric(file_responses[tax_payment_col], errors='coerce').fillna(0)
        
        # # Calculate Financial Token Weights
        # for i, col in enumerate(columns, start=1):
        #     file_responses[f'Financial Token Weight for Problem {i}'] = (
        #         file_responses[tax_payment_col] * file_responses[col] / file_responses['Total Allocation']
        #     ).fillna(0)
        
        return file_responses
    except Exception as e:
        return str(e)

def nlp_pipeline(original_df):
    processed_df = data_pre_processing(original_df)
    return processed_df

def process_excel(file):
    try:
        # Ensure the file path is correct
        file_path = file.name if hasattr(file, 'name') else file
        # Read the Excel file
        df = pd.read_excel(file_path)
        
        # Process the DataFrame
        result_df = nlp_pipeline(df)

        output_file = "OutPut_file.xlsx"
        result_df.to_excel(output_file, index=False)
        
        return output_file  # Return the processed DataFrame as Excel file
        
    except Exception as e:
        return str(e)  # Return the error message






example_files = ['#TaxDirection (Responses)_Example1.xlsx', ]
    
# Define the Gradio interface
interface = gr.Interface(
    fn=process_excel,  # The function to process the uploaded file
    inputs=gr.File(type="filepath", label="Upload Excel File"),  # File upload input
    
    examples=example_files,  # Add the example files
    
    # outputs=gr.File(label="Download Processed Excel File"),  # File download output
    outputs=gr.File(label="<p style='font-weight: bold; font-size: 16px;'> Download Processed Excel File containing the Project Proposals for each Location~Problem paired combination </p>"),  # File download output
    
    
    # title="Excel File Uploader",
    # description="Upload an Excel file to process and download the result.",
    # description="Upload an Excel file to process and download the result or use the example files (click on anyone of them)."

    title="<h1 style='font-weight: bold;'>Excel File Uploader</h1>",  # Solid title
    description="<p style='font-weight: bold; font-size: 16px;'>Upload an Excel file to process and download the result or use the example files.</p>",  # Solid description
)


# Launch the interface
if __name__ == "__main__":
    interface.launch()