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| 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 | |
| # 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 | |
| outputs=gr.File(label="Download Processed Excel File"), # File download output | |
| title="Excel File Uploader", | |
| description="Upload an Excel file to process and download the result." | |
| # outputs="dataframe", # Display the output as a DataFrame | |
| # title="Excel File Uploader", | |
| # description="Upload an Excel file to see the first few rows." | |
| ) | |
| # Launch the interface | |
| if __name__ == "__main__": | |
| interface.launch() | |