import gradio as gr import pandas as pd def data_pre_processing(file_responses): # Financial Weights can be anything (ultimately the row-wise weights are aggregated and the corresponding fractions are obtained from that rows' total tax payed) try: # Define the columns to be processed # Developing Numeric Columns # Convert columns to numeric and fill NaN values with 0 file_responses['Personal_TaxDirection_1_TaxWeightageAllocated'] = pd.to_numeric(file_responses['Personal_TaxDirection_1_TaxWeightageAllocated'], errors='coerce').fillna(0) file_responses['Personal_TaxDirection_2_TaxWeightageAllocated'] = pd.to_numeric(file_responses['Personal_TaxDirection_2_TaxWeightageAllocated'], errors='coerce').fillna(0) file_responses['Personal_TaxDirection_3_TaxWeightageAllocated'] = pd.to_numeric(file_responses['Personal_TaxDirection_3_TaxWeightageAllocated'], errors='coerce').fillna(0) file_responses['Latest estimated Tax payment?'] = pd.to_numeric(file_responses['Latest estimated Tax payment?'], errors='coerce').fillna(0) # Adding a new column 'TotalWeightageAllocated' by summing specific columns by their names file_responses['TotalWeightageAllocated'] = file_responses['Personal_TaxDirection_1_TaxWeightageAllocated'] + file_responses['Personal_TaxDirection_2_TaxWeightageAllocated'] + file_responses['Personal_TaxDirection_3_TaxWeightageAllocated'] # Creating Datasets (we assume everything has been provided to us in English, or the translations have been done already) # Renaming the datasets into similar column headings initial_dataset_1 = file_responses.rename(columns={ 'Personal_TaxDirection_1_Wish': 'Problem_Description', 'Personal_TaxDirection_1_GeographicalLocation': 'Geographical_Location', 'Personal_TaxDirection_1_TaxWeightageAllocated': 'Financial_Weight' })[['Problem_Description', 'Geographical_Location', 'Financial_Weight']] initial_dataset_2 = file_responses.rename(columns={ 'Personal_TaxDirection_2_Wish': 'Problem_Description', 'Personal_TaxDirection_2_GeographicalLocation': 'Geographical_Location', 'Personal_TaxDirection_2_TaxWeightageAllocated': 'Financial_Weight' })[['Problem_Description', 'Geographical_Location', 'Financial_Weight']] initial_dataset_3 = file_responses.rename(columns={ 'Personal_TaxDirection_3_Wish': 'Problem_Description', 'Personal_TaxDirection_3_GeographicalLocation': 'Geographical_Location', 'Personal_TaxDirection_3_TaxWeightageAllocated': 'Financial_Weight' })[['Problem_Description', 'Geographical_Location', 'Financial_Weight']] # Calculating the actual TaxAmount to be allocated against each WISH (by overwriting the newly created columns) initial_dataset_1['Financial_Weight'] = file_responses['Personal_TaxDirection_1_TaxWeightageAllocated'] * file_responses['Latest estimated Tax payment?'] / file_responses['TotalWeightageAllocated'] initial_dataset_2['Financial_Weight'] = file_responses['Personal_TaxDirection_2_TaxWeightageAllocated'] * file_responses['Latest estimated Tax payment?'] / file_responses['TotalWeightageAllocated'] initial_dataset_3['Financial_Weight'] = file_responses['Personal_TaxDirection_3_TaxWeightageAllocated'] * file_responses['Latest estimated Tax payment?'] / file_responses['TotalWeightageAllocated'] # Removing useless rows # Drop rows where Problem_Description is NaN or an empty string initial_dataset_1 = initial_dataset_1.dropna(subset=['Problem_Description'], axis=0) initial_dataset_2 = initial_dataset_2.dropna(subset=['Problem_Description'], axis=0) initial_dataset_3 = initial_dataset_3.dropna(subset=['Problem_Description'], axis=0) # Convert 'Problem_Description' column to string type initial_dataset_1['Problem_Description'] = initial_dataset_1['Problem_Description'].astype(str) initial_dataset_2['Problem_Description'] = initial_dataset_2['Problem_Description'].astype(str) initial_dataset_3['Problem_Description'] = initial_dataset_3['Problem_Description'].astype(str) # Merging the Datasets # Vertically concatenating (merging) the 3 DataFrames merged_dataset = pd.concat([initial_dataset_1, initial_dataset_2, initial_dataset_3], ignore_index=True) # Different return can be used to check the processing # return file_responses return merged_dataset 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_ProjectProposals.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)_BasicExample.xlsx', '#TaxDirection (Responses)_IntermediateExample.xlsx', '#TaxDirection (Responses)_UltimateExample.xlsx' ] import random a_random_object = random.choice(["⇒", "↣", "↠", "→"]) # 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 here. \t Be sure to check that the column headings in your upload are the same as in the Example files below. \t (Otherwise there will be Error during the processing)"), # 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="Download the processed Excel File containing the ** Project Proposals ** for each Location~Problem paired combination"), # File download output # title="Excel File Uploader", # title="Upload Excel file containing #TaxDirections → Download HyperLocal Project Proposals\n", title = ( "

" "Upload Excel file containing #TaxDirections " # "→ " # "⇒ ↣ ↠ " " " +a_random_object +" " "Download HyperLocal Project Proposals" "

\n" ), description=( "

This tool allows for the systematic evaluation and proposal of solutions tailored to specific location-problem pairs, ensuring efficient resource allocation and project planning. For more information, visit #TaxDirection weblink.

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Upload an Excel file to process and download the result or use the Example files:

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(click on any of them to directly process the file and Download the result)

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Processed output contains a Project Proposal for each Location~Problem paired combination (i.e. each cell).

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Corresponding Budget Allocation and estimated Project Completion Time are provided in different sheets.

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Note: The example files provided above are for demonstration purposes. Feel free to upload your own Excel files to see the results. If you have any questions, refer to the documentation-links or contact support.

" ) # Solid description with right-aligned second sentence ) # Launch the interface if __name__ == "__main__": interface.launch()