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| #!/usr/bin/env python | |
| # filepath: /Users/tristanhearn/code/maximum-submatrix-sum/app.py | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| from algorithms import brute_submatrix_max, fft_submatrix_max, kidane_max_submatrix | |
| # Function to generate a random 10x10 matrix with numbers having 1 decimal place | |
| def generate_random_matrix(rows=10, cols=10): | |
| # Generate random floats between -10 and 10 with 1 decimal place | |
| matrix = np.round(np.random.uniform(-10, 10, size=(rows, cols)), 1) | |
| # Convert to Pandas DataFrame | |
| df = pd.DataFrame(matrix) | |
| return df | |
| # Function to process the matrix and find the maximum submatrix sum | |
| def process_matrix(matrix_df, algorithm): | |
| try: | |
| # Convert input to numpy array | |
| matrix_array = matrix_df.values.astype(float) | |
| # Select the appropriate algorithm | |
| if algorithm == "Brute Force": | |
| loc, max_sum, time_taken = brute_submatrix_max(matrix_array) | |
| elif algorithm == "FFT": | |
| loc, max_sum, time_taken = fft_submatrix_max(matrix_array) | |
| else: # Kidane method | |
| loc, max_sum, time_taken = kidane_max_submatrix(matrix_array) | |
| print(f"Using algorithm: {algorithm}") | |
| # Format the result message | |
| result_message = ( | |
| f"Algorithm used: {algorithm}\n" | |
| f"Maximum Submatrix Sum: {max_sum:.2f}\n" | |
| f"Time taken: {time_taken:.6f} seconds\n" | |
| f"Submatrix location: Rows {loc[0].start} to {loc[0].stop-1}, Columns {loc[1].start} to {loc[1].stop-1}" | |
| ) | |
| # Create a styled DataFrame with highlighted cells | |
| df = pd.DataFrame(matrix_array) | |
| # Create a mask for the maximum submatrix | |
| mask = pd.DataFrame(np.zeros_like(matrix_array, dtype=bool)) | |
| mask.iloc[loc[0], loc[1]] = True | |
| # Apply background color based on the mask | |
| def highlight_max_submatrix(val): | |
| color = 'background-color: #90EE90' # Light green | |
| default = '' | |
| return np.where(mask, color, default) | |
| # Style the DataFrame with the highlighting | |
| styled_df = df.style.apply(highlight_max_submatrix, axis=None) | |
| # Attempt to render styled DataFrame to HTML using to_html, fallback on string conversion if necessary | |
| try: | |
| html_output = styled_df.to_html() | |
| except Exception as e: | |
| html_output = str(styled_df) | |
| return html_output, result_message | |
| except Exception as e: | |
| print(f"Error in process_matrix: {e}") | |
| return matrix_df, str(e) | |
| # Initialize Gradio interface | |
| with gr.Blocks(title="Maximum Submatrix Sum Calculator") as app: | |
| gr.Markdown("# Maximum Submatrix Sum Calculator") | |
| gr.Markdown("Edit the matrix below or use the random generator, then select an algorithm to find the maximum sum submatrix.") | |
| random_matrix_btn = gr.Button("Generate New Random Matrix") | |
| # Use a dataframe component for the matrix input/output | |
| # Generate initial matrix and headers | |
| initial_matrix = generate_random_matrix() | |
| empty_headers = [""] * initial_matrix.shape[1] | |
| matrix_display = gr.Dataframe( | |
| value=initial_matrix, | |
| interactive=True, | |
| label="Matrix (cells in max submatrix will be highlighted in green)", | |
| headers=empty_headers | |
| ) | |
| highlighted_matrix = gr.HTML(label="Highlighted Matrix") | |
| with gr.Row(): | |
| algorithm = gr.Radio( | |
| ["Brute Force", "FFT", "Kidane"], | |
| value="FFT", | |
| label="Algorithm" | |
| ) | |
| with gr.Row(): | |
| submit_btn = gr.Button("Find Maximum Submatrix Sum", variant="primary") | |
| result_text = gr.Textbox(label="Results", lines=3) | |
| # Event handlers | |
| random_matrix_btn.click(generate_random_matrix, outputs=[matrix_display]) | |
| submit_btn.click( | |
| process_matrix, | |
| inputs=[matrix_display, algorithm], | |
| outputs=[highlighted_matrix, result_text] | |
| ) | |
| # Print a message before launching | |
| print("Launching Gradio app for Maximum Submatrix Sum Calculator...") | |
| # Run the app | |
| if __name__ == "__main__": | |
| app.launch(show_error=True) | |