added gradio app

app.py

@@ -0,0 +1,107 @@
+#!/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
+    matrix_display = gr.Dataframe(
+        value=generate_random_matrix(),
+        interactive=True,
+        label="Matrix (cells in max submatrix will be highlighted in green)"
+    )
+    
+    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)

requirements.txt

@@ -1,3 +1,5 @@
 numpy
 scipy
-pytest
+pytest
+gradio
+pandas