Update app.py

app.py

@@ -26,7 +26,7 @@ def beam_prediction_task(data_percentage, task_complexity, theme):
         raw_cm_path = os.path.join(raw_folder, "confusion_matrix_raw.png")
         plot_confusion_matrix_beamPred(raw_cm, 
                                        classes=np.arange(raw_cm.shape[0]), 
-                                       title=f"Raw Confusion Matrix\n({data_percentage}% data, {task_complexity} beams)", 
+                                       title=f"Raw Confusion Matrix\n{data_percentage}% data, {task_complexity} beams", 
                                        save_path=raw_cm_path, 
                                        theme=theme)
         raw_img = Image.open(raw_cm_path)
@@ -39,7 +39,7 @@ def beam_prediction_task(data_percentage, task_complexity, theme):
         embeddings_cm_path = os.path.join(embeddings_folder, "confusion_matrix_embeddings.png")
         plot_confusion_matrix_beamPred(embeddings_cm, 
                                        classes=np.arange(embeddings_cm.shape[0]), 
-                                       title=f"Embeddings Confusion Matrix\n({data_percentage}% data, {task_complexity} beams)", 
+                                       title=f"Embeddings Confusion Matrix\n{data_percentage}% data, {task_complexity} beams", 
                                        save_path=embeddings_cm_path, 
                                        theme=theme)
         embeddings_img = Image.open(embeddings_cm_path)
@@ -76,7 +76,8 @@ def plot_confusion_matrix_beamPred(cm, classes, title, save_path, theme='Light')
         plt.style.use('dark_background')  # Use dark mode styling
         #text_color = 'white'
         text_color = 'gray'
-        cmap = 'cividis'  # Dark-mode-friendly colormap
+        #cmap = 'cividis'  # Dark-mode-friendly colormap
+        cmap = 'coolwarm'
     else:
         plt.style.use('default')  # Use default (light) mode styling
         #text_color = 'black'
@@ -86,10 +87,10 @@ def plot_confusion_matrix_beamPred(cm, classes, title, save_path, theme='Light')
     plt.figure(figsize=(10, 10))
 
     # Plot the confusion matrix with a colormap compatible for the mode
-    sns.heatmap(cm, cmap=cmap, cbar=True, linecolor=text_color, vmin=0, vmax=cm.max(), alpha=0.85)
+    sns.heatmap(cm, cmap=cmap, cbar=True, linecolor=text_color ) #, vmin=0, vmax=cm.max(), alpha=0.85)
 
     # Add F1-score to the title
-    plt.title(f"{title}\n(F1 Score: {avg_f1:.3f})", color=text_color, fontsize=20)
+    plt.title(f"{title}\nF1 Score: {avg_f1:.3f}", color=text_color, fontsize=23)
 
     tick_marks = np.arange(len(classes))
     plt.xticks(tick_marks, classes, color=text_color, fontsize=14)  # Adjust text color based on the mode
@@ -574,16 +575,17 @@ with gr.Blocks(css="""
             with gr.Column():
                 data_percentage_slider = gr.Slider(label="Data Percentage for Training", minimum=10, maximum=100, step=10, value=10)
                 task_complexity_dropdown = gr.Dropdown(label="Task Complexity (Number of Beams)", choices=[16, 32, 64, 128, 256], value=16)
-                theme_dropdown = gr.Dropdown(label="Select Theme", choices=['Light', 'Dark'], value='Light')
+                #theme_dropdown = gr.Dropdown(label="Select Theme", choices=['Light', 'Dark'], value='Light')
 
         with gr.Row():
             raw_img_bp = gr.Image(label="Raw Channels", type="pil", width=300, height=500)
             embeddings_img_bp = gr.Image(label="Embeddings", type="pil", width=300, height=500)
-    
+
+        theme_dropdown = 'Dark'
         # Update the confusion matrices whenever sliders change
         data_percentage_slider.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown, theme_dropdown], outputs=[raw_img_bp, embeddings_img_bp])
         task_complexity_dropdown.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown, theme_dropdown], outputs=[raw_img_bp, embeddings_img_bp])
-        theme_dropdown.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown, theme_dropdown], outputs=[raw_img_bp, embeddings_img_bp])
+        #theme_dropdown.change(fn=beam_prediction_task, inputs=[data_percentage_slider, task_complexity_dropdown, theme_dropdown], outputs=[raw_img_bp, embeddings_img_bp])
         
         # Add a conclusion section at the bottom
         gr.Markdown("""