Update app.py

app.py

@@ -231,6 +231,15 @@ def visualize_ROC(set_threshold,set_input):
       f1_score_list.append(f1_score)
       g_mean_list.append(g_mean) 
     
+  # Find best thresholds
+  best_f1_idx = np.argmax(f1_score_list)
+  best_gmean_idx = np.argmax(g_mean_list)
+  
+  best_f1_threshold = thres_list[best_f1_idx]
+  best_gmean_threshold = thres_list[best_gmean_idx]
+  best_f1_value = f1_score_list[best_f1_idx]
+  best_gmean_value = g_mean_list[best_gmean_idx]
+  
   fig, ax = plt.subplots(figsize=(12,8))
 
   import matplotlib.pyplot as plt
@@ -240,17 +249,23 @@ def visualize_ROC(set_threshold,set_input):
   m1, c1 = 1, 0
   x = np.linspace(0, 1, 500)
 
+  # Plot curves
   plt.plot(thres_list, f1_score_list, label = 'F1-score', c='black', linestyle='-')
   plt.plot(thres_list, g_mean_list, label = 'G-mean', c='red', linestyle='-')
 
   plt.xlim(0, 1)
   plt.ylim(0, 1)
-  #xi = (c1 - c2) / (m2 - m1)
-  #yi = m1 * xi + c1
-  plt.axvline(x=set_threshold, color='gray', linestyle='--')
-  plt.axhline(y=f1_score_cur, color='gray', linestyle='--')
-  plt.scatter(set_threshold, f1_score_cur, color='red', s=300)
-  plt.scatter(set_threshold, g_mean_cur, color='red', s=300)
+  
+  # Mark current threshold (user selected)
+  plt.axvline(x=set_threshold, color='blue', linestyle=':', linewidth=2, alpha=0.5, label='Current threshold')
+  plt.scatter(set_threshold, f1_score_cur, color='blue', s=200, alpha=0.5, marker='o')
+  plt.scatter(set_threshold, g_mean_cur, color='blue', s=200, alpha=0.5, marker='o')
+  
+  # Mark BEST thresholds (optimal)
+  plt.scatter(best_f1_threshold, best_f1_value, color='black', s=400, marker='*', 
+              edgecolors='gold', linewidths=2, zorder=5, label=f'Best F1 (threshold={best_f1_threshold:.2f})')
+  plt.scatter(best_gmean_threshold, best_gmean_value, color='red', s=400, marker='*', 
+              edgecolors='gold', linewidths=2, zorder=5, label=f'Best G-mean (threshold={best_gmean_threshold:.2f})')
 
   ax.set_facecolor("white")
 
@@ -259,13 +274,24 @@ def visualize_ROC(set_threshold,set_input):
   ax.spines['left'].set_color('black')
   ax.spines['bottom'].set_color('black')
   ax.spines['top'].set_color('black')
-  ax.spines['right'].set_color('black')
+  ax.spines['right'].set_color='black')
   plt.xlabel('Threshold cut-off')
   plt.ylabel('F1-score & G-mean')
-  plt.legend(loc='upper left')
-  plt.text(set_threshold, f1_score_cur, 'F1-score:%s' % (round(f1_score_cur,2)))
-  plt.text(set_threshold, g_mean_cur, 'G-mean:%s' % (round(g_mean_cur,2)))
-  plt.title("Threshold tuning curves (F1-score & G-mean)", fontsize=20)
+  plt.legend(loc='upper right', fontsize=10)
+  
+  # Add text annotations for best values
+  plt.text(best_f1_threshold, best_f1_value + 0.03, f'Best F1: {best_f1_value:.2f}', 
+           ha='center', fontsize=10, bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
+  plt.text(best_gmean_threshold, best_gmean_value + 0.03, f'Best G-mean: {best_gmean_value:.2f}', 
+           ha='center', fontsize=10, bbox=dict(boxstyle='round', facecolor='lightcoral', alpha=0.5))
+  
+  # Add text annotations for current values
+  plt.text(set_threshold, f1_score_cur - 0.05, f'Current F1: {f1_score_cur:.2f}', 
+           ha='center', fontsize=9, color='blue', alpha=0.7)
+  plt.text(set_threshold, g_mean_cur - 0.05, f'Current G-mean: {g_mean_cur:.2f}', 
+           ha='center', fontsize=9, color='blue', alpha=0.7)
+  
+  plt.title("Threshold tuning curves (F1-score & G-mean)\nGold stars mark optimal thresholds", fontsize=20)
   plt.tight_layout()
 
   plt.savefig('tmp3.png', dpi=100)
@@ -301,10 +327,10 @@ interface = gr.Interface(fn=visualize_ROC,
                               [0.7,get_example()],
                           ],
                          title="ML Demo for Receiver Operating Characteristic (ROC) curve", 
-                         description= "Click examples below for a quick demo",
+                         description= "Click examples below for a quick demo. Gold stars show optimal F1 and G-mean thresholds.",
                          theme = 'huggingface',
                          #layout = 'horizontal',
                          )
 
 
-interface.launch(debug=True, height=1400, width=2800)
+interface.launch(debug=True, height=1400, width=2800)