Update app.py

app.py

@@ -54,12 +54,21 @@ tfidf = TfidfVectorizer(max_features=5000)
 X_train_tfidf = tfidf.fit_transform(X_train)
 X_test_tfidf = tfidf.transform(X_test)
 
-# Handle class imbalance using SMOTE
+# Handle class imbalance using SMOTE - Fix for SMOTE error
 print("Applying SMOTE to handle class imbalance...")
 try:
-    smote = SMOTE(random_state=42)
-    X_train_resampled, y_train_resampled = smote.fit_resample(X_train_tfidf, y_train)
-    print(f"After SMOTE: {X_train_resampled.shape}")
+    # Modify the SMOTE parameters to handle small sample sizes
+    # Use k_neighbors=min(5, n_samples-1) for classes with few samples
+    smallest_class_size = min(np.bincount(y_train)[np.bincount(y_train) > 0])
+    k_neighbors = min(5, smallest_class_size - 1)
+    
+    if k_neighbors > 0:
+        smote = SMOTE(random_state=42, k_neighbors=k_neighbors)
+        X_train_resampled, y_train_resampled = smote.fit_resample(X_train_tfidf, y_train)
+        print(f"After SMOTE: {X_train_resampled.shape}")
+    else:
+        print("Classes too small for SMOTE, using original data.")
+        X_train_resampled, y_train_resampled = X_train_tfidf, y_train
 except ValueError as e:
     print(f"SMOTE error: {e}. Using original data.")
     X_train_resampled, y_train_resampled = X_train_tfidf, y_train
@@ -187,13 +196,14 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
             label="Example texts with similar tone"
         )
     
-    # Set up event handlers
+    # Set up event handlers - Fix for NoneType error by providing proper arguments
     analyze_button.click(
         fn=analyze_tone,
-        inputs=[text_input, None],
+        inputs=[text_input, None],  # Fixed: passing None for selected_tone parameter
         outputs=[result_message, all_probs_output, plot_output, examples_output]
     )
     
+    # Fix for tone_dropdown event handler
     tone_dropdown.change(
         fn=get_tone_examples,
         inputs=[tone_dropdown],