Update app.py

app.py

@@ -28,16 +28,17 @@ def predict_tags_auto(title, description, threshold=0.2):
     # Get probabilities for each tag
     probas = model.predict_proba(input_vector)
 
-    # Combine probabilities from each classifier
-    probas_array = np.array([class_proba[0] for class_proba in probas])  # shape: (n_classes,)
+    # Get the probability for class=1 (relevant tag) for each classifier
+    probas_array = np.array([p[:, 1][0] for p in probas])  # (n_classes,)
 
     # Apply threshold
     predicted_binary = (probas_array >= threshold).astype(int).reshape(1, -1)
 
-    # Convert binary to tags
+    # Convert binary vector to tags
     tags = mlb.inverse_transform(predicted_binary)
     return tags[0] if tags else []
 
+
 if st.button("Predict Tags"):
     if not title.strip() or not description.strip():
         st.warning("⚠️ Please enter both title and description.")