Update app.py

app.py

@@ -68,7 +68,6 @@ for name, filename in model_files.items():
     except:
         print(f"⚠️ Skipping {name}")
 
-# --- 3. PREDICTION FUNCTION ---
 # --- 3. PREDICTION FUNCTION ---
 def predict(text, model_name):
     if not text: return "Please enter text", {}, []
@@ -81,9 +80,10 @@ def predict(text, model_name):
         vectors_reduced = svd.transform(vectors)    # SVD Matrix (Dense)
         model = models[model_name]
         
-        # --- NEW: EXTRACT KEYWORDS ---
-        # We look at the TF-IDF vector (before SVD) to find the strongest words
+        # --- EXTRACT KEYWORDS ---
+        # We look at the TF-IDF vector to find the strongest words
         feature_array = np.array(vectorizer.get_feature_names_out())
+        # Sort by score (descending)
         tfidf_sorting = np.argsort(vectors.toarray()).flatten()[::-1]
         
         # Get top 10 words that actually have a score > 0
@@ -104,16 +104,16 @@ def predict(text, model_name):
             top_label = LABELS[pred_idx]
             confidences = {LABELS[pred_idx]: 1.0}
             
+        # Return 3 items: Label, Confidences, Keywords List
         return top_label, confidences, keywords
         
     except Exception as e:
         return f"Error: {str(e)}", {}, []
-
 # --- 4. LAUNCH ---
 # IMPORTANT: allowed_origins="*" fixes the 405 error
 demo = gr.Interface(
     fn=predict,
     inputs=[gr.Textbox(), gr.Dropdown(choices=list(models.keys()))],
-    outputs=[gr.Label(), gr.Label()]
+    outputs=[gr.Label(), gr.Label(), gr.JSON()]
 )
 demo.launch()