runtime error

app.py

@@ -18,7 +18,7 @@ logger.info("Initializing emotion classification pipeline...")
 classifier = pipeline(
     "text-classification",
     model="bhadresh-savani/distilbert-base-uncased-emotion",
-    return_all_scores=True
+    top_k=None  # Return all scores
 )
 logger.info("Pipeline initialized successfully")
 
@@ -29,11 +29,12 @@ def predict_emotion(text):
         
         if not text:
             logger.warning("Empty text received")
-            data = {
-                'label': ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'],
-                'score': [0, 0, 0, 0, 0, 0]
-            }
-            return pd.DataFrame(data)
+            return gr.BarPlot.update(
+                value=pd.DataFrame({
+                    'label': ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'],
+                    'score': [0, 0, 0, 0, 0, 0]
+                })
+            )
         
         # Get predictions
         logger.info("Running prediction...")
@@ -44,18 +45,20 @@ def predict_emotion(text):
         sorted_predictions = sorted(predictions, key=lambda x: x['score'], reverse=True)
         
         # Create DataFrame with the correct format
-        data = {
-            'label': [pred['label'] for pred in sorted_predictions],
-            'score': [pred['score'] for pred in sorted_predictions]
-        }
-        df = pd.DataFrame(data)
+        df = pd.DataFrame(sorted_predictions)
+        df.columns = ['label', 'score']  # Rename columns to match expected format
         logger.info(f"Processed scores:\n{df}")
         
-        return df
+        return gr.BarPlot.update(value=df)
         
     except Exception as e:
         logger.error(f"Error in prediction: {str(e)}")
-        return pd.DataFrame({'label': ['error'], 'score': [1.0]})
+        return gr.BarPlot.update(
+            value=pd.DataFrame({
+                'label': ['error'],
+                'score': [1.0]
+            })
+        )
 
 # Create the Gradio interface
 demo = gr.Interface(
@@ -66,12 +69,16 @@ demo = gr.Interface(
         lines=4
     ),
     outputs=gr.BarPlot(
-        label="emotion",
-        value="probability",
+        value=pd.DataFrame({
+            'label': ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'],
+            'score': [0, 0, 0, 0, 0, 0]
+        }),
+        x="label",
+        y="score",
         title="Emotion Probabilities",
-        height=400,
         color="#2563eb",
-        show_label=True
+        height=400,
+        vertical=True
     ),
     title="Emotion Detection with DistilBERT",
     description="This app uses the DistilBERT model fine-tuned for emotion detection. Enter any text to analyze its emotional content.",