working on the bar

app.py

@@ -29,30 +29,27 @@ def predict_emotion(text):
         
         if not text:
             logger.warning("Empty text received")
-            return pd.DataFrame({
-                'emotion': ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'],
-                'score': [0, 0, 0, 0, 0, 0]
-            })
+            return None
         
         # Get predictions
         logger.info("Running prediction...")
         predictions = classifier(text)[0]
         logger.info(f"Raw predictions: {predictions}")
         
-        # Convert to DataFrame and format for visualization
-        df = pd.DataFrame(predictions)
-        df.columns = ['emotion', 'score']
-        df = df.sort_values('score', ascending=True)
-        logger.info(f"Processed scores as DataFrame: {df.to_dict()}")
+        # Sort predictions by score in descending order
+        predictions.sort(key=lambda x: x['score'], reverse=True)
         
-        return df
+        # Extract labels and scores
+        labels = [p['label'] for p in predictions]
+        scores = [p['score'] for p in predictions]
+        
+        logger.info(f"Processed scores: {dict(zip(labels, scores))}")
+        
+        return (labels, scores)
         
     except Exception as e:
         logger.error(f"Error in prediction: {str(e)}")
-        return pd.DataFrame({
-            'emotion': ['error'],
-            'score': [1.0]
-        })
+        return None
 
 # Create the Gradio interface
 demo = gr.Interface(
@@ -63,21 +60,20 @@ demo = gr.Interface(
         lines=4
     ),
     outputs=gr.BarPlot(
-        title="Emotion Probabilities",
-        x="emotion",
         y="score",
-        xlabel="Emotion",
-        ylabel="Probability",
-        height=400
+        title="Emotion Probabilities",
+        tooltip=["emotion", "score"],
+        height=400,
+        color="#2563eb"
     ),
     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.",
     examples=[
-        ["I am so happy to see you!"],
-        ["I'm really angry about what happened."],
-        ["The sunset was absolutely beautiful today."],
-        ["I'm worried about the upcoming exam."],
-        ["Fear is the mind-killer. I will face my fear."]
+        "I am so happy to see you!",
+        "I'm really angry about what happened.",
+        "The sunset was absolutely beautiful today.",
+        "I'm worried about the upcoming exam.",
+        "Fear is the mind-killer. I will face my fear."
     ],
     allow_flagging="never"
 )