try this change

app.py

@@ -6,29 +6,29 @@ import sys
 import pandas as pd
 
 # Set up logging
-logging.basicConfig(level=logging.INFO, stream=sys.stdout,
-                   format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(
+    level=logging.INFO,
+    stream=sys.stdout,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
 logger = logging.getLogger(__name__)
 
-try:
-    logger.info("Initializing emotion classification pipeline...")
-    classifier = pipeline(
-        "text-classification",
-        model="bhadresh-savani/distilbert-base-uncased-emotion",
-        return_all_scores=True
-    )
-    logger.info("Pipeline initialized successfully")
-except Exception as e:
-    logger.error(f"Failed to initialize pipeline: {str(e)}")
-    raise
+# Initialize the pipeline
+logger.info("Initializing emotion classification pipeline...")
+classifier = pipeline(
+    "text-classification",
+    model="bhadresh-savani/distilbert-base-uncased-emotion",
+    return_all_scores=True
+)
+logger.info("Pipeline initialized successfully")
 
 def predict_emotion(text):
+    """Predict emotions from text and return formatted results."""
     try:
         logger.info(f"Received input text: {text}")
         
         if not text:
             logger.warning("Empty text received")
-            # Return empty DataFrame with correct structure
             return pd.DataFrame({
                 'emotion': ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'],
                 'score': [0, 0, 0, 0, 0, 0]
@@ -41,51 +41,46 @@ def predict_emotion(text):
         
         # Convert to DataFrame and format for visualization
         df = pd.DataFrame(predictions)
-        df.columns = ['emotion', 'score']  # Rename columns
-        df = df.sort_values('score', ascending=True)  # Sort for better visualization
+        df.columns = ['emotion', 'score']
+        df = df.sort_values('score', ascending=True)
         logger.info(f"Processed scores as DataFrame: {df.to_dict()}")
         
         return df
+        
+    except Exception as e:
+        logger.error(f"Error in prediction: {str(e)}")
+        return pd.DataFrame({
+            'emotion': ['error'],
+            'score': [1.0]
+        })
 
-# Create the Gradio interface with error handling
-try:
-    logger.info("Setting up Gradio interface...")
-    demo = gr.Interface(
-        fn=predict_emotion,
-        inputs=gr.Textbox(
-            placeholder="Enter text to analyze...", 
-            label="Input Text",
-            lines=4
-        ),
-        outputs=gr.BarPlot(
-            title="Emotion Probabilities",
-            x="emotion",
-            y="score",
-            xlabel="Emotion",
-            ylabel="Probability",
-            height=400
-        ),
-        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."]
-        ],
-        allow_flagging="never"
-    )
-    logger.info("Gradio interface setup complete")
-except Exception as e:
-    logger.error(f"Failed to create Gradio interface: {str(e)}")
-    raise
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(
+        placeholder="Enter text to analyze...", 
+        label="Input Text",
+        lines=4
+    ),
+    outputs=gr.BarPlot(
+        title="Emotion Probabilities",
+        x="emotion",
+        y="score",
+        xlabel="Emotion",
+        ylabel="Probability",
+        height=400
+    ),
+    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."]
+    ],
+    allow_flagging="never"
+)
 
 if __name__ == "__main__":
-    try:
-        logger.info("Launching Gradio app...")
-        demo.launch(debug=True)
-        logger.info("Gradio app launched successfully")
-    except Exception as e:
-        logger.error(f"Failed to launch Gradio app: {str(e)}")
-        raise
+    demo.launch(debug=True)