better interface

app.py

@@ -7,18 +7,19 @@ classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-b
 def classify_emotion(text):
     # Make predictions using the Hugging Face pipeline
     predictions = classifier(text)
+    # Convert predictions to a dictionary for gr.Label output
     return {item["label"]: item["score"] for item in predictions}
 
 # Create a custom Gradio interface with title, description, and examples
 gr.Interface(
     fn=classify_emotion,
     inputs=gr.Textbox(
-        placeholder="Enter text to analyze...", 
+        placeholder="Enter text to analyze...",
         label="Input Text",
         lines=4
     ),
-    outputs=gr.JSON(),  # Display results in JSON format
-    title="Emotion Detection with DistilBERT",
+    outputs=gr.Label(label="Emotion Probabilities"),  # Use gr.Label for a cleaner interface
+    title="CMACHINES | Emotion Detection with DistilBERT",
     description="This app uses the DistilBERT model fine-tuned for emotion detection. Enter a piece of text to analyze its emotional content!",
     examples=[
         "I am so happy to see you!",