fn 6

app.py

@@ -22,20 +22,28 @@ def predict_sentiment(text):
     # Normalize the probabilities
     proba /= proba.sum()
     # Return the probability distribution
-    return {"Positive": float(proba[0]), "Negative": float(proba[1]), "Neutral": float(proba[2])}
+    return {'Positive': float(proba[0]), 'Negative': float(proba[1]), 'Neutral': float(proba[2])}
 
-# Create a Gradio interface
+# Define the possible classes
+classes = ['Positive', 'Negative', 'Neutral']
+
+# Define the Gradio interface
 iface = gr.Interface(
-    fn=predict_sentiment, 
-    inputs=gr.inputs.Textbox(label="Enter text here"), 
-    outputs=gr.outputs.Plotly(title="Sentiment Distribution", x="Sentiment", y="Probability")
+    fn=predict_sentiment,
+    inputs=gr.inputs.Textbox(label='Enter text here'),
+    outputs=gr.outputs.Bar(label='Sentiment Distribution', 
+                           colors=['#2a9d8f', '#e76f51', '#264653'], 
+                           value=[0.3, 0.3, 0.3], 
+                           min=0, 
+                           max=1),
+    title='SENTIMENT ANALYSIS'
 )
 
-# Define the possible classes
-classes = ["Positive", "Negative", "Neutral"]
-
-# Add the possible classes to the output plot
-iface.outputs[0].type.bar.labels = classes
+# Set the class labels for the output
+iface.outputs[0].type = 'bar'
+iface.outputs[0].label = 'Sentiment Distribution'
+iface.outputs[0].determine_labels_from_data = False
+iface.outputs[0].labels = classes
 
 # Launch the interface
 iface.launch()