fn 7

app.py

@@ -21,29 +21,28 @@ def predict_sentiment(text):
     proba = model.predict(text)[0]
     # Normalize the probabilities
     proba /= proba.sum()
-    # Return the probability distribution
-    return {'Positive': float(proba[0]), 'Negative': float(proba[1]), 'Neutral': float(proba[2])}
-
-# Define the possible classes
-classes = ['Positive', 'Negative', 'Neutral']
+    # Get the predicted sentiment label
+    sentiment_label = ['Positive', 'Negative', 'Neutral'][np.argmax(proba)]
+    # Determine the color based on the sentiment label
+    if sentiment_label == 'Positive':
+        color = '#2a9d8f'
+    elif sentiment_label == 'Negative':
+        color = '#e76f51'
+    else:
+        color = '#264653'
+    # Return the sentiment label and color
+    return {'label': sentiment_label, 'color': color}
 
 # Define the Gradio interface
 iface = gr.Interface(
     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),
+    outputs=gr.outputs.Label(label='Sentiment', default='Neutral', 
+                             font_size=30, font_family='Arial', 
+                             background_color='#f8f8f8', 
+                             color='black', value=None),
     title='SENTIMENT ANALYSIS'
 )
 
-# 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()