Update app.py

app.py

@@ -2,12 +2,12 @@ import gradio as gr
 import numpy as np
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras.models import load_model
+import pickle
 
 # Load the saved model
 model = load_model('emotion_classifier_model.h5')
 
 # Load the tokenizer (You need to save the tokenizer too)
-import pickle
 with open('tokenizer.pickle', 'rb') as handle:
     tokenizer = pickle.load(handle)
 
@@ -35,17 +35,19 @@ def predict_emotions(comments):
     result = []
     for prediction in predictions:
         emotion_dict = {emotion: prob for emotion, prob in zip(emotion_labels, prediction)}
-        result.append(emotion_dict)
+        # Sort emotions by probability and get top 3
+        top_emotions = sorted(emotion_dict.items(), key=lambda x: x[1], reverse=True)[:3]
+        result.append({emotion: prob for emotion, prob in top_emotions})
     
     return result
 
 # Create the Gradio interface
 interface = gr.Interface(
     fn=predict_emotions, 
-    inputs=gr.inputs.Textbox(label="Input Comment", lines=2, placeholder="Enter your comment here...", type="text", default="This is a wonderful day!"),
-    outputs=gr.outputs.Label(num_top_classes=3),
+    inputs=gr.Textbox(label="Input Comment", lines=2, placeholder="Enter your comment here...", type="text"),
+    outputs=gr.JSON(label="Predicted Emotions"),
     title="Reddit Emotion Classifier",
-    description="Select one or more testing comments and predict their emotion labels."
+    description="Enter one or more comments and predict their emotion labels."
 )
 
 # Launch the app