Update app.py

app.py

@@ -6,24 +6,11 @@ learn = from_pretrained_fastai("haripriyaram/Text-emotion-Recognizer-Model")
 
 # Prediction function
 def predict_emotion(text):
-    #pred_label, _, probs = learn.predict(text)
-    # pred, pred_idx, probs = learn.predict(text)
+    pred_label, _, probs = learn.predict(text)
 
     # # Handle nested vocab if needed
-    # vocab = learn.dls.vocab[0] if isinstance(learn.dls.vocab[1], list) else learn.dls.vocab
-    # probs_dict = {label: float(prob) for label, prob in zip(vocab, probs)}
-    # Perform prediction on the input text
-    prediction, class_index, probabilities = learn.predict(text)
-    
-    # Get the list of class labels
-    class_labels = learn.dls.vocab
-    
-    # Create a dictionary of probabilities
-    probs_dict = dict(zip(class_labels, probabilities.tolist()))
-    
-    print(f"Input text: {input_text}")
-    print(f"Predicted emotion: {prediction}")
-    print(f"Probabilities: {probs_dict}")
+    vocab = learn.dls.vocab[0] if isinstance(learn.dls.vocab[1], list) else learn.dls.vocab
+    probs_dict = {label: float(prob) for label, prob in zip(vocab, probs)}
 
     return prediction, probs_dict
 
@@ -33,7 +20,7 @@ iface = gr.Interface(
     inputs=gr.Textbox(lines=2, placeholder="Enter a sentence..."),
     outputs=[
         gr.Label(label="Predicted Emotion"),
-        gr.JSON(label="Confidence Scores")
+        #gr.JSON(label="Confidence Scores")
     ],
     title="🎭 Emotion Classifier (ML Service)",
     description="Enter a sentence and the model will predict the corresponding emotion.",