Update app.py

app.py

@@ -6,13 +6,26 @@ learn = from_pretrained_fastai("haripriyaram/Text-emotion-Recognizer-Model")
 
 # Prediction function
 def predict_emotion(text):
-    pred_label, _, probs = learn.predict(text)
+    #pred_label, _, probs = learn.predict(text)
+    # pred, pred_idx, probs = learn.predict(text)
 
-    # Handle nested vocab if needed
-    vocab = learn.dls.vocab[0] if isinstance(learn.dls.vocab[0], list) else learn.dls.vocab
-    probs_dict = {label: float(prob) for label, prob in zip(vocab, probs)}
+    # # 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}")
 
-    return pred_label, probs_dict
+    return prediction, probs_dict
 
 # Gradio UI
 iface = gr.Interface(
@@ -20,9 +33,9 @@ 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 (FastAI)",
+    title="🎭 Emotion Classifier (ML Service)",
     description="Enter a sentence and the model will predict the corresponding emotion.",
     allow_flagging="never"
 )