Update app.py

app.py

@@ -128,8 +128,7 @@ def evolve_emotions():
     toolbox.register("attr_float", random.uniform, 0, 20)
     toolbox.register("attr_intensity", random.uniform, 0, 10)
     toolbox.register("individual", tools.initCycle, creator.Individual,
-                     (toolbox.attr_float,) * (len(emotions) - 1)
-    + 
+                     (toolbox.attr_float,) * (len(emotions) - 1) + 
                      (toolbox.attr_intensity,) * len(emotions) +
                      (lambda: 100,), n=1)
     toolbox.register("population", tools.initRepeat, list, toolbox.individual)
@@ -154,10 +153,10 @@ def evolve_emotions():
     emotions['ideal_state']['percentage'] = ideal_state
 
 def predict_emotion(context):
-    emotion_prediction_pipeline = pipeline('text-classification', model=emotion_prediction_model, tokenizer=emotion_prediction_tokenizer, return_all_scores=True)
+    emotion_prediction_pipeline = pipeline('text-classification', model=emotion_prediction_model, tokenizer=emotion_prediction_tokenizer, top_k=None)
     predictions = emotion_prediction_pipeline(context)
     emotion_scores = predictions[0]
-    emotion_pred = max(emotion_scores, key=emotion_scores.get)
+    emotion_pred = emotion_classes[np.argmax(emotion_scores)]
     return emotion_pred
 
 def generate_text(prompt, max_length=100, emotion=None):
@@ -203,4 +202,4 @@ with gr.Blocks() as demo:
     
     predict_btn.click(fn=lambda context: (predict_emotion(context), generate_response(context, emotion=predict_emotion(context))), inputs=context_input, outputs=[emotion_output, generated_text_output])
 
-demo.launch()
+demo.launch(share=True)