Update app.py

app.py

@@ -46,15 +46,14 @@ emotion_classes = pd.Categorical(df['emotion']).categories
 emotion_prediction_model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
 
 # Lazy loading for the fine-tuned language model
-_finetuned_lm_tokenizer = None
-_finetuned_lm_model = None
-def get_finetuned_lm_model():
-    global _finetuned_lm_tokenizer, _finetuned_lm_model
-    if _finetuned_lm_tokenizer is None or _finetuned_lm_model is None:
-        finetuned_lm_model_name = "microsoft/DialoGPT-large"  # Replace with your fine-tuned language model name
-        _finetuned_lm_tokenizer = AutoTokenizer.from_pretrained(finetuned_lm_model_name)
-        _finetuned_lm_model = AutoModelForCausalLM.from_pretrained(finetuned_lm_model_name, device_map="auto", low_cpu_mem_usage=True)
-    return _finetuned_lm_tokenizer, _finetuned_lm_model
+def predict_emotion(context):
+    tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
+    emotion_prediction_pipeline = pipeline('text-classification', model=emotion_prediction_model, tokenizer=tokenizer, return_all_scores=True)
+    predictions = emotion_prediction_pipeline(context)
+    emotion_scores = predictions[0]
+    emotion_pred = max(emotion_scores, key=emotion_scores.get)
+    return emotion_pred
+
 
 # Enhanced Emotional States
 emotions = {