Update app.py

app.py

@@ -42,7 +42,7 @@ emotion_classes = pd.Categorical(df['emotion']).categories
 
 # Load pre-trained BERT model for emotion prediction
 emotion_prediction_model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
-emotion_prediction_tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
+emotion_prediction_tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion", padding_side='left')
 
 # Lazy loading for the fine-tuned language model (DialoGPT-medium)
 _finetuned_lm_tokenizer = None
@@ -52,7 +52,7 @@ def get_finetuned_lm_model():
     global _finetuned_lm_tokenizer, _finetuned_lm_model
     if _finetuned_lm_tokenizer is None or _finetuned_lm_model is None:
         model_name = "microsoft/DialoGPT-medium"
-        _finetuned_lm_tokenizer = AutoTokenizer.from_pretrained(model_name)
+        _finetuned_lm_tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left')
         _finetuned_lm_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", low_cpu_mem_usage=True)
         _finetuned_lm_tokenizer.pad_token = _finetuned_lm_tokenizer.eos_token
     return _finetuned_lm_tokenizer, _finetuned_lm_model