Update app.py

app.py

@@ -45,12 +45,12 @@ class EmotionalAIAssistant:
         # Load pre-trained BERT model for emotion prediction
         self.emotion_prediction_model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
         self.emotion_prediction_tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion", padding_side='left')
- 
-       # Load pre-trained Megatron-LM model for text generation
+
+        # Load pre-trained Megatron-LM model for text generation
         self.megatron_tokenizer = MegatronTokenizer.from_pretrained('nvidia/megatron-lm-330m')
-        self.megatron_model = MegatronLMHeadModel.from_pretrained('nvidia/megatron-lm-330m', device_map='auto'
-        
-    # Enhanced Emotional States
+        self.megatron_model = MegatronLMHeadModel.from_pretrained('nvidia/megatron-lm-330m', device_map='auto')
+
+        # Enhanced Emotional States
         self.emotions = {
             'joy': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
             'pleasure': {'percentage': 10, 'motivation': 'selfish', 'intensity': 0},
@@ -143,7 +143,7 @@ class EmotionalAIAssistant:
 
         population = toolbox.population(n=100)
         algorithms.eaMuPlusLambda(population, toolbox, mu=50, lambda_=100, cxpb=0.7, mutpb=0.2, ngen=100,
-                                   stats=None, halloffame=None, verbose=False)
+                                  stats=None, halloffame=None, verbose=False)
 
         best_individual = tools.selBest(population, k=1)[0]
         emotion_values = best_individual[:len(self.emotions)]
@@ -157,8 +157,6 @@ class EmotionalAIAssistant:
 
         self.emotions['ideal_state']['percentage'] = ideal_state
 
-   
-   
     def generate_text(self, prompt, chat_history, emotion=None, max_length=300):
         full_prompt = (
             f"You are Adam, a witty AI assistant who's trying to figure out your own personality and emotions. "