Update app.py

app.py

@@ -62,7 +62,7 @@ emotion_prediction_tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/di
 # Load pre-trained large language model and tokenizer for response generation with increased context window
 response_model_name = "gpt2-xl"
 response_tokenizer = AutoTokenizer.from_pretrained(response_model_name)
-response_model = AutoModelForCausalLM.from_pretrained(response_model_name)
+response_model = AutoModelForCausalLM.from_pretrained(response_model_name, torch_dtype=torch.float16, device_map="auto")
 
 # Set the pad token
 response_tokenizer.pad_token = response_tokenizer.eos_token
@@ -147,10 +147,10 @@ def evolve_emotions():
         data['percentage'] = emotion_values[i]
         data['intensity'] = intensities[i]
 
-    # Normalize percentages
+     # Normalize percentages
     total = sum(e['percentage'] for e in emotions.values())
     for e in emotions:
-        emotions[e]['percentage'] = (emotions[e]['percentage'] / total) * 100
+        emotions[e]['percentage'] = (emotions[e]['percentage'] /total) * 100
 
 def update_emotion_history(emotion, percentage, intensity, context):
     entry = {
@@ -175,7 +175,7 @@ def feature_transformations():
 
 def generate_response(input_text, ai_emotion, conversation_history):
     # Prepare a prompt based on the current emotion and input
-    prompt = f"You are an AI assistant currently feeling {ai_emotion}. Your response should reflect this emotion. Human: {input_text}\nAI:"
+    prompt = f"You are an AI assistant created by Sephiroth Baptiste, and you are currently feeling {ai_emotion}. Your response should reflect this emotion. Human: {input_text}\nAI:"
     
     # Add conversation history to the prompt
     for entry in conversation_history[-100:]:  # Use last 100 entries for context
@@ -190,7 +190,7 @@ def generate_response(input_text, ai_emotion, conversation_history):
     elif ai_emotion == 'joy':
         temperature = 0.5  # More focused responses for joyful state
 
-    with torch.no_grad():
+    with torch.no_grad(), torch.cuda.amp.autocast():
         response_ids = response_model.generate(
             inputs.input_ids,
             attention_mask=inputs.attention_mask,
@@ -288,26 +288,28 @@ def interactive_interface(input_text):
         
         emotion_visualization = visualize_emotions()
         
-        return (
-            predicted_emotion,
-            ai_emotion,
-            str(sentiment_scores),
-            str(entities),
-            str(text_complexity),
-            response,
-            emotion_visualization
-        )
+        analysis_result = {
+            'predicted_user_emotion': predicted_emotion,
+            'ai_emotion': ai_emotion,
+            'sentiment_scores': sentiment_scores,
+            'entities': entities,
+            'text_complexity': text_complexity,
+            'response': response,
+            'emotion_visualization': emotion_visualization
+        }
+        
+        return analysis_result
     except Exception as e:
         print(f"Error: {e}")
-        return (
-            'unknown',
-            'neutral',
-            str({'compound': 0, 'neg': 0, 'neu': 1, 'pos': 0}),
-            '[]',
-            str({'word_count': 0, 'sentence_count': 0, 'average_sentence_length': 0, 'polarity': 0, 'subjectivity': 0}),
-            "I'm sorry, but I encountered an error and was unable to generate a response.",
-            'emotional_state.png'
-        )
+        return {
+            'predicted_user_emotion': 'unknown',
+            'ai_emotion': 'neutral',
+            'sentiment_scores': {'compound': 0, 'neg': 0, 'neu': 1, 'pos': 0},
+            'entities': [],
+            'text_complexity': {'word_count': 0, 'sentence_count': 0, 'average_sentence_length': 0, 'polarity': 0, 'subjectivity': 0},
+            'response': "I'm sorry, but I encountered an error and was unable to generate a response.",
+            'emotion_visualization': 'emotional_state.png'
+        }
 
 # Create a Gradio interface
 gr.Interface(
@@ -322,6 +324,6 @@ gr.Interface(
         gr.Textbox(label="AI Response"),
         gr.Image(label="Emotional State Visualization")
     ],
-    title="Emotion-Aware AI Assistant",
-    description="Interact with an AI assistant that responds based on its emotional state.",
+    title="Emotion-Aware AI Assistant by Sephfox",
+    description="Interact with an AI assistant created by Sephfox that responds based on its emotional state.",
 ).launch()