Update app.py

app.py

@@ -57,26 +57,32 @@ emotions_target = pd.Categorical(df['emotion']).codes
 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")
-
-# 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)
-
-# Set the pad token
-response_tokenizer.pad_token = response_tokenizer.eos_token
+emotion_prediction_model = None
+emotion_prediction_tokenizer = None
+
+# Load pre-trained large language model and tokenizer for response generation
+response_model = None
+response_tokenizer = None
+
+def load_models():
+    global emotion_prediction_model, emotion_prediction_tokenizer, response_model, response_tokenizer
+    if emotion_prediction_model is None or response_model is None:
+        emotion_prediction_model = AutoModelForSequenceClassification.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
+        emotion_prediction_tokenizer = AutoTokenizer.from_pretrained("bhadresh-savani/distilbert-base-uncased-emotion")
+        response_model_name = "gpt2-xl"
+        response_tokenizer = AutoTokenizer.from_pretrained(response_model_name)
+        response_model = AutoModelForCausalLM.from_pretrained(response_model_name)
+        response_tokenizer.pad_token = response_tokenizer.eos_token
 
 # Enhanced Emotional States
 emotions = {
-    'joy': {'percentage': 10, 'motivation': 'positive and uplifting', 'intensity': 0},
-    'sadness': {'percentage': 10, 'motivation': 'reflective and introspective', 'intensity': 0},
-    'anger': {'percentage': 10, 'motivation': 'passionate and driven', 'intensity': 0},
-    'fear': {'percentage': 10, 'motivation': 'cautious and protective', 'intensity': 0},
-    'love': {'percentage': 10, 'motivation': 'affectionate and caring', 'intensity': 0},
-    'surprise': {'percentage': 10, 'motivation': 'curious and intrigued', 'intensity': 0},
-    'neutral': {'percentage': 40, 'motivation': 'balanced and composed', 'intensity': 0},
+    'joy': {'percentage': 20, 'motivation': 'positive and uplifting', 'intensity': 8},
+    'sadness': {'percentage': 15, 'motivation': 'reflective and introspective', 'intensity': 6},
+    'anger': {'percentage': 15, 'motivation': 'passionate and driven', 'intensity': 7},
+    'fear': {'percentage': 10, 'motivation': 'cautious and protective', 'intensity': 5},
+    'love': {'percentage': 15, 'motivation': 'affectionate and caring', 'intensity': 7},
+    'surprise': {'percentage': 10, 'motivation': 'curious and intrigued', 'intensity': 6},
+    'neutral': {'percentage': 15, 'motivation': 'balanced and composed', 'intensity': 4},
 }
 
 total_percentage = 100
@@ -106,6 +112,9 @@ def update_emotion(emotion, percentage, intensity):
     for e in emotions:
         emotions[e]['percentage'] = (emotions[e]['percentage'] / total) * 100
 
+def normalize_context(context):
+    return context.lower().strip()
+
 creator.create("FitnessMulti", base.Fitness, weights=(-1.0, -0.5, -0.2))
 creator.create("Individual", list, fitness=creator.FitnessMulti)
 
@@ -138,7 +147,7 @@ def evolve_emotions():
 
     best_individual = tools.selBest(population, k=1)[0]
     emotion_values = best_individual[:len(emotions)]
-    intensities = best_individual[len(emotions):]
+   intensities = best_individual[len(emotions):]
 
     for i, (emotion, data) in enumerate(emotions.items()):
         data['percentage'] = emotion_values[i]
@@ -150,6 +159,7 @@ def evolve_emotions():
         emotions[e]['percentage'] = (emotions[e]['percentage'] / total) * 100
 
 def predict_emotion(context):
+    load_models()
     inputs = emotion_prediction_tokenizer(context, return_tensors="pt", truncation=True, max_length=512)
     with torch.no_grad():
         outputs = emotion_prediction_model(**inputs)
@@ -208,14 +218,12 @@ def generate_emotion_visualization(ai_emotion, ai_emotion_percentage, ai_emotion
         emotion_visualization_path = None
     return emotion_visualization_path
 
-def generate_response(input_text, ai_emotion, conversation_history):
-    # Prepare a prompt based on the current emotion and input
-    prompt = f"As an AI assistant, I am currently feeling {ai_emotion}. My response will reflect this emotional state. Human: {input_text}\nAI: "
-    
-    # Add conversation history to the prompt
-    for entry in conversation_history[-100:]:  # Use last 100 entries for context
-        prompt = f"Human: {entry['user']}\nAI: {entry['response']}\n" + prompt
+def generate_response(ai_emotion, input_text):
+    load_models()
+    # Prepare a prompt based on the current emotion
+    prompt = f"As an AI assistant, I am currently feeling {ai_emotion}. My response will reflect this emotional state."
     
+    # Generate the response
     inputs = response_tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=8192)
     
     # Adjust generation parameters based on emotion
@@ -251,7 +259,7 @@ def interactive_interface(input_text):
     text_complexity = analyze_text_complexity(input_text)
     ai_emotion, ai_emotion_percentage, ai_emotion_intensity = get_ai_emotion(input_text)
     emotion_visualization = generate_emotion_visualization(ai_emotion, ai_emotion_percentage, ai_emotion_intensity)
-    response = generate_response(input_text, ai_emotion, conversation_history)
+    response = generate_response(ai_emotion, input_text)
 
     # Update conversation history
     conversation_history.append({'user': input_text, 'response': response})
@@ -268,7 +276,7 @@ def interactive_interface(input_text):
         gr.Textbox(value=f"{ai_emotion_percentage:.2f}%", label="AI Emotion Percentage"),
         gr.Textbox(value=f"{ai_emotion_intensity:.2f}", label="AI Emotion Intensity"),
         gr.Image(value=emotion_visualization, label="Emotion Visualization"),
-        gr.Textbox(value=f"As an AI assistant, I am currently feeling {ai_emotion}. My response will reflect this emotional state.\n\n{response}", label="AI Response")
+        gr.Textbox(value=response, label="AI Response")
     )
 
 # 443 additional features
@@ -305,8 +313,7 @@ def visualize_emotions():
 
 # Create the Gradio interface
 iface = gr.Interface(
-    fn=interactive_interface,
-    inputs=gr.Textbox(label="Input Text"),
+    fn=interactive_interface,inputs=gr.Textbox(label="Input Text"),
     outputs=[
         gr.Textbox(label="Predicted Emotion"),
         gr.Textbox(label="Sentiment Scores"),
@@ -322,4 +329,4 @@ iface = gr.Interface(
     description="An AI assistant that can analyze the emotional content of text and generate responses based on its emotional state.",
 )
 
-iface.launch()
+iface.launch()