Update app.py

app.py

@@ -30,11 +30,12 @@ nltk.download('words', quiet=True)
 # Initialize Example Dataset (For Emotion Prediction)
 data = {
     'context': [
-        'I am happy', 'I am sad', 'I am angry', 'I am excited', 'I am calm',
-        'I am feeling joyful', 'I am grieving', 'I am feeling peaceful', 'I am frustrated',
-        'I am determined', 'I feel resentment', 'I am feeling glorious', 'I am motivated',
-        'I am surprised', 'I am fearful', 'I am trusting', 'I feel disgust', 'I am optimistic',
-        'I am pessimistic', 'I feel bored', 'I am envious'
+        'I am overjoyed', 'I am deeply saddened', 'I am seething with rage', 'I am exhilarated', 'I am tranquil',
+        'I am brimming with joy', 'I am grieving profoundly', 'I am at peace', 'I am frustrated beyond measure',
+        'I am determined to succeed', 'I feel resentment burning within me', 'I am feeling glorious and triumphant',
+        'I am motivated and inspired', 'I am utterly surprised', 'I am gripped by fear', 'I am trusting and open',
+        'I feel a sense of disgust', 'I am optimistic and hopeful', 'I am pessimistic and gloomy', 'I feel bored and listless',
+        'I am envious and jealous'
     ],
     'emotion': [
         'joy', 'sadness', 'anger', 'joy', 'calmness', 'joy', 'grief', 'calmness', 'anger',
@@ -69,13 +70,13 @@ response_tokenizer.pad_token = response_tokenizer.eos_token
 
 # Enhanced Emotional States
 emotions = {
-    'joy': {'percentage': 10, 'motivation': 'positive', 'intensity': 0},
-    'sadness': {'percentage': 10, 'motivation': 'negative', 'intensity': 0},
-    'anger': {'percentage': 10, 'motivation': 'traumatic or strong', 'intensity': 0},
-    'fear': {'percentage': 10, 'motivation': 'defensive', 'intensity': 0},
-    'love': {'percentage': 10, 'motivation': 'affectionate', 'intensity': 0},
-    'surprise': {'percentage': 10, 'motivation': 'unexpected', 'intensity': 0},
-    'neutral': {'percentage': 40, 'motivation': 'balanced', 'intensity': 0},
+    '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},
 }
 
 total_percentage = 100
@@ -108,7 +109,6 @@ def update_emotion(emotion, percentage, intensity):
 def normalize_context(context):
     return context.lower().strip()
 
-# Create FitnessMulti and Individual outside of evolve_emotions
 creator.create("FitnessMulti", base.Fitness, weights=(-1.0, -0.5, -0.2))
 creator.create("Individual", list, fitness=creator.FitnessMulti)
 
@@ -147,7 +147,7 @@ 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
@@ -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"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
@@ -247,7 +247,7 @@ def visualize_emotions():
     
     plt.figure(figsize=(12, 6))
     sns.barplot(x='Emotion', y='Percentage', data=emotions_df)
-    plt.title('Current Emotional State')
+    plt.title('Current Emotional State of the AI')
     plt.xticks(rotation=45, ha='right')
     plt.tight_layout()
     plt.savefig('emotional_state.png')
@@ -290,40 +290,37 @@ def interactive_interface(input_text):
         
         analysis_result = {
             'predicted_user_emotion': predicted_emotion,
-            'ai_emotion': ai_emotion,
             'sentiment_scores': sentiment_scores,
             'entities': entities,
             'text_complexity': text_complexity,
-            'response': response,
+            'ai_emotion': ai_emotion,
+            'ai_emotion_percentage': emotions[ai_emotion]['percentage'],
+            'ai_emotion_intensity': emotions[ai_emotion]['intensity'],
             'emotion_visualization': emotion_visualization
         }
         
         return analysis_result
     except Exception as e:
         print(f"Error: {e}")
-        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'
-        }
+        return {'error': str(e)}
 
-# Create a Gradio interface
-gr.Interface(
+# Define the Gradio interface
+demo = gr.Interface(
     fn=interactive_interface,
-    inputs=gr.Textbox(label="Your Message"),
+    inputs=gr.Textbox(label="Enter your message"),
     outputs=[
         gr.Textbox(label="Predicted User Emotion"),
-        gr.Textbox(label="AI Emotion"),
         gr.Textbox(label="Sentiment Scores"),
         gr.Textbox(label="Extracted Entities"),
         gr.Textbox(label="Text Complexity"),
-        gr.Textbox(label="AI Response"),
+        gr.Textbox(label="AI Emotion"),
+        gr.Textbox(label="AI Emotion Percentage"),
+        gr.Textbox(label="AI Emotion Intensity"),
         gr.Image(label="Emotional State Visualization")
     ],
-    title="Emotion-Aware AI Assistant",
-    description="Interact with an AI assistant that responds based on its emotional state.",
-).launch()
+    title="Emotional AI Assistant",
+    description="Interact with an AI assistant that adapts its responses based on its emotional state."
+)
+
+# Launch the Gradio interface
+demo.launch()