Update app.py

app.py

@@ -59,8 +59,8 @@ emotion_classes = pd.Categorical(df['emotion']).categories
 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 LLM model and tokenizer for response generation with increased context window
-response_model_name = "microsoft/DialoGPT-large"
+# Load pre-trained large language model and tokenizer for response generation with increased context window
+response_model_name = "facebook/opt-5b"
 response_tokenizer = AutoTokenizer.from_pretrained(response_model_name)
 response_model = AutoModelForCausalLM.from_pretrained(response_model_name)
 
@@ -82,7 +82,7 @@ total_percentage = 100
 emotion_history_file = 'emotion_history.json'
 global conversation_history
 conversation_history = []
-max_history_length = 100  # Increase the maximum history length
+max_history_length = 1000  # Increase the maximum history length
 
 def load_historical_data(file_path=emotion_history_file):
     if os.path.exists(file_path):
@@ -178,10 +178,10 @@ def generate_response(input_text, ai_emotion, conversation_history):
     prompt = f"You are an AI assistant 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[-20:]:  # Use last 20 entries for context
+    for entry in conversation_history[-100:]:  # Use last 100 entries for context
         prompt = f"Human: {entry['user']}\nAI: {entry['response']}\n" + prompt
     
-    inputs = response_tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=2048)
+    inputs = response_tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=8192)
     
     # Adjust generation parameters based on emotion
     temperature = 0.7
@@ -194,7 +194,7 @@ def generate_response(input_text, ai_emotion, conversation_history):
         response_ids = response_model.generate(
             inputs.input_ids,
             attention_mask=inputs.attention_mask,
-            max_length=2048,
+            max_length=8192,
             num_return_sequences=1,
             no_repeat_ngram_size=2,
             do_sample=True,
@@ -254,6 +254,7 @@ def visualize_emotions():
     plt.close()
 
     return 'emotional_state.png'
+
 def interactive_interface(input_text):
     global conversation_history
     try:
@@ -287,26 +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(
@@ -322,5 +325,5 @@ gr.Interface(
         gr.Image(label="Emotional State Visualization")
     ],
     title="Emotion-Aware AI Assistant",
-    description="Interact with an AI assistant that responds based on its emotional state."
+    description="Interact with an AI assistant that responds based on its emotional state.",
 ).launch()