import warnings import numpy as np import pandas as pd import os import json import random import gradio as gr import torch from sklearn.preprocessing import OneHotEncoder from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForCausalLM, pipeline from deap import base, creator, tools, algorithms import nltk from nltk.sentiment import SentimentIntensityAnalyzer from nltk.tokenize import word_tokenize from nltk.tag import pos_tag from nltk.chunk import ne_chunk from textblob import TextBlob import matplotlib.pyplot as plt import seaborn as sns warnings.filterwarnings('ignore', category=FutureWarning, module='huggingface_hub.file_download') # Download necessary NLTK data nltk.download('vader_lexicon', quiet=True) nltk.download('punkt', quiet=True) nltk.download('averaged_perceptron_tagger', quiet=True) nltk.download('maxent_ne_chunker', quiet=True) nltk.download('words', quiet=True) # Initialize Example Dataset (For Emotion Prediction) data = { 'context': [ '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', 'determination', 'resentment', 'glory', 'motivation', 'surprise', 'fear', 'trust', 'disgust', 'optimism', 'pessimism', 'boredom', 'envy' ] } df = pd.DataFrame(data) # Encoding the contexts using One-Hot Encoding (memory-efficient) try: encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=True) except TypeError: encoder = OneHotEncoder(handle_unknown='ignore', sparse=True) contexts_encoded = encoder.fit_transform(df[['context']]) # Encoding emotions 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 # 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}, } total_percentage = 100 emotion_history_file = 'emotion_history.json' global conversation_history conversation_history = [] max_history_length = 1000 # Increase the maximum history length def load_historical_data(file_path=emotion_history_file): if os.path.exists(file_path): with open(file_path, 'r') as file: return json.load(file) return [] def save_historical_data(historical_data, file_path=emotion_history_file): with open(file_path, 'w') as file: json.dump(historical_data, file) emotion_history = load_historical_data() def update_emotion(emotion, percentage, intensity): emotions[emotion]['percentage'] += percentage emotions[emotion]['intensity'] = intensity # Normalize percentages total = sum(e['percentage'] for e in emotions.values()) 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) def evaluate(individual): emotion_values = individual[:len(emotions)] intensities = individual[len(emotions):] total_diff = abs(100 - sum(emotion_values)) intensity_range = max(intensities) - min(intensities) emotion_balance = max(emotion_values) - min(emotion_values) return total_diff, intensity_range, emotion_balance def evolve_emotions(): toolbox = base.Toolbox() toolbox.register("attr_float", random.uniform, 0, 100) toolbox.register("attr_intensity", random.uniform, 0, 10) toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_float,) * len(emotions) + (toolbox.attr_intensity,) * len(emotions), n=1) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2) toolbox.register("select", tools.selNSGA2) toolbox.register("evaluate", evaluate) population = toolbox.population(n=100) algorithms.eaMuPlusLambda(population, toolbox, mu=50, lambda_=100, cxpb=0.7, mutpb=0.2, ngen=50, stats=None, halloffame=None, verbose=False) best_individual = tools.selBest(population, k=1)[0] emotion_values = best_individual[:len(emotions)] intensities = best_individual[len(emotions):] for i, (emotion, data) in enumerate(emotions.items()): data['percentage'] = emotion_values[i] data['intensity'] = intensities[i] # Normalize percentages total = sum(e['percentage'] for e in emotions.values()) for e in emotions: emotions[e]['percentage'] = (emotions[e]['percentage'] / total) * 100 def update_emotion_history(emotion, percentage, intensity, context): entry = { 'emotion': emotion, 'percentage': percentage, 'intensity': intensity, 'context': context, 'timestamp': pd.Timestamp.now().isoformat() } emotion_history.append(entry) save_historical_data(emotion_history) # Adding 443 features additional_features = {} for i in range(443): additional_features[f'feature_{i+1}'] = 0 def feature_transformations(): global additional_features for feature in additional_features: additional_features[feature] += random.uniform(-1, 1) 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 inputs = response_tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=8192) # Adjust generation parameters based on emotion temperature = 0.7 if ai_emotion == 'anger': temperature = 0.9 # More randomness for angry responses elif ai_emotion == 'joy': temperature = 0.5 # More focused responses for joyful state with torch.no_grad(): response_ids = response_model.generate( inputs.input_ids, attention_mask=inputs.attention_mask, max_length=8192, num_return_sequences=1, no_repeat_ngram_size=2, do_sample=True, top_k=50, top_p=0.95, temperature=temperature, pad_token_id=response_tokenizer.eos_token_id ) response = response_tokenizer.decode(response_ids[0], skip_special_tokens=True) # Extract only the AI's response response = response.split("AI:")[-1].strip() return response def predict_emotion(context): inputs = emotion_prediction_tokenizer(context, return_tensors="pt", truncation=True, max_length=512) with torch.no_grad(): outputs = emotion_prediction_model(**inputs) probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1) predicted_class = torch.argmax(probabilities, dim=-1).item() emotion_labels = ["sadness", "joy", "love", "anger", "fear", "surprise"] return emotion_labels[predicted_class] def sentiment_analysis(text): sia = SentimentIntensityAnalyzer() sentiment_scores = sia.polarity_scores(text) return sentiment_scores def extract_entities(text): chunked = ne_chunk(pos_tag(word_tokenize(text))) entities = [] for chunk in chunked: if hasattr(chunk, 'label'): entities.append(((' '.join(c[0] for c in chunk)), chunk.label())) return entities def analyze_text_complexity(text): blob = TextBlob(text) return { 'word_count': len(blob.words), 'sentence_count': len(blob.sentences), 'average_sentence_length': len(blob.words) / len(blob.sentences) if len(blob.sentences) > 0 else 0, 'polarity': blob.sentiment.polarity, 'subjectivity': blob.sentiment.subjectivity } def visualize_emotions(): emotions_df = pd.DataFrame([(e, d['percentage'], d['intensity']) for e, d in emotions.items()], columns=['Emotion', 'Percentage', 'Intensity']) plt.figure(figsize=(12, 6)) sns.barplot(x='Emotion', y='Percentage', data=emotions_df) plt.title('Current Emotional State of the AI') plt.xticks(rotation=45, ha='right') plt.tight_layout() plt.savefig('emotional_state.png') plt.close() return 'emotional_state.png' def interactive_interface(input_text): global conversation_history try: evolve_emotions() predicted_emotion = predict_emotion(input_text) sentiment_scores = sentiment_analysis(input_text) entities = extract_entities(input_text) text_complexity = analyze_text_complexity(input_text) # Update AI's emotional state based on input update_emotion(predicted_emotion, random.uniform(5, 15), random.uniform(0, 10)) # Determine AI's current dominant emotion ai_emotion = max(emotions, key=lambda e: emotions[e]['percentage']) # Generate response based on AI's emotion response = generate_response(input_text, ai_emotion, conversation_history) # Update conversation history conversation_history.append({ 'user': input_text, 'response': response }) # Trim conversation history if it exceeds the maximum length if len(conversation_history) > max_history_length: conversation_history = conversation_history[-max_history_length:] update_emotion_history(ai_emotion, emotions[ai_emotion]['percentage'], emotions[ai_emotion]['intensity'], input_text) feature_transformations() emotion_visualization = visualize_emotions() analysis_result = { 'predicted_user_emotion': predicted_emotion, 'sentiment_scores': sentiment_scores, 'entities': entities, 'text_complexity': text_complexity, '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 {'error': str(e)} # Define the Gradio interface demo = gr.Interface( fn=interactive_interface, inputs=gr.Textbox(label="Enter your message"), outputs=[ gr.Textbox(label="Predicted User Emotion"), gr.Textbox(label="Sentiment Scores"), gr.Textbox(label="Extracted Entities"), gr.Textbox(label="Text Complexity"), gr.Textbox(label="AI Emotion"), gr.Textbox(label="AI Emotion Percentage"), gr.Textbox(label="AI Emotion Intensity"), gr.Image(label="Emotional State Visualization") ], 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()