import warnings import numpy as np import pandas as pd import os import json import random import gradio as gr import torch from sklearn.model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForCausalLM, pipeline from deap import base, creator, tools, algorithms warnings.filterwarnings('ignore', category=FutureWarning, module='huggingface_hub.file_download') # 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' ], '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) 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 LLM model and tokenizer for response generation with increased context window response_model_name = "microsoft/DialoGPT-medium" response_tokenizer = AutoTokenizer.from_pretrained(response_model_name) response_model = AutoModelForCausalLM.from_pretrained(response_model_name) # Enhanced Emotional States emotions = { 'joy': {'percentage': 10, 'motivation': 'positive', 'intensity': 0}, 'pleasure': {'percentage': 10, 'motivation': 'selfish', 'intensity': 0}, 'sadness': {'percentage': 10, 'motivation': 'negative', 'intensity': 0}, 'grief': {'percentage': 10, 'motivation': 'negative', 'intensity': 0}, 'anger': {'percentage': 10, 'motivation': 'traumatic or strong', 'intensity': 0}, 'calmness': {'percentage': 10, 'motivation': 'neutral', 'intensity': 0}, 'determination': {'percentage': 10, 'motivation': 'positive', 'intensity': 0}, 'resentment': {'percentage': 10, 'motivation': 'negative', 'intensity': 0}, 'glory': {'percentage': 10, 'motivation': 'positive', 'intensity': 0}, 'motivation': {'percentage': 10, 'motivation': 'positive', 'intensity': 0}, 'ideal_state': {'percentage': 100, 'motivation': 'balanced', 'intensity': 0}, 'fear': {'percentage': 10, 'motivation': 'defensive', 'intensity': 0}, 'surprise': {'percentage': 10, 'motivation': 'unexpected', 'intensity': 0}, 'anticipation': {'percentage': 10, 'motivation': 'predictive', 'intensity': 0}, 'trust': {'percentage': 10, 'motivation': 'reliable', 'intensity': 0}, 'disgust': {'percentage': 10, 'motivation': 'repulsive', 'intensity': 0}, 'optimism': {'percentage': 10, 'motivation': 'hopeful', 'intensity': 0}, 'pessimism': {'percentage': 10, 'motivation': 'doubtful', 'intensity': 0}, 'boredom': {'percentage': 10, 'motivation': 'indifferent', 'intensity': 0}, 'envy': {'percentage': 10, 'motivation': 'jealous', 'intensity': 0}, 'neutral': {'percentage': 10, 'motivation': 'balanced', 'intensity': 0}, 'wit': {'percentage': 15, 'motivation': 'clever', 'intensity': 0}, 'curiosity': {'percentage': 20, 'motivation': 'inquisitive', 'intensity': 0}, } total_percentage = 200 emotion_history_file = 'emotion_history.json' 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): if percentage > emotions['ideal_state']['percentage']: percentage = emotions['ideal_state']['percentage'] emotions['ideal_state']['percentage'] -= percentage emotions[emotion]['percentage'] += percentage emotions[emotion]['intensity'] = intensity # Introduce some randomness in emotional evolution for e in emotions: if e != emotion and e != 'ideal_state': change = random.uniform(-2, 2) emotions[e]['percentage'] = max(0, emotions[e]['percentage'] + change) total_current = sum(e['percentage'] for e in emotions.values()) adjustment = total_percentage - total_current emotions['ideal_state']['percentage'] += adjustment def normalize_context(context): return context.lower().strip() def evaluate(individual): emotion_values = individual[:len(emotions) - 1] intensities = individual[len(emotions) - 1:-1] ideal_state = individual[-1] ideal_diff = abs(100 - ideal_state) sum_non_ideal = sum(emotion_values) intensity_range = max(intensities) - min(intensities) return ideal_diff, sum_non_ideal, intensity_range def evolve_emotions(): creator.create("FitnessMulti", base.Fitness, weights=(-1.0, -0.5, -0.2)) creator.create("Individual", list, fitness=creator.FitnessMulti) toolbox = base.Toolbox() toolbox.register("attr_float", random.uniform, 0, 20) toolbox.register("attr_intensity", random.uniform, 0, 10) toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_float,) * (len(emotions) - 1) + (toolbox.attr_intensity,) * (len(emotions) - 1) + (lambda: 100,), 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=100, stats=None, halloffame=None, verbose=False) best_individual = tools.selBest(population, k=1)[0] emotion_values = best_individual[:len(emotions) - 1] intensities = best_individual[len(emotions) - 1:-1] ideal_state = best_individual[-1] for i, emotion in enumerate(emotions): if emotion != 'ideal_state': emotions[emotion]['percentage'] = emotion_values[i] emotions[emotion]['intensity'] = intensities[i] emotions['ideal_state']['percentage'] = ideal_state 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): inputs = response_tokenizer(input_text, return_tensors="pt") response_ids = response_model.generate(inputs.input_ids, max_length=int(inputs.input_ids.shape[1] * 2.289)) response = response_tokenizer.decode(response_ids[:, inputs.input_ids.shape[1]:][0], skip_special_tokens=True) return response def predict_emotion(context): inputs = emotion_prediction_tokenizer(context, return_tensors="pt") outputs = emotion_prediction_model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) predicted_class = torch.argmax(predictions, dim=-1).item() predicted_emotion = emotion_classes[predicted_class] return predicted_emotion def interactive_interface(context): normalized_context = normalize_context(context) predicted_emotion = predict_emotion(normalized_context) update_emotion(predicted_emotion, random.uniform(5, 15), random.uniform(1, 10)) update_emotion_history(predicted_emotion, emotions[predicted_emotion]['percentage'], emotions[predicted_emotion]['intensity'], normalized_context) evolve_emotions() feature_transformations() response = generate_response(normalized_context) return response # Gradio Interface def gradio_interface(input_text): response = interactive_interface(input_text) return response iface = gr.Interface( fn=gradio_interface, inputs="text", outputs="text", title="Emotion-aware AI with Enhanced Features", description="An AI that predicts emotions from text and generates responses with 443 additional features." ) if __name__ == "__main__": iface.launch()