import warnings import numpy as np import pandas as pd import os import json import random import gradio as gr import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset, IterableDataset from sklearn.ensemble import IsolationForest, RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder from sklearn.neural_network import MLPClassifier from deap import base, creator, tools, algorithms from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, AutoModelForSequenceClassification import gc import multiprocessing as mp from joblib import Parallel, delayed warnings.filterwarnings('ignore', category=FutureWarning, module='huggingface_hub.file_download') # Initialize Example Emotions Dataset 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 # Memory-efficient Neural Network with PyTorch class MemoryEfficientNN(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(MemoryEfficientNN, self).__init__() self.layers = nn.Sequential( nn.Linear(input_size, hidden_size), nn.ReLU(), nn.Dropout(0.2), nn.Linear(hidden_size, hidden_size), nn.ReLU(), nn.Dropout(0.2), nn.Linear(hidden_size, num_classes) ) def forward(self, x): return self.layers(x) # Memory-efficient dataset class MemoryEfficientDataset(IterableDataset): def __init__(self, X, y, batch_size): self.X = X self.y = y self.batch_size = batch_size def __iter__(self): for i in range(0, len(self.y), self.batch_size): X_batch = self.X[i:i+self.batch_size].toarray() y_batch = self.y[i:i+self.batch_size] yield torch.FloatTensor(X_batch), torch.LongTensor(y_batch) # Train Memory-Efficient Neural Network X_train, X_test, y_train, y_test = train_test_split(contexts_encoded, emotions_target, test_size=0.2, random_state=42) input_size = X_train.shape[1] hidden_size = 64 num_classes = len(emotion_classes) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = MemoryEfficientNN(input_size, hidden_size, num_classes).to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) train_dataset = MemoryEfficientDataset(X_train, y_train, batch_size=32) train_loader = DataLoader(train_dataset, batch_size=None) num_epochs = 100 for epoch in range(num_epochs): for batch_X, batch_y in train_loader: batch_X, batch_y = batch_X.to(device), batch_y.to(device) outputs = model(batch_X) loss = criterion(outputs, batch_y) optimizer.zero_grad() loss.backward() optimizer.step() gc.collect() # Garbage collection after each epoch # Ensemble with Random Forest (memory-efficient) rf_model = RandomForestClassifier(n_estimators=50, random_state=42, n_jobs=-1) rf_model.fit(X_train, y_train) # Isolation Forest Anomaly Detection Model (memory-efficient) isolation_forest = IsolationForest(contamination=0.1, random_state=42, n_jobs=-1, max_samples='auto') isolation_forest.fit(X_train) # Fit the model before using it # 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} } total_percentage = 200 default_percentage = total_percentage / len(emotions) for emotion in emotions: emotions[emotion]['percentage'] = default_percentage 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): emotions['ideal_state']['percentage'] -= percentage emotions[emotion]['percentage'] += percentage emotions[emotion]['intensity'] = intensity 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() # Memory-efficient genetic algorithm for emotion evolution def evolve_emotions(): def evaluate(individual): ideal_state = individual[-1] other_emotions = individual[:-1] intensities = individual[-21:-1] return (abs(ideal_state - 100), sum(other_emotions), max(intensities) - min(intensities)) creator.create("FitnessMulti", base.Fitness, weights=(-1.0, -1.0, -1.0)) 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) + (lambda: random.uniform(80, 120),), n=1) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evaluate) toolbox.register("mate", tools.cxSimulatedBinaryBounded, low=0, up=120, eta=20.0) toolbox.register("mutate", tools.mutPolynomialBounded, low=0, up=120, eta=20.0, indpb=0.1) toolbox.register("select", tools.selNSGA2) population = toolbox.population(n=50) for gen in range(25): offspring = algorithms.varAnd(population, toolbox, cxpb=0.7, mutpb=0.3) fits = toolbox.map(toolbox.evaluate, offspring) for fit, ind in zip(fits, offspring): ind.fitness.values = fit population = toolbox.select(offspring + population, k=len(population)) best_individual = tools.selBest(population, k=1)[0] for idx, emotion in enumerate(emotions.keys()): if idx < len(emotions) - 1: emotions[emotion]['percentage'] = best_individual[idx] emotions[emotion]['intensity'] = best_individual[idx + len(emotions) - 1] else: emotions[emotion]['percentage'] = best_individual[-1] # Lazy loading for the language model def get_language_model(): model_name = 'bigscience/bloom-1b7' tokenizer = AutoTokenizer.from_pretrained(model_name) lm_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", low_cpu_mem_usage=True) return tokenizer, lm_model def generate_text(prompt, max_length=100): tokenizer, lm_model = get_language_model() input_ids = tokenizer.encode(prompt, return_tensors='pt').to(lm_model.device) with torch.no_grad(): output = lm_model.generate( input_ids, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2, do_sample=True, top_k=50, top_p=0.95, temperature=0.7 ) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) return generated_text model_name = "distilbert-base-uncased-finetuned-sst-2-english" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) def get_sentiment(text): result = sentiment_pipeline(text)[0] return f"Sentiment: {result['label']}, Score: {result['score']:.4f}" def get_emotional_response(context): context = normalize_context(context) context_encoded = encoder.transform([[context]]) # Neural network prediction with torch.no_grad(): nn_output = model(torch.FloatTensor(context_encoded.toarray()).to(device)) nn_prediction = nn_output.argmax(1).item() # Random Forest prediction rf_prediction = rf_model.predict(context_encoded)[0] # Weighted ensemble ensemble_prediction = (0.6 * nn_prediction + 0.4 * rf_prediction) predicted_emotion = emotion_classes[int(ensemble_prediction)] # Isolation Forest anomaly detection anomaly_score = isolation_forest.decision_function(context_encoded.toarray()) is_anomaly = isolation_forest.predict(context_encoded.toarray())[0] == -1 if is_anomaly: return "Anomaly detected in the emotional response." return f"Predicted Emotion: {predicted_emotion}" # Gradio interface def process_input(input_text): emotional_response = get_emotional_response(input_text) sentiment_response = get_sentiment(input_text) generated_text = generate_text(input_text) return { "Emotional Response": emotional_response, "Sentiment Response": sentiment_response, "Generated Text": generated_text } iface = gr.Interface( iface = gr.Interface( fn=process_input, inputs="text", outputs=[ gradio.components.Textbox(label="Emotional Response"), gradio.components.Textbox(label="Sentiment Response"), gradio.components.Textbox(label="Generated Text") ] ) iface.launch()