import warnings import numpy as np import pandas as pd import os import json import random import gradio as gr from sklearn.ensemble import IsolationForest, RandomForestClassifier, GradientBoostingClassifier from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.preprocessing import OneHotEncoder from sklearn.neural_network import MLPClassifier from sklearn.metrics import accuracy_score from deap import base, creator, tools, algorithms from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset 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 encoder = OneHotEncoder(handle_unknown='ignore') contexts_encoded = encoder.fit_transform(df[['context']]).toarray() # Encoding emotions emotions_target = df['emotion'].astype('category').cat.codes emotion_classes = df['emotion'].astype('category').cat.categories # Advanced Neural Network with PyTorch class AdvancedNN(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(AdvancedNN, self).__init__() self.layer1 = nn.Linear(input_size, hidden_size) self.layer2 = nn.Linear(hidden_size, hidden_size) self.layer3 = nn.Linear(hidden_size, num_classes) self.relu = nn.ReLU() self.dropout = nn.Dropout(0.2) def forward(self, x): x = self.relu(self.layer1(x)) x = self.dropout(x) x = self.relu(self.layer2(x)) x = self.dropout(x) x = self.layer3(x) return x # Train Advanced Neural Network X_train, X_test, y_train, y_test = train_test_split(contexts_encoded, emotions_target, test_size=0.2, random_state=42) X_train = X_train.toarray() # Convert sparse matrix to dense y_train = y_train.to_numpy() # Convert pandas Series to numpy array input_size = X_train.shape[1] hidden_size = 64 num_classes = len(emotion_classes) model = AdvancedNN(input_size, hidden_size, num_classes) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) train_dataset = TensorDataset(torch.FloatTensor(X_train), torch.LongTensor(y_train)) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) # Ensemble with Random Forest and Gradient Boosting rf_model = RandomForestClassifier(n_estimators=100, random_state=42) gb_model = GradientBoostingClassifier(n_estimators=100, random_state=42) rf_model.fit(X_train, y_train) gb_model.fit(X_train, y_train) # Isolation Forest Anomaly Detection Model historical_data = np.array([model(torch.FloatTensor(contexts_encoded)).argmax(1).numpy()]).T isolation_forest = IsolationForest(contamination=0.1, random_state=42) isolation_forest.fit(historical_data) # 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() # Advanced 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=100) algorithms.eaMuPlusLambda(population, toolbox, mu=100, lambda_=100, cxpb=0.7, mutpb=0.3, ngen=50, verbose=False) 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] # Initialize the pre-trained language model (BLOOM-1b7) model_name = 'bigscience/bloom-1b7' tokenizer = AutoTokenizer.from_pretrained(model_name) lm_model = AutoModelForCausalLM.from_pretrained(model_name) def generate_text(prompt, max_length=150): input_ids = tokenizer.encode(prompt, return_tensors='pt') 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 sentiment_pipeline = pipeline("sentiment-analysis", model=model_name, 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]]).toarray() # Advanced NN prediction nn_output = model(torch.FloatTensor(context_encoded)) nn_prediction = nn_output.argmax(1).item() # Ensemble predictions rf_prediction = rf_model.predict(context_encoded)[0] gb_prediction = gb_model.predict(context_encoded)[0] # Weighted ensemble ensemble_prediction = (0.4 * nn_prediction + 0.3 * rf_prediction + 0.3 * gb_prediction) predicted_emotion = emotion_classes[int(round(ensemble_prediction))] # Anomaly detection anomaly_score = isolation_forest.decision_function(np.array([[nn_prediction]])) is_anomaly = anomaly_score < 0 # Calculate emotion intensity based on model confidence nn_proba = torch.softmax(nn_output, dim=1).max().item() rf_proba = rf_model.predict_proba(context_encoded).max() gb_proba = gb_model.predict_proba(context_encoded).max() intensity = (nn_proba + rf_proba + gb_proba) / 3 * 10 # Scale to 0-10 update_emotion(predicted_emotion, 20, intensity) evolve_emotions() emotion_history.append(emotions.copy()) save_historical_data(emotion_history) response = f"Predicted Emotion: {predicted_emotion}\n" response += f"Emotion Details: {emotions[predicted_emotion]}\n" response += f"Anomaly Detected: {'Yes' if is_anomaly else 'No'}\n" response += f"Emotion Intensity: {intensity:.2f}/10\n" response += f"Current Emotional State: {json.dumps(emotions, indent=2)}" return response def process_input(input_text): emotional_response = get_emotional_response(input_text) sentiment = get_sentiment(input_text) generated_text = generate_text(f"Based on the emotion analysis: {emotional_response}\nGenerate a response: {input_text}") return f"{emotional_response}\n\nSentiment Analysis: {sentiment}\n\nGenerated Response: {generated_text}" # Gradio Interface iface = gr.Interface( fn=process_input, inputs="text", outputs="text", title="Advanced Emotion Analysis and Text Generation with BLOOM-1b7", description="Enter a sentence for comprehensive emotion analysis, sentiment analysis, and context-aware text generation using advanced AI models." ) if __name__ == "__main__": iface.launch()