import numpy as np
import pandas as pd
import os
import json
import random
from sklearn.ensemble import IsolationForest
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 GPT2LMHeadModel, GPT2Tokenizer


# 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


# Train Neural Network
X_train, X_test, y_train, y_test = train_test_split(contexts_encoded, emotions_target, test_size=0.2, random_state=42)
model = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=1000, random_state=42)
model.fit(X_train, y_train)


# Isolation Forest Anomaly Detection Model
historical_data = np.array([model.predict(contexts_encoded)]).T
isolation_forest = IsolationForest(contamination=0.1, random_state=42)
isolation_forest.fit(historical_data)


# Emotional States with 20 emotions
emotions = {
    'joy': {'percentage': 10, 'motivation': 'positive'},
    'pleasure': {'percentage': 10, 'motivation': 'selfish'},
    'sadness': {'percentage': 10, 'motivation': 'negative'},
    'grief': {'percentage': 10, 'motivation': 'negative'},
    'anger': {'percentage': 10, 'motivation': 'traumatic or strong'},
    'calmness': {'percentage': 10, 'motivation': 'neutral'},
    'determination': {'percentage': 10, 'motivation': 'positive'},
    'resentment': {'percentage': 10, 'motivation': 'negative'},
    'glory': {'percentage': 10, 'motivation': 'positive'},
    'motivation': {'percentage': 10, 'motivation': 'positive'},
    'ideal_state': {'percentage': 100, 'motivation': 'balanced'},
    'fear': {'percentage': 10, 'motivation': 'defensive'},
    'surprise': {'percentage': 10, 'motivation': 'unexpected'},
    'anticipation': {'percentage': 10, 'motivation': 'predictive'},
    'trust': {'percentage': 10, 'motivation': 'reliable'},
    'disgust': {'percentage': 10, 'motivation': 'repulsive'},
    'optimism': {'percentage': 10, 'motivation': 'hopeful'},
    'pessimism': {'percentage': 10, 'motivation': 'doubtful'},
    'boredom': {'percentage': 10, 'motivation': 'indifferent'},
    'envy': {'percentage': 10, 'motivation': 'jealous'}
}


# Adjust all emotions to a total of 200%
total_percentage = 200
default_percentage = total_percentage / len(emotions)
for emotion in emotions:
    emotions[emotion]['percentage'] = default_percentage


emotion_history_file = 'emotion_history.json'


# Load historical data from file if exists
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 []


# Save historical data to file
def save_historical_data(historical_data, file_path=emotion_history_file):
    with open(file_path, 'w') as file:
        json.dump(historical_data, file)


# Load previous emotional states
emotion_history = load_historical_data()


# Function to update emotions
def update_emotion(emotion, percentage):
    emotions['ideal_state']['percentage'] -= percentage
    emotions[emotion]['percentage'] += percentage

    # Ensure total percentage remains 200%
    total_current = sum(e['percentage'] for e in emotions.values())
    adjustment = total_percentage - total_current
    emotions['ideal_state']['percentage'] += adjustment


# Function to normalize context
def normalize_context(context):
    return context.lower().strip()


# Function to evolve emotions using genetic algorithm
def evolve_emotions():
    # Define the fitness function
    def evaluate(individual):
        ideal_state = individual[-1]  # Last value is the ideal state percentage
        other_emotions = individual[:-1]  # All other emotions
        return abs(ideal_state - 100), sum(other_emotions)

    # Register the genetic algorithm components
    creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
    creator.create("Individual", list, fitness=creator.FitnessMin)

    # Create individuals and population
    toolbox = base.Toolbox()
    toolbox.register("attribute", lambda: random.uniform(0, 20))
    toolbox.register("individual", tools.initCycle, creator.Individual, toolbox.attribute, n=(len(emotions) - 1))
    toolbox.register("ideal_state", lambda: random.uniform(80, 120))
    toolbox.register("complete_individual", tools.initConcat, creator.Individual, toolbox.individual, toolbox.ideal_state)
    toolbox.register("population", tools.initRepeat, list, toolbox.complete_individual)

    # Register genetic operators
    toolbox.register("evaluate", evaluate)
    toolbox.register("mate", tools.cxBlend, alpha=0.5)
    toolbox.register("mutate", tools.mutGaussian, mu=10, sigma=5, indpb=0.3)
    toolbox.register("select", tools.selTournament, tournsize=3)

    # Initialize the population
    population = toolbox.population(n=10)

    # Run genetic algorithm
    population, log = algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.2, ngen=20, verbose=False)

    # Update the emotions with the best individual
    best_individual = tools.selBest(population, k=1)[0]
    for idx, emotion in enumerate(emotions.keys()):
        emotions[emotion]['percentage'] = best_individual[idx]


# Function to get emotional response
def get_emotional_response(context):
    # Normalize context
    context = normalize_context(context)

    # Encode the context and predict the emotion using the neural network
    context_encoded = encoder.transform([[context]]).toarray()
    prediction = model.predict(context_encoded)
    predicted_emotion = emotion_classes[prediction[0]]

    # Check for anomalies using Isolation Forest
    anomaly_score = isolation_forest.decision_function([prediction])[0]
    if anomaly_score < -0.5:
        print("Anomalous context detected. Adjusting emotional response.")
        update_emotion('calmness', 20)
    else:
        # Define emotional responses
        if predicted_emotion == 'joy':
            update_emotion('joy', 20)
            update_emotion('pleasure', 20)
        elif predicted_emotion == 'sadness':
            update_emotion('sadness', 20)
            update_emotion('grief', 20)
        elif predicted_emotion == 'anger':
            update_emotion('anger', 20)
        elif predicted_emotion == 'determination':
            update_emotion('determination', 20)
        elif predicted_emotion == 'resentment':
            update_emotion('resentment', 20)
        elif predicted_emotion == 'glory':
            update_emotion('glory', 20)
        elif predicted_emotion == 'motivation':
            update_emotion('motivation', 20)
        elif predicted_emotion == 'surprise':
            update_emotion('surprise', 20)
        elif predicted_emotion == 'fear':
            update_emotion('fear', 20)
        elif predicted_emotion == 'trust':
            update_emotion('trust', 20)
        elif predicted_emotion == 'disgust':
            update_emotion('disgust', 20)
        elif predicted_emotion == 'optimism':
            update_emotion('optimism', 20)
        elif predicted_emotion == 'pessimism':
            update_emotion('pessimism', 20)
        elif predicted_emotion == 'boredom':
            update_emotion('boredom', 20)
        elif predicted_emotion == 'envy':
            update_emotion('envy', 20)
        else:
            update_emotion('calmness', 20)

    # Record the current emotional state in history
    emotion_state = {emotion: data['percentage'] for emotion, data in emotions.items()}
    emotion_history.append(emotion_state)

    # Save the history to file
    save_historical_data(emotion_history)

    # Print the current emotional state
    for emotion, data in emotions.items():
        print(f"{emotion.capitalize()}: {data['percentage']}% ({data['motivation']} motivation)")


# Function to handle idle state using genetic algorithm
def handle_idle_state():
    print("Entering idle state...")
    evolve_emotions()
    print("Emotions evolved")
    for emotion, data in emotions.items():
        print(f"{emotion.capitalize()}: {data['percentage']}% ({data['motivation']} motivation)")


# S.O.U.L. (Self-Organizing Universal Learning) Function
class SOUL:
    def __init__(self, gpt2_model='gpt2'):
        self.gpt2_tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model)
        self.gpt2_model = GPT2LMHeadModel.from_pretrained(gpt2_model)

    def generate_text(self, prompt, max_length=50):
        inputs = self.gpt2_tokenizer.encode(prompt, return_tensors='pt')
        outputs = self.gpt2_model.generate(inputs, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2)
        return self.gpt2_tokenizer.decode(outputs[0], skip_special_tokens=True)

    def bridge_ai(self, prompt):
        # Generate the response using GPT-2
        print("\nGPT-2 Response:")
        gpt2_response = self.generate_text(prompt)
        print(gpt2_response)

        # Get the emotional response
        print("\nEmotional Response:")
        get_emotional_response(gpt2_response)


# Example usage of S.O.U.L. function
soul = SOUL()


# Test open-ended conversation with emotional response
while True:
    user_input = input("You: ")
    soul.bridge_ai(user_input)