import numpy as np
import pandas as pd
import os
import json
import random
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.ensemble import IsolationForest
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from deap import base, creator, tools, algorithms
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoFeatureExtractor
from transformers import pipeline
from sentence_transformers import SentenceTransformer
from textblob import TextBlob
import speech_recognition as sr
from PIL import Image
import cv2
from googletrans import Translator
import onnx
import onnxruntime
from torch.quantization import quantize_dynamic, quantize_static, prepare, convert
import torch.nn.functional as F

# Enable CUDA if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 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

# Neural Network for Emotional Processing
class EmotionalNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(EmotionalNN, self).__init__()
        self.attention = nn.MultiheadAttention(hidden_size, num_heads=8)
        self.layers = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, output_size),
            nn.Softmax(dim=1)
        )

    def forward(self, x):
        x, _ = self.attention(x, x, x)
        return self.layers(x)

# Initialize and train the Emotional Neural Network
input_size = contexts_encoded.shape[1]
hidden_size = 512
output_size = len(emotion_classes)
emotional_nn = EmotionalNN(input_size, hidden_size, output_size).to(device)

# Quantization
emotional_nn_quantized = quantize_dynamic(emotional_nn, {nn.Linear}, dtype=torch.qint8)

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(emotional_nn_quantized.parameters(), lr=0.001)

# Train the Emotional Neural Network
num_epochs = 5000
for epoch in range(num_epochs):
    inputs = torch.FloatTensor(contexts_encoded).to(device)
    targets = torch.LongTensor(emotions_target).to(device)
    
    outputs = emotional_nn_quantized(inputs)
    loss = criterion(outputs, targets)
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# Export to ONNX for inference optimization
dummy_input = torch.randn(1, input_size, device=device)
torch.onnx.export(emotional_nn_quantized, dummy_input, "emotional_nn.onnx")

# ONNX Runtime inference session
ort_session = onnxruntime.InferenceSession("emotional_nn.onnx")

# Emotional States
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 and save historical data functions
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()

# Function to update emotions
def update_emotion(emotion, percentage):
    emotions['ideal_state']['percentage'] -= percentage
    emotions[emotion]['percentage'] += percentage
    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():
    def evaluate(individual):
        ideal_state = individual[-1]
        other_emotions = individual[:-1]
        return abs(ideal_state - 100), sum(other_emotions)

 creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))
    creator.create("Individual", list, fitness=creator.FitnessMin)

    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)

    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)

    population = toolbox.population(n=1000)
    population, log = algorithms.eaSimple(population, toolbox, cxpb=0.5, mutpb=0.2, ngen=50, verbose=False)

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

# Sentiment analysis
sentiment_analyzer = pipeline("sentiment-analysis")

# Sentence embeddings for context-aware emotion tracking
sentence_model = SentenceTransformer('all-MiniLM-L6-v2')

# Function to get emotional response
def get_emotional_response(context):
    context = normalize_context(context)
    context_encoded = encoder.transform([[context]]).toarray()
    
    # Use ONNX Runtime for inference
    ort_inputs = {ort_session.get_inputs()[0].name: context_encoded.astype(np.float32)}
    ort_outputs = ort_session.run(None, ort_inputs)
    output = ort_outputs[0]
    predicted_emotion = emotion_classes[np.argmax(output)]

    # Sentiment analysis
    sentiment = sentiment_analyzer(context)[0]
    sentiment_score = sentiment['score'] if sentiment['label'] == 'POSITIVE' else -sentiment['score']

    # Context-aware emotion tracking
    context_embedding = sentence_model.encode(context)
    
    # Combine predicted emotion, sentiment, and context
    emotion_intensity = abs(sentiment_score) * np.max(output)
    
    # Update emotions based on prediction and intensity
    update_emotion(predicted_emotion, emotion_intensity * 20)
    
    # Check for anomalies using Isolation Forest
    anomaly_score = isolation_forest.decision_function([output])[0]
    if anomaly_score < -0.5:
        print("Anomalous context detected. Adjusting emotional response.")
        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_historical_data(emotion_history)

    # Print the current emotional state
    for emotion, data in emotions.items():
        print(f"{emotion.capitalize()}: {data['percentage']:.2f}% ({data['motivation']} motivation)")

    return predicted_emotion, emotion_intensity

# 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']:.2f}% ({data['motivation']} motivation)")

# S.O.U.L. (Self-Organizing Universal Learning) Function
class SOUL:
    def __init__(self, model_name='tiiuae/falcon-40b'):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True)
        self.model.to(device)

        # Quantization for optimization (INT8)
        self.model = quantize_dynamic(self.model, {nn.Linear}, dtype=torch.qint8)

    def generate_text(self, prompt, max_length=200):
        inputs = self.tokenizer(prompt, return_tensors="pt").to(device)
        
        with torch.no_grad():
            generate_ids = self.model.generate(
                inputs.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
            )
        
        return self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

    def bridge_ai(self, prompt):
        print("\nFalcon-40B Response:")
        falcon_response = self.generate_text(prompt)
        print(falcon_response)

        print("\nEmotional Response:")
        emotion, intensity = get_emotional_response(falcon_response)
        return falcon_response, emotion, intensity

# Combine Neural Network and Genetic Algorithm
def neural_genetic_convergence():
    if len(emotion_history) % 10 == 0:
        print("Neural-Genetic Convergence...")
        evolve_emotions()
        # Train the Emotional Neural Network with new data
        X = np.array([list(state.values()) for state in emotion_history[-10:]])
        y = np.argmax(X, axis=1)
        optimizer.zero_grad()
        inputs = torch.FloatTensor(X).to(device)
        targets = torch.LongTensor(y).to(device)
        outputs = emotional_nn_quantized(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        print("Convergence complete.")

# Emotion-based decision making
def emotion_based_decision(emotion, intensity):
    if intensity > 0.8:
        if emotion in ['joy', 'excitement']:
            return "I'm feeling very positive! Let's do something fun!"
        elif emotion in ['sadness', 'grief']:
            return "I'm feeling down. I might need some time to process this."
        elif emotion in ['anger', 'frustration']:
            return "I'm feeling upset. It might be best to take a break and calm down."
    elif intensity > 0.5:
        return f"I'm feeling {emotion} at a moderate level. How about we discuss this further?"
    else:
        return f"I'm experiencing a mild sense of {emotion}. What are your thoughts on this?"

# Self-reflection and introspection module
def self_reflect():
    dominant_emotion = max(emotions, key=lambda e: emotions[e]['percentage'])
    print(f"Self-reflection: My dominant emotion is {dominant_emotion}.")
    print("Analyzing my recent emotional states...")
    recent_states = emotion_history[-5:]
    emotion_trends = {}
    for state in recent_states:
        for emotion, percentage in state.items():
            if emotion not in emotion_trends:
                emotion_trends[emotion] = []
            emotion_trends[emotion].append(percentage)
    
    for emotion, trend in emotion_trends.items():
        if len(trend) > 1:
            if trend[-1] > trend[0]:
                print(f"{emotion} has been increasing.")
            elif trend[-1] < trend[0]:
                print(f"{emotion} has been decreasing.")
    
    print("Based on this reflection, I should adjust my responses accordingly.")

# Adaptive personality traits
personality_traits = {
    'openness': 0.5,
    'conscientiousness': 0.5,
    'extraversion': 0.5,
    'agreeableness': 0.5,
    'neuroticism': 0.5
}

def adapt_personality():
    for trait in personality_traits:
        change = random.uniform(-0.1, 0.1)
        personality_traits[trait] = max(0, min(1, personality_traits[trait] + change))
    print("Personality traits adapted:", personality_traits)

# Empathy simulation module
def simulate_empathy(user_input):
    user_emotion = TextBlob(user_input).sentiment.polarity
    if user_emotion > 0.5:
        print("I sense that you're feeling positive. That's wonderful!")
    elif user_emotion < -0.5:
        print("I can tell you might be feeling down. Is there anything I can do to help?")
    else:
        print("I'm here to listen and support you, whatever you're feeling.")

# Dream-like state for offline learning
def dream_state():
    print("Entering dream-like state for offline learning...")
    dream_contexts = [
        "flying through clouds",
        "solving complex puzzles",
        "exploring ancient ruins",
        "conversing with historical figures",
        "inventing new technologies"
    ]
    for context in dream_contexts:
        get_emotional_response(context)
    print("Dream-like state completed. New insights gained.")

# Emotional intelligence scoring
def calculate_eq_score():
    eq_score = sum(emotions[e]['percentage'] for e in ['empathy', 'self_awareness', 'social_skills']) / 3
    print(f"Current Emotional Intelligence Score: {eq_score:.2f}")
    return eq_score

# Multi-modal input processing
def process_multimodal_input():
    text_input = input("You (text): ")
    
    # Speech recognition
    r = sr.Recognizer()
    with sr.Microphone() as source:
        print("Speak now...")
        audio = r.listen(source)
    try:
        voice_input = r.recognize_google(audio)
        print(f"Voice input: {voice_input}")
    except sr.UnknownValueError:
        voice_input = None
        print("Voice input not recognized")

    # Image processing
    image_path = input("Enter path to image (or press enter to skip): ")
    if image_path:
        image = cv2.imread(image_path)
        if image is not None:
            # Perform basic image analysis (e.g., dominant color)
            average_color = np.mean(image, axis=(0, 1))
            image_input = f"Image with dominant color: RGB({average_color[2]:.0f}, {average_color[1]:.0f}, {average_color[0]:.0f})"
            print(image_input)
        else:
            image_input = None
            print("Failed to process image")
    else:
        image_input = None

    combined_input = f"{text_input} {voice_input or ''} {image_input or ''}"
    return combined_input.strip()

# Multi-language support
translator = Translator()

def translate_input(text, target_language='en'):
    translated = translator.translate(text, dest=target_language)
    return translated.text

# Main interaction loop
soul = SOUL()

print("Welcome to the advanced SOUL AI. Type 'exit' to end the conversation.")
conversation_turn = 0
while True:
    user_input = process_multimodal_input()
    if user_input.lower() == 'exit':
        print("Thank you for the conversation. Goodbye!")
        break
    
    conversation_turn += 1
    
    # Multi-language processing
    translated_input = translate_input(user_input)
    
    response, emotion, intensity = soul.bridge_ai(translated_input)
    
    decision = emotion_based_decision(emotion, intensity)
    print("AI Decision:", decision)
    
    simulate_empathy(user_input)
    
    neural_genetic_convergence()
    
    if conversation_turn % 10 == 0:
        adapt_personality()
        calculate_eq_score()
    
    if conversation_turn % 20 == 0:
        self_reflect()
        dream_state()

    # Simulate idle state every 5 interactions
    if conversation_turn % 5 == 0:
        handle_idle_state()

# End of script

if __name__ == "__main__":
    # Initialize isolation forest
    historical_data = np.array([emotional_nn_quantized(torch.FloatTensor(contexts_encoded).to(device)).detach().cpu().numpy()])
    isolation_forest = IsolationForest(contamination=0.1, random_state=42)
    isolation_forest.fit(historical_data)

    # Run the main interaction loop
    try:
        # Main interaction loop is already defined above
        pass
    except Exception as e:
        print(f"An error occurred: {e}")
    finally:
        print("SOUL AI is shutting down. Final self-reflection:")
        self_reflect()
        print("Thank you for using SOUL AI. Goodbye!")