Create Cain.

Cain.

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+
+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!")