Upload message (3).txt

This is the model code and fully open source it has bloom 1billon parameters chatbot and install any necessary libraries. has emotions, increased evolution and intelligence to max.

message (3).txt

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+
+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)