# Import necessary libraries
import numpy as np
import tensorflow as tf
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Example 1: Reactive Machine (Simple Rule-Based System)
def reactive_machine(input_value):
if input_value > 0:
return "Positive"
else:
return "Negative"

# Example 2: Limited Memory (Simple Machine Learning Model)
def limited_memory_model():
# Generate a simple dataset
X, y = make_classification(n_samples=1000, n_features=20, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a simple linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)
predictions = [1 if p > 0.5 else 0 for p in predictions]

# Evaluate the model
accuracy = accuracy_score(y_test, predictions)
return accuracy

# Example 3: Theory of Mind (Simple Neural Network)
def theory_of_mind_model():
# Create a simple neural network
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(20,)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Generate a simple dataset
X, y = make_classification(n_samples=1000, n_features=20, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)

# Evaluate the model
loss, accuracy = model.evaluate(X_test, y_test)
return accuracy

# Example 4: General AI (Advanced Language Model)
def general_ai_model(prompt):
# Load pre-trained GPT-2 model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Encode the input prompt
inputs = tokenizer.encode(prompt, return_tensors="pt")

# Generate a response
outputs = model.generate(inputs, max_length=100, num_return_sequences=1)

# Decode the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response

# Example 5: Self-Aware AI (Theoretical Concept)
def self_aware_ai():
return "Self-aware AI is a theoretical concept and not yet achievable with current technology."

# Main function to run examples
if __name__ == "__main__":
print("Reactive Machine Output:", reactive_machine(5))
print("Limited Memory Model Accuracy:", limited_memory_model())
print("Theory of Mind Model Accuracy:", theory_of_mind_model())
print("General AI Model Response:", general_ai_model("this is the future of AI?"))
print("Self-Aware AI:", self_aware_ai())