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https://github.com/ChatGPTNextWeb/ChatGPT-Next-Web.git
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85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
# Import necessary libraries
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import numpy as np
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import tensorflow as tf
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from sklearn.linear_model import LinearRegression
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from sklearn.datasets import make_classification
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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# Example 1: Reactive Machine (Simple Rule-Based System)
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def reactive_machine(input_value):
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if input_value > 0:
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return "Positive"
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else:
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return "Negative"
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# Example 2: Limited Memory (Simple Machine Learning Model)
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def limited_memory_model():
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# Generate a simple dataset
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X, y = make_classification(n_samples=1000, n_features=20, random_state=42)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Train a simple linear regression model
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model = LinearRegression()
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model.fit(X_train, y_train)
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# Make predictions
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predictions = model.predict(X_test)
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predictions = [1 if p > 0.5 else 0 for p in predictions]
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# Evaluate the model
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accuracy = accuracy_score(y_test, predictions)
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return accuracy
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# Example 3: Theory of Mind (Simple Neural Network)
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def theory_of_mind_model():
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# Create a simple neural network
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model = tf.keras.Sequential([
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tf.keras.layers.Dense(128, activation='relu', input_shape=(20,)),
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tf.keras.layers.Dense(64, activation='relu'),
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tf.keras.layers.Dense(1, activation='sigmoid')
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])
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# Compile the model
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
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# Generate a simple dataset
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X, y = make_classification(n_samples=1000, n_features=20, random_state=42)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Train the model
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model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
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# Evaluate the model
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loss, accuracy = model.evaluate(X_test, y_test)
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return accuracy
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# Example 4: General AI (Advanced Language Model)
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def general_ai_model(prompt):
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# Load pre-trained GPT-2 model and tokenizer
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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model = GPT2LMHeadModel.from_pretrained("gpt2")
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# Encode the input prompt
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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# Generate a response
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outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
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# Decode the response
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return response
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# Example 5: Self-Aware AI (Theoretical Concept)
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def self_aware_ai():
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return "Self-aware AI is a theoretical concept and not yet achievable with current technology."
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# Main function to run examples
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if __name__ == "__main__":
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print("Reactive Machine Output:", reactive_machine(5))
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print("Limited Memory Model Accuracy:", limited_memory_model())
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print("Theory of Mind Model Accuracy:", theory_of_mind_model())
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print("General AI Model Response:", general_ai_model("this is the future of AI?"))
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print("Self-Aware AI:", self_aware_ai())
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