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ai_kubu-hai.h5

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+# 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())

pipeline.yaml

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+name: CI/CD Pipeline
+
+on:
+push:
+branches:
+  main
+pull_request:
+
+branches:
+  main
+
+
+jobs:
+build:
+runs-on: ubuntu-latest
+
+steps:
+  name: Checkout code
+
+uses: actions/checkout@v2
+
+  name: Set up Python
+
+uses: actions/setup-python@v2
+with:
+python-version: 3.8.0
+
+  name: Install dependencies
+
+run: |
+python -m pip install --upgrade pip
+pip install -r requirements.txt
+
+  name: Lint with flake8
+
+run: |
+pip install flake8
+# stop the build if there are Python syntax errors or undefined names
+flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+
+  name: Build Docker image
+
+run: docker build -t my-flask-api .
+
+  name: Run tests
+
+run: |
+docker run -d -p 5000:5000 my-flask-api
+# Add your test commands here
+
+  name: Push to Docker Hub
+
+run: |
+echo "${{ secrets.DOCKER_PASSWORD }}" | docker login -u "${{ secrets.DOCKER_USERNAME }}" --password-stdin
+docker tag my-flask-api:latest my-dockerhub-username/my-flask-api:latest
+docker push my-dockerhub-username/my-flask-api:latest