Update main.py

src/cosmic_pi_network/main.py

@@ -3,6 +3,10 @@
 from cosmic_pi_network.data_preprocessing import DataPreprocessing
 from cosmic_pi_network.neural_network import NeuralNetwork
 from cosmic_pi_network.nlp import NLP
+from cosmic_pi_network.quantum_computing import QuantumCircuit
+from cosmic_pi_network.artificial_intelligence import AIAgent
+from cosmic_pi_network.cryptography import Crypto
+from cosmic_pi_network.machine_learning import ReinforcementLearning
 
 def main():
     api = API()
@@ -23,7 +27,7 @@ def main():
     thresh_image = computer_vision.apply_threshold(grayscale_image)
     edges_image = computer_vision.detect_edges(grayscale_image)
     faces = computer_vision.detect_faces(grayscale_image)
-    print("Number offaces detected:", len(faces))
+    print("Number of faces detected:", len(faces))
 
     data_preprocessing = DataPreprocessing()
     data = data_preprocessing.load_data("data.csv")
@@ -36,5 +40,36 @@ def main():
     loss, accuracy = neural_network.evaluate_model(model, X_test_scaled, y_test)
     print("Loss:", loss, "Accuracy:", accuracy)
 
+    quantum_circuit = QuantumCircuit()
+    qubits = quantum_circuit.create_qubit(2)
+    gates = [cirq.H(qubits[0]), cirq.CNOT(qubits[0], qubits[1])]
+    circuit = quantum_circuit.create_circuit(qubits, gates)
+    result = quantum_circuit.simulate_circuit(circuit)
+    print("Result:", result)
+
+    ai_agent = AIAgent()
+    X = np.array([[1, 2], [3, 4], [5, 6]])
+    y = np.array([0, 0, 1])
+    clf = ai_agent.create_decision_tree(X, y)
+    prediction = ai_agent.make_prediction(clf, X)
+    print("Prediction:", prediction)
+    accuracy = ai_agent.evaluate_model(clf, X, y)
+    print("Accuracy:", accuracy)
+
+    crypto = Crypto()
+    key = crypto.generate_key_pair()
+    data = b"Hello, World!"
+    encrypted_data = crypto.encrypt_data(key, data)
+    print("Encrypted data:", encrypted_data)
+    decrypted_data = crypto.decrypt_data(key, encrypted_data)
+    print("Decrypted data:", decrypted_data)
+
+    reinforcement_learning = ReinforcementLearning()
+    env = reinforcement_learning.create_environment("CartPole-v1")
+    model = reinforcement_learning.create_agent(env)
+    model = reinforcement_learning.train_agent(model, env)
+    rewards = reinforcement_learning.evaluate_agent(model, env)
+    print("Rewards:", rewards)
+
 if __name__ == "__main__":
     main()