python quantunbot.py

# Load Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Data preprocessing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

# Prepare data for the quantum circuit
n_qubits = 2
n_params = n_qubits

# Generate qubits
qubits = [cirq.GridQubit(0, i) for i in range(n_qubits)]

# Prepare parameters
params = sympy.symbols('theta(0:{})'.format(n_qubits))

# Create the quantum circuit
qnn_circuit = create_qnn_circuit(qubits, params)

# Convert the quantum circuit to a TensorFlow layer
qnn_layer = tfq.layers.PQC(qnn_circuit, readout_operators=cirq.PauliString(cirq.Z(qubits[-1])))

# Build the QNN model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(n_qubits),
    tf.keras.layers.Reshape((n_qubits, 1)),
    qnn_layer
])

# Compile the model
model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),
              metrics=['accuracy'])

def app():
    # Set the title of the app
    st.title('Iris Flower Classifier using Quantum Neural Network')

    # Ask the user to input values for the features
    feature_names = iris.feature_names
    features = []
    for name in feature_names:
        feature = st.number_input(name)
        features.append(feature)
    X_user = np.array(features).reshape(1, -1)
    X_user = scaler.transform(X_user)

    # Train the model
    model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=0)

    # Evaluate the model
    loss, accuracy = model.evaluate(X_test, y_test, verbose=0)

    # Predict the class of the input
    y_user = model.predict(X_user)
    class_names = iris.target_names
    class_idx = np.argmax(y_user, axis=1)[0]
    class_name = class_names[class_idx]

    # Display the predicted class
    st.write("Predicted class:", class_name)

    # Display the model accuracy
    st.write("Model accuracy:", accuracy)

# Run the if __name__ == '__main__':
    app()