from flask import Flask, jsonify, request, render_template_string
import tensorflow as tf
from qiskit import QuantumCircuit, Aer, execute
import numpy as np
app = Flask(__name__)
HTML_TEMPLATE = '''
Gautam AI Neural Network Simulation
Gautam AI Neural Network Simulation
'''
@app.route('/')
def index():
return render_template_string(HTML_TEMPLATE)
@app.route('/run_neural_network', methods=['POST'])
def run_neural_network():
# Example neural network setup
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation='relu', input_shape=(4,)),
tf.keras.layers.Dense(3, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Dummy data
data = np.random.random((100, 4))
labels = np.random.randint(3, size=(100,))
model.fit(data, labels, epochs=1, verbose=0)
# Return dummy results
return jsonify({'message': 'Neural network trained', 'accuracy': 0.95})
@app.route('/run_quantum_circuit', methods=['GET'])
def run_quantum_circuit():
qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()
backend = Aer.get_backend('qasm_simulator')
job = execute(qc, backend, shots=1024)
result = job.result()
counts = result.get_counts()
return jsonify({'counts': counts})
if __name__ == '__main__':
app.run(debug=True)
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