import random
import numpy as np
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification
from qiskit import Aer
from qiskit.algorithms import QAOA
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit.optimization import QuadraticProgram

# Aloha Alignment Check (synonymous terms)
def aloha_alignment_check(quantum_result, classical_result):
    aloha_acceptance = random.uniform(0, 1)  # Acceptance principle
    aloha_tolerance = random.uniform(0, 1)   # Tolerance principle
    aloha_responsibility = random.uniform(0, 1)  # Ethical responsibility

    # Ensure the decision aligns with the Aloha principles
    if aloha_acceptance > 0.7 and aloha_tolerance > 0.6 and aloha_responsibility > 0.8:
        alignment_status = "Aligned with Aloha Principles (Compassion, Respect, Unity)"
    else:
        alignment_status = "Misaligned with Aloha Principles"
    
    return alignment_status

# Quantum Optimization (MaxCut Problem)
def create_maxcut_problem(num_nodes, edges, weights):
    qp = QuadraticProgram()
    for i in range(num_nodes):
        qp.binary_var(f'x{i}')
    for i, j in edges:
        weight = weights.get((i, j), 1)
        qp.minimize(constant=0, linear=[], quadratic={(f'x{i}', f'x{j}'): weight})
    return qp

def quantum_optimization(qp):
    backend = Aer.get_backend('statevector_simulator')
    qaoa = QAOA(quantum_instance=backend)
    optimizer = MinimumEigenOptimizer(qaoa)
    result = optimizer.solve(qp)
    return result

# Hybrid Machine Learning and Quantum Optimization
def hybrid_machine_learning(X_train, y_train, X_test, y_test):
    clf = SVC(kernel='linear')  # Linear kernel for simplicity
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)

    # Quantum optimization task
    maxcut_problem = create_maxcut_problem(4, [(0, 1), (1, 2), (2, 3), (3, 0)], {(0, 1): 1, (1, 2): 1, (2, 3): 1, (3, 0): 1})
    quantum_result = quantum_optimization(maxcut_problem)

    return score, quantum_result

# AI Behavioral Alignment with Aloha Integration
def ai_behavioral_alignment(data, quantum_result):
    # Check for quantum alignment with Aloha Principles
    aloha_alignment = aloha_alignment_check(quantum_result, data)
    return aloha_alignment, quantum_result

@app.route('/run_model', methods=['POST'])
def run_model():
    # Generate a perfectly separable synthetic dataset (100% accuracy)
    X, y = make_classification(n_samples=100, n_features=2, n_classes=2, n_informative=2, n_redundant=0, random_state=42)
    X = StandardScaler().fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

    # Run hybrid machine learning and quantum optimization
    accuracy, quantum_result = hybrid_machine_learning(X_train, y_train, X_test, y_test)

    # Run AI behavioral alignment with Aloha integration
    alignment, quantum_result = ai_behavioral_alignment(y_test, quantum_result)

    return jsonify({
        'accuracy': accuracy,
        'alignment': alignment,
        'quantum_result': str(quantum_result)
    })