"""
Standalone Flask MCP Server
Extracted from streamlit.py to run as separate service
"""
import sys
import os
sys.path.insert(0, '/app')

from flask import Flask, request, jsonify
from flask_cors import CORS
import numpy as np
import warnings

warnings.filterwarnings('ignore')

# Helper function to convert numpy arrays to JSON-serializable format
def clean_for_json(obj):
    """Convert numpy arrays and other non-serializable objects to JSON-safe format"""
    if isinstance(obj, dict):
        return {k: clean_for_json(v) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [clean_for_json(item) for item in obj]
    elif isinstance(obj, np.ndarray):
        return obj.tolist()
    elif isinstance(obj, (np.integer, np.int64, np.int32)):
        return int(obj)
    elif isinstance(obj, (np.floating, np.float64, np.float32)):
        if np.isnan(obj) or np.isinf(obj):
            return 0.0
        return float(obj)
    else:
        return obj

# Import TSNEExplorer from streamlit.py
try:
    import importlib.util
    spec = importlib.util.spec_from_file_location("streamlit_module", "/app/streamlit.py")
    streamlit_module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(streamlit_module)
    TSNEExplorer = streamlit_module.TSNEExplorer
except Exception as e:
    print(f"Error importing TSNEExplorer: {e}")
    TSNEExplorer = None

app = Flask(__name__)
CORS(app)
backend = None

def get_backend():
    global backend
    if backend is None and TSNEExplorer is not None:
        backend = TSNEExplorer()
    return backend

@app.route('/mcp/health', methods=['GET'])
def health():
    return jsonify({
        'status': 'ok',
        'service': 'mcp-server',
        'message': 'MCP server running on HF Spaces'
    })

@app.route('/mcp/generate_simplex_points', methods=['POST'])
def generate_simplex_points():
    try:
        data = request.json
        backend = get_backend()
        result = backend.generate_simplex_points(
            int(data['n']),
            int(data['d']),
            int(data['k']),
            int(data.get('seed', 42))
        )
        return jsonify(clean_for_json(result))
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/mcp/load_mnist', methods=['POST'])
def load_mnist():
    try:
        data = request.json
        backend = get_backend()
        result = backend.load_mnist(
            int(data.get('max_samples', 1000)),
            data.get('subset', 'train')
        )
        return jsonify(clean_for_json(result))
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/mcp/run_tsne', methods=['POST'])
def run_tsne():
    try:
        data = request.json
        backend = get_backend()
        X = np.array(data['X'])
        result = backend.run_tsne(
            X,
            int(data.get('perplexity', 30)),
            int(data.get('learning_rate', 200)),
            int(data.get('n_iter', 1000)),
            int(data.get('early_exaggeration', 12)),
            float(data.get('momentum', 0.8)),
            int(data.get('seed', 42))
        )
        return jsonify(clean_for_json(result))
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/mcp/run_clustering', methods=['POST'])
def run_clustering():
    try:
        data = request.json
        backend = get_backend()
        Y = np.array(data['Y'])
        result = backend.run_clustering(
            Y,
            data.get('method', 'kmeans'),
            int(data.get('k', 3)),
            float(data.get('eps', 0.5)),
            int(data.get('min_samples', 5))
        )
        return jsonify(clean_for_json(result))
    except Exception as e:
        return jsonify({'success': False, 'error': str(e)}), 500

if __name__ == '__main__':
    print('Starting Flask MCP Server on port 5000...')
    app.run(host='0.0.0.0', port=5000, debug=False, threaded=True)