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from flask import Flask, render_template_string, request, jsonify
import numpy as np
from sklearn.datasets import make_classification, make_regression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import (
accuracy_score, precision_score, recall_score, f1_score,
mean_squared_error, mean_absolute_error, r2_score
)
import torch
import torch.nn as nn
import torch.optim as optim
import time
import logging
import os
app = Flask(__name__)
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
HTML_TEMPLATE = '''<!DOCTYPE html>
<html>
<head>
<title>ML Loss Function Playground</title>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
<script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet">
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
body { font-family: 'Inter', sans-serif; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); min-height: 100vh; padding: 20px; }
.header-banner { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 40px 20px; text-align: center; border-radius: 15px 15px 0 0; box-shadow: 0 4px 15px rgba(0,0,0,0.2); }
.header-banner h1 { font-size: 2.5em; font-weight: 700; margin-bottom: 10px; }
.container { max-width: 1600px; margin: 0 auto; background: white; border-radius: 15px; box-shadow: 0 10px 40px rgba(0,0,0,0.2); overflow: hidden; }
.tab-navigation { display: flex; background: #f8f9fa; border-bottom: 2px solid #e0e0e0; }
.tab-button { flex: 1; padding: 20px; background: transparent; border: none; font-size: 1.1em; font-weight: 600; color: #666; cursor: pointer; transition: all 0.3s ease; border-bottom: 3px solid transparent; }
.tab-button:hover { background: #fff; color: #667eea; }
.tab-button.active { background: white; color: #667eea; border-bottom: 3px solid #667eea; }
.tab-content { display: none; padding: 40px; }
.tab-content.active { display: block; }
.section-title { font-size: 1.8em; color: #333; margin: 30px 0 20px; padding-bottom: 10px; border-bottom: 3px solid #667eea; }
.controls { display: grid; grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)); gap: 25px; margin-bottom: 30px; }
.control-group { background: #f8f9fa; padding: 20px; border-radius: 10px; border: 2px solid transparent; }
.control-group:hover { border-color: #667eea; }
label { font-weight: 600; margin-bottom: 8px; display: block; }
input, select { width: 100%; padding: 12px; border: 2px solid #e0e0e0; border-radius: 8px; font-size: 15px; }
input:focus, select:focus { border-color: #667eea; outline: none; }
.checkbox-group { display: flex; flex-wrap: wrap; gap: 10px; background: #f8f9fa; padding: 10px; border-radius: 10px; justify-content: flex-start; }
.checkbox-group label { display: flex; align-items: center; gap: 5px; cursor: pointer; padding: 5px; border-radius: 6px; transition: background 0.2s; white-space: nowrap; }
.checkbox-group label:hover { background: #e0e0e0; }
.checkbox-group input[type="checkbox"] { width: 16px; height: 16px; margin: 0; }
button { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 18px 40px; border: none; border-radius: 10px; font-size: 18px; font-weight: 600; margin: 30px auto; display: block; width: fit-content; }
button:disabled { background: #ccc; cursor: not-allowed; }
#loading { display: none; text-align: center; padding: 40px; }
.plot-container { margin: 40px 0; background: white; padding: 25px; border-radius: 12px; box-shadow: 0 2px 10px rgba(0,0,0,0.08); }
.summary-table { width: 100%; border-collapse: collapse; margin-top: 20px; }
.summary-table th, .summary-table td { padding: 15px; text-align: left; border-bottom: 1px solid #eee; }
.summary-table th { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; }
.best-metric { background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%); color: white; font-weight: 600; padding: 8px; border-radius: 6px; }
.loss-function-card { background: white; padding: 25px; border-radius: 12px; margin: 20px 0; box-shadow: 0 2px 10px rgba(0,0,0,0.1); border-left: 5px solid #667eea; }
.loss-function-card .formula { background: #f8f9fa; padding: 20px; border-radius: 8px; margin: 15px 0; overflow-x: auto; font-family: 'Courier New', monospace; }
.footer { background: #2d3436; color: white; padding: 30px; text-align: center; margin-top: 50px; border-radius: 0 0 15px 15px; }
.footer a { color: #667eea; text-decoration: none; font-weight: 600; }
.footer a:hover { color: #764ba2; text-decoration: underline; }
</style>
</head>
<body>
<div class="container">
<div class="header-banner">
<h1>ML Loss Function Playground</h1>
<p>Interactive Tool to Compare Loss Functions</p>
</div>
<div class="tab-navigation">
<button class="tab-button active" onclick="switchTab('playground')">Playground</button>
<button class="tab-button" onclick="switchTab('documentation')">Documentation & Formulas</button>
</div>
<!-- PLAYGROUND TAB -->
<div id="playground-tab" class="tab-content active">
<h2 class="section-title">Configure Experiment</h2>
<div class="controls">
<div class="control-group">
<label>Task Type:</label>
<select id="task_type" onchange="updateLossOptions()">
<option value="regression">Regression</option>
<option value="binary_classification">Binary Classification</option>
<option value="multi_classification">Multi-class Classification</option>
</select>
</div>
<div class="control-group" id="n_classes_group" style="display:none;">
<label>Number of Classes:</label>
<input type="number" id="n_classes" value="3" min="3" max="10">
</div>
<div class="control-group">
<label>Samples:</label>
<input type="number" id="n_samples" value="1000" min="100" max="10000">
</div>
<div class="control-group">
<label>Features:</label>
<input type="number" id="n_features" value="10" min="2" max="50">
</div>
<div class="control-group">
<label>Noise Level (%):</label>
<input type="number" id="noise_level" value="10" min="0" max="100" step="0.1">
</div>
<div class="control-group">
<label>Outlier %:</label>
<input type="number" id="outlier_percentage" value="1" min="0" max="20" step="0.1">
</div>
<div class="control-group">
<label>Outlier Magnitude:</label>
<input type="number" id="outlier_magnitude" value="10" min="1" max="100">
</div>
<div class="control-group">
<label>Hidden Layers:</label>
<input type="number" id="n_hidden_layers" value="2" min="1" max="5">
</div>
<div class="control-group">
<label>Neurons/Layer:</label>
<input type="number" id="neurons_per_layer" value="32" min="8" max="128">
</div>
<div class="control-group">
<label>Epochs:</label>
<input type="number" id="epochs" value="100" min="10" max="500">
</div>
</div>
<div class="control-group">
<label>Loss Functions:</label>
<div class="checkbox-group" id="loss_functions">
<label><input type="checkbox" value="mse"> MSE</label>
<label><input type="checkbox" value="mae"> MAE</label>
<label><input type="checkbox" value="cross_entropy" checked> Cross Entropy</label>
<label><input type="checkbox" value="sparse_categorical_crossentropy"> Sparse Cat CE</label>
<label><input type="checkbox" value="bce"> BCE</label>
<label><input type="checkbox" value="huber"> Huber</label>
</div>
</div>
<button onclick="trainModels()" id="trainBtn">Train Models</button>
<div id="loading"><h3>Training... please wait</h3></div>
<div id="results"></div>
</div>
<!-- DOCUMENTATION TAB -->
<div id="documentation-tab" class="tab-content">
<h2 class="section-title">Loss Function Formulas</h2>
<div class="loss-function-card">
<h5>Mean Squared Error (MSE)</h5>
<div class="formula">
$$ L_{\\text{MSE}} = \\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2 $$
</div>
<p>Best for regression. Sensitive to outliers.</p>
</div>
<div class="loss-function-card">
<h5>Mean Absolute Error (MAE)</h5>
<div class="formula">
$$ L_{\\text{MAE}} = \\frac{1}{n} \\sum_{i=1}^{n} |y_i - \\hat{y}_i| $$
</div>
<p>Robust to outliers. Constant gradient.</p>
</div>
<div class="loss-function-card">
<h5>Cross-Entropy Loss</h5>
<div class="formula">
$$ L_{\\text{CE}} = -\\sum_{c=1}^{C} y_c \\log(\\hat{p}_c) $$
</div>
<p>For multi-class classification with softmax.</p>
</div>
<div class="loss-function-card">
<h5>Sparse Categorical Cross-Entropy</h5>
<div class="formula">
$$ L = -\\log(\\hat{p}_{y}) $$
</div>
<p>Same as CE, but label is integer index.</p>
</div>
<div class="loss-function-card">
<h5>Binary Cross-Entropy (BCE)</h5>
<div class="formula">
$$ L = -[y \\log(\\sigma(z)) + (1-y) \\log(1-\\sigma(z))] $$
</div>
<p>For binary classification with sigmoid.</p>
</div>
<div class="loss-function-card">
<h5>Huber Loss</h5>
<div class="formula">
$$ L_{\\delta} = \\begin{cases}
\\frac{1}{2}(y - \\hat{y})^2 & |y - \\hat{y}| \\leq \\delta \\\\
\\delta(|y - \\hat{y}| - \\frac{1}{2}\\delta) & \\text{otherwise}
\\end{cases} $$
</div>
<p>Combines MSE and MAE. Robust + smooth.</p>
</div>
<!-- What You'll Learn -->
<div class="info-section" style="margin-top: 40px; background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); color: white; padding: 25px; border-radius: 12px;">
<h3>What You'll Learn</h3>
<ul style="list-style-type: disc; padding-left: 20px; margin-top: 10px;">
<li>Compare loss functions on the same dataset</li>
<li>See convergence speed and stability</li>
<li>Experiment with noise and outliers</li>
<li>Try mismatched loss functions (educational!)</li>
</ul>
</div>
</div>
<!-- Footer -->
<div class="footer">
<p>
<strong>© 2025 Mohammad Noorchenarboo</strong> |
<a href="https://www.linkedin.com/in/mnoorchenar" target="_blank">LinkedIn Profile</a>
</p>
<p style="margin-top: 10px; font-size: 0.85em;">
⚖️ <strong>Copyright Notice:</strong> All rights reserved. Unauthorized copying, reproduction, or distribution of this application is strictly prohibited.
</p>
<p style="margin-top: 8px; font-size: 0.8em; opacity: 0.8;">
This application is provided for educational and research purposes only.
</p>
</div>
</div>
<script>
function switchTab(tabName) {
document.querySelectorAll('.tab-content').forEach(tab => tab.classList.remove('active'));
document.querySelectorAll('.tab-button').forEach(btn => btn.classList.remove('active'));
document.getElementById(tabName + '-tab').classList.add('active');
event.target.classList.add('active');
if (tabName === 'documentation' && window.MathJax) {
MathJax.typesetPromise();
}
}
function updateLossOptions() {
const taskType = document.getElementById('task_type').value;
document.getElementById('n_classes_group').style.display = (taskType === 'multi_classification') ? 'block' : 'none';
const checkboxes = document.querySelectorAll('#loss_functions input');
checkboxes.forEach(cb => cb.checked = false);
if (taskType === 'regression') {
document.querySelector('input[value="mse"]').checked = true;
} else {
document.querySelector('input[value="cross_entropy"]').checked = true;
if (taskType === 'binary_classification') {
document.querySelector('input[value="bce"]').checked = true;
}
}
}
async function trainModels() {
const config = {
task_type: document.getElementById('task_type').value,
n_samples: parseInt(document.getElementById('n_samples').value),
n_features: parseInt(document.getElementById('n_features').value),
noise_level: parseFloat(document.getElementById('noise_level').value),
outlier_percentage: parseFloat(document.getElementById('outlier_percentage').value),
outlier_magnitude: parseFloat(document.getElementById('outlier_magnitude').value),
n_hidden_layers: parseInt(document.getElementById('n_hidden_layers').value),
neurons_per_layer: parseInt(document.getElementById('neurons_per_layer').value),
epochs: parseInt(document.getElementById('epochs').value),
loss_functions: Array.from(document.querySelectorAll('#loss_functions input:checked')).map(cb => cb.value)
};
if (config.task_type === 'multi_classification') {
config.n_classes = parseInt(document.getElementById('n_classes').value);
}
if (config.loss_functions.length === 0) {
alert('Please select at least one loss function');
return;
}
document.getElementById('trainBtn').disabled = true;
document.getElementById('loading').style.display = 'block';
document.getElementById('results').innerHTML = '';
try {
const response = await fetch('/train', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify(config)
});
const data = await response.json();
if (data.error) throw new Error(data.error);
displayResults(data, config.epochs, config.task_type);
} catch (error) {
document.getElementById('results').innerHTML = `<div class="error-message">Error: ${error.message}</div>`;
} finally {
document.getElementById('trainBtn').disabled = false;
document.getElementById('loading').style.display = 'none';
}
}
function displayResults(data, epochs, taskType) {
let html = '<h2 class="section-title">Results</h2>';
Object.keys(data).forEach(lossName => {
html += `
<div class="plot-container">
<h3>${lossName.toUpperCase()} Loss</h3>
<div id="plot_${lossName}"></div>
</div>
`;
});
html += `
<div class="plot-container">
<h3>Total Training Time</h3>
<div id="timePlot"></div>
</div>
<h2 class="section-title">Performance Metrics</h2>
<table class="summary-table" id="summaryTable"></table>
`;
document.getElementById('results').innerHTML = html;
Object.keys(data).forEach(lossName => {
const d = data[lossName];
const x = Array.from({length: epochs}, (_, i) => i + 1);
Plotly.newPlot(`plot_${lossName}`, [
{ x, y: d.train_losses, name: 'Train Loss', line: {color: '#667eea', width: 2} },
{ x, y: d.val_losses, name: 'Val Loss', line: {color: '#f5576c', width: 2, dash: 'dot'} }
], {
title: `${lossName.toUpperCase()} Train and Val Loss`,
xaxis: {title: 'Epoch'},
yaxis: {title: 'Loss', type: 'linear'}, // Changed to linear for better visibility of small values
height: 400
});
});
const lossNames = Object.keys(data);
const times = lossNames.map(name => data[name].total_time);
Plotly.newPlot('timePlot', [{
x: lossNames,
y: times,
type: 'bar',
marker: {color: '#667eea'}
}], {
title: 'Total Training Time per Loss Function',
xaxis: {title: 'Loss Function'},
yaxis: {title: 'Time (seconds)'},
height: 400
});
let tableHTML = '<tr><th>Loss Function</th><th>Convergence Epoch</th><th>Total Time (s)</th>';
if (taskType === 'regression') {
tableHTML += '<th>MSE</th><th>MAE</th><th>R²</th></tr>';
} else {
tableHTML += '<th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr>';
}
Object.keys(data).forEach(lossName => {
const result = data[lossName];
const metrics = result.metrics;
tableHTML += `<tr>
<td><strong>${lossName}</strong></td>
<td>${result.convergence_epoch}</td>
<td>${result.total_time.toFixed(3)}</td>
`;
if (taskType === 'regression') {
tableHTML += `
<td>${metrics.mse.toFixed(4)}</td>
<td>${metrics.mae.toFixed(4)}</td>
<td>${metrics.r2.toFixed(4)}</td>
`;
} else {
tableHTML += `
<td>${metrics.accuracy.toFixed(4)}</td>
<td>${metrics.precision.toFixed(4)}</td>
<td>${metrics.recall.toFixed(4)}</td>
<td>${metrics.f1.toFixed(4)}</td>
`;
}
tableHTML += '</tr>';
});
document.getElementById('summaryTable').innerHTML = tableHTML;
}
</script>
</body>
</html>'''
class SimpleNN(nn.Module):
def __init__(self, input_dim, output_dim, task_type, loss_name, n_hidden_layers, neurons_per_layer):
super().__init__()
self.task_type = task_type
self.loss_name = loss_name
layers = [nn.Linear(input_dim, neurons_per_layer), nn.ReLU()]
for _ in range(n_hidden_layers - 1):
layers.extend([nn.Linear(neurons_per_layer, neurons_per_layer), nn.ReLU()])
layers.append(nn.Linear(neurons_per_layer, output_dim))
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
def get_loss_function(name):
return {
'mse': nn.MSELoss(),
'mae': nn.L1Loss(),
'cross_entropy': nn.CrossEntropyLoss(),
'sparse_categorical_crossentropy': nn.CrossEntropyLoss(),
'bce': nn.BCEWithLogitsLoss(),
'huber': nn.SmoothL1Loss()
}.get(name, nn.MSELoss())
def evaluate_regression(model, X, y):
model.eval()
with torch.no_grad():
pred = model(X).cpu().numpy()
true = y.cpu().numpy()
return {
'mse': mean_squared_error(true, pred),
'mae': mean_absolute_error(true, pred),
'r2': r2_score(true, pred)
}
def evaluate_classification(model, X, y):
model.eval()
with torch.no_grad():
out = model(X)
if model.loss_name == 'bce' and model.task_type == 'binary_classification':
pred = (torch.sigmoid(out) > 0.5).long()
else:
_, pred = torch.max(out, 1)
true = y.cpu().numpy()
pred = pred.cpu().numpy()
return {
'accuracy': accuracy_score(true, pred),
'precision': precision_score(true, pred, average='macro', zero_division=0),
'recall': recall_score(true, pred, average='macro', zero_division=0),
'f1': f1_score(true, pred, average='macro', zero_division=0)
}
def add_outliers(X, y, n_samples, perc, mag):
n = int(n_samples * perc / 100)
if n == 0: return X, y
idx = np.random.choice(n_samples, n, replace=False)
X[idx] *= mag
if y.ndim == 2: # regression
y[idx] *= mag
else: # classification
if y.max() > 1: # multi-class
y[idx] = np.random.randint(0, y.max() + 1, size=n)
else: # binary
y[idx] = 1 - y[idx]
return X, y
def clean_nans(d):
if isinstance(d, list):
return [0.0 if np.isnan(x) or np.isinf(x) else x for x in d]
if isinstance(d, dict):
return {k: clean_nans(v) for k, v in d.items()}
return d
@app.route('/')
def index():
return render_template_string(HTML_TEMPLATE)
@app.route('/health')
def health():
return jsonify({'status': 'healthy'}), 200
@app.route('/train', methods=['POST'])
def train():
try:
cfg = request.get_json()
if not cfg: return jsonify({'error': 'No data'}), 400
task = cfg['task_type']
n_samples = cfg['n_samples']
n_features = cfg['n_features']
noise = cfg['noise_level']
out_p = cfg['outlier_percentage']
out_m = cfg['outlier_magnitude']
layers = cfg['n_hidden_layers']
neurons = cfg['neurons_per_layer']
epochs = cfg['epochs']
losses = cfg['loss_functions']
n_classes = cfg.get('n_classes')
# Generate data
if task == 'regression':
X, y = make_regression(n_samples=n_samples, n_features=n_features, noise=noise, random_state=42)
y = y.reshape(-1, 1)
output_dim = 1
else:
flip = min(1.0, noise / 100)
n_cls = 2 if task == 'binary_classification' else n_classes
X, y = make_classification(n_samples=n_samples, n_features=n_features, n_classes=n_cls,
n_informative=max(2, n_features//2), n_clusters_per_class=1,
flip_y=flip, random_state=42)
output_dim = n_cls
X, y = add_outliers(X, y, n_samples, out_p, out_m)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_val = scaler.transform(X_val)
X_train_t = torch.FloatTensor(X_train)
X_val_t = torch.FloatTensor(X_val)
if task == 'regression':
y_train_t = torch.FloatTensor(y_train)
y_val_t = torch.FloatTensor(y_val)
else:
y_train_t = torch.LongTensor(y_train)
y_val_t = torch.LongTensor(y_val)
results = {}
for loss_name in losses:
out_dim = 1 if task == 'binary_classification' and loss_name == 'bce' else output_dim
model = SimpleNN(n_features, out_dim, task, loss_name, layers, neurons)
criterion = get_loss_function(loss_name)
opt = optim.Adam(model.parameters(), lr=0.001)
train_losses = []
val_losses = []
start = time.time()
convergence_epoch = None
for epoch in range(epochs):
model.train()
opt.zero_grad()
outputs = model(X_train_t)
loss = None
try:
if loss_name in ['cross_entropy', 'sparse_categorical_crossentropy'] and task != 'regression':
loss = criterion(outputs, y_train_t)
elif loss_name == 'bce' and task == 'binary_classification':
loss = criterion(outputs, y_train_t.float().unsqueeze(1))
else:
# Mismatched cases
if task == 'regression':
if loss_name in ['cross_entropy', 'sparse_categorical_crossentropy']:
# For mismatched CE on regression, treat as binary for demo
logits = torch.cat([outputs, -outputs], dim=1) # Fake 2 classes
targets = (y_train_t > 0).long().squeeze(1)
loss = criterion(logits, targets)
elif loss_name == 'bce':
loss = criterion(outputs, (y_train_t > 0).float())
else:
loss = criterion(outputs, y_train_t)
else:
loss = criterion(outputs.mean(dim=1, keepdim=True), y_train_t.float().unsqueeze(1))
if loss is not None:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
opt.step()
except Exception as e:
logger.warning(f"Loss computation or backward failed for {loss_name} on {task}: {str(e)}")
loss = torch.tensor(float('nan'))
model.eval()
with torch.no_grad():
val_outputs = model(X_val_t)
val_loss = None
try:
if loss_name in ['cross_entropy', 'sparse_categorical_crossentropy'] and task != 'regression':
val_loss = criterion(val_outputs, y_val_t)
elif loss_name == 'bce' and task == 'binary_classification':
val_loss = criterion(val_outputs, y_val_t.float().unsqueeze(1))
else:
if task == 'regression':
if loss_name in ['cross_entropy', 'sparse_categorical_crossentropy']:
logits = torch.cat([val_outputs, -val_outputs], dim=1)
targets = (y_val_t > 0).long().squeeze(1)
val_loss = criterion(logits, targets)
elif loss_name == 'bce':
val_loss = criterion(val_outputs, (y_val_t > 0).float())
else:
val_loss = criterion(val_outputs, y_val_t)
else:
val_loss = criterion(val_outputs.mean(dim=1, keepdim=True), y_val_t.float().unsqueeze(1))
except Exception as e:
logger.warning(f"Val loss failed for {loss_name} on {task}: {str(e)}")
val_loss = torch.tensor(float('nan'))
train_losses.append(float(loss.item()) if loss is not None and not torch.isnan(loss) else float('nan'))
val_losses.append(float(val_loss.item()) if val_loss is not None and not torch.isnan(val_loss) else float('nan'))
if epoch > 10 and convergence_epoch is None:
recent_val = val_losses[-5:]
if len(recent_val) == 5 and all(not np.isnan(r) for r in recent_val):
if abs(recent_val[-1] - recent_val[0]) / (abs(recent_val[0]) + 1e-8) < 0.001:
convergence_epoch = epoch + 1
total_time = time.time() - start
if task == 'regression':
metrics = evaluate_regression(model, X_val_t, y_val_t)
else:
metrics = evaluate_classification(model, X_val_t, y_val_t)
results[loss_name] = {
'train_losses': train_losses,
'val_losses': val_losses,
'total_time': total_time,
'convergence_epoch': convergence_epoch or epochs,
'metrics': metrics
}
return jsonify(clean_nans(results))
except Exception as e:
logger.error(f"Server error in train: {str(e)}")
return jsonify({'error': str(e)}), 500
if __name__ == '__main__':
port = int(os.environ.get('PORT', 7860))
app.run(host='0.0.0.0', port=port, debug=False, use_reloader=False, threaded=True) |