notnotrishi · January 6, 2026 01:31
diff --git a/app.py b/app.py
 """
 Flask application for converting images to 3D using Apple's SHARP model.

 Installation:
 1. Clone ml-sharp repository:
   git clone https://github.com/apple/ml-sharp
   cd ml-sharp
   pip install -r requirements.txt

 2. Install Flask and Flask-CORS:
   pip install flask flask-cors

 3. Place this script in the ml-sharp directory

 4. Run: python app.py

 5. Navigate to http://localhost:5050
 """

 from flask import Flask, render_template_string, request, jsonify, send_file, send_from_directory
 from flask_cors import CORS
 from pathlib import Path
 import torch
 import numpy as np
 from PIL import Image
 import io
 import logging

 app = Flask(__name__)
 CORS(app)

 @app.after_request
 def add_headers(response):
    response.headers['Cross-Origin-Opener-Policy'] = 'same-origin'
    response.headers['Cross-Origin-Embedder-Policy'] = 'require-corp'
    return response

 from sharp.models import create_predictor
 from sharp.models.params import PredictorParams
 from sharp.utils.gaussians import save_ply
 from sharp.cli.predict import predict_image

 logging.basicConfig(level=logging.INFO)
 LOGGER = logging.getLogger(__name__)

 predictor = None
 device = None

 def load_model():
    global predictor, device
    
    if torch.cuda.is_available():
        device = torch.device("cuda")
    elif torch.mps.is_available():
        device = torch.device("mps")
    else:
        device = torch.device("cpu")
    
    LOGGER.info(f"Using device: {device}")
    LOGGER.info("Loading SHARP model...")
    
    DEFAULT_MODEL_URL = "https://ml-site.cdn-apple.com/models/sharp/sharp_2572gikvuh.pt"
    state_dict = torch.hub.load_state_dict_from_url(DEFAULT_MODEL_URL, progress=True)
    
    predictor = create_predictor(PredictorParams())
    predictor.load_state_dict(state_dict)
    predictor.eval()
    predictor.to(device)
    
    LOGGER.info("Model loaded")

 VIEWER_TEMPLATE = """
 <!DOCTYPE html>
 <html>
 <head>
    <title>3D Viewer</title>
    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <style>
        body { margin:0; overflow:hidden; font-family: monospace; background:#000; }
        #info { position:absolute; top:15px; left:15px; color:white; background:rgba(0,0,0,0.7); padding:15px; border-radius:8px; z-index:1000; font-size:14px; max-width:300px }
        #loading { position:absolute; top:50%; left:50%; transform:translate(-50%,-50%); color:white; font-size:18px; text-align:center }
        .spinner{ border:4px solid rgba(255,255,255,0.3); border-top:4px solid white; border-radius:50%; width:50px; height:50px; animation:spin 1s linear infinite; margin:0 auto 15px }
        @keyframes spin{0%{transform:rotate(0deg)}100%{transform:rotate(360deg)}}
    </style>
 </head>
 <body>
    <div id="info">
        <div><strong>GaussianSplats3D Viewer</strong></div>
        <div id="status">Loading...</div>
        <div style="margin-top:10px; font-size:12px; color:#aaa;">Mouse: Drag to orbit, scroll to zoom<br>File: {{ filename }}</div>
    </div>
    <div id="loading"><div class="spinner"></div><div>Loading 3D scene...</div></div>

    <script type="importmap">
    {
        "imports": {
            "three": "https://cdn.jsdelivr.net/npm/three@0.160.0/build/three.module.js",
            "@mkkellogg/gaussian-splats-3d": "https://cdn.jsdelivr.net/npm/@mkkellogg/gaussian-splats-3d@0.4.7/build/gaussian-splats-3d.module.js"
        }
    }
    </script>

    <script type="module">
        import * as GaussianSplats3D from '@mkkellogg/gaussian-splats-3d';

        const statusEl = document.getElementById('status');
        const loadingEl = document.getElementById('loading');
        statusEl.textContent = 'Initializing viewer...';

        const viewer = new GaussianSplats3D.Viewer({
            cameraUp: [0, -1, -0.6],
            initialCameraPosition: [0, -2, -5],
            initialCameraLookAt: [0, 0, 0],
            sphericalHarmonicsDegree: 0,
            enableSplatSort: true,
            freeIntermediateSplatData: true
        });

        viewer.addSplatScene('{{ url_for('static', filename=filename) }}', {
            progressiveLoad: true,
            showLoadingUI: false,
            position: [0,0,0], rotation: [0,0,0,1], scale: [1,1,1]
        })
        .then(()=>{
            statusEl.textContent = 'Loaded successfully!';
            loadingEl.style.display='none';
            viewer.start();
            setTimeout(()=>{document.getElementById('info').style.opacity='0.7'},3000);
        })
        .catch((err)=>{
            statusEl.textContent = 'Error: '+err.message; statusEl.style.color='#ff6b6b'; console.error('Error loading splat:', err);
        });
    </script>
 </body>
 </html>
 """
 HTML_TEMPLATE = """
 <!DOCTYPE html>
 <html>
 <head>
    <title>Image to 3D</title>
    <style>
        * { margin: 0; padding: 0; box-sizing: border-box; }
        body {
            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            min-height: 100vh;
            display: flex;
            align-items: center;
            justify-content: center;
            padding: 20px;
        }
        .container {
            background: white;
            border-radius: 20px;
            box-shadow: 0 20px 60px rgba(0,0,0,0.3);
            max-width: 900px;
            width: 100%;
            padding: 40px;
        }
        h1 { font-size: 32px; color: #333; margin-bottom: 10px; }
        .subtitle { color: #666; font-size: 14px; margin-bottom: 30px; }
        .note { background: #fff3cd; border-left: 4px solid #ffc107; padding: 12px; margin-bottom: 20px; font-size: 13px; color: #856404; }
        .drop-zone { border: 3px dashed #667eea; border-radius: 12px; padding: 60px 20px; text-align: center; cursor: pointer; transition: all 0.3s; background: #f8f9ff; margin-bottom: 20px; }
        .drop-zone:hover, .drop-zone.dragover { border-color: #764ba2; background: #f0f0ff; transform: scale(1.02); }
        .drop-zone-text { color: #667eea; font-size: 18px; font-weight: 600; margin-bottom: 8px; }
        .drop-zone-subtext { color: #999; font-size: 14px; }
        #preview { max-width: 100%; max-height: 400px; border-radius: 8px; display: none; margin: 20px auto; }
        .status { padding: 15px; border-radius: 8px; display: none; margin-top: 15px; font-size: 14px; }
        .status.info { background: #e3f2fd; color: #1565c0; display: block; }
        .status.success { background: #e8f5e9; color: #2e7d32; display: block; }
        .status.error { background: #ffebee; color: #c62828; display: block; }
        .result-box { display: none; margin-top: 30px; padding: 20px; background: #f8f9ff; border-radius: 12px; }
        .result-box.show { display: block; }
        .result-title { font-size: 18px; font-weight: 600; color: #333; margin-bottom: 15px; }
        .btn-group { display: flex; gap: 10px; flex-wrap: wrap; }
        .btn { background: #667eea; border: none; padding: 12px 24px; border-radius: 8px; cursor: pointer; font-size: 14px; font-weight: 600; color: white; transition: all 0.2s; text-decoration: none; display: inline-block; }
        .btn:hover { background: #764ba2; transform: translateY(-2px); box-shadow: 0 4px 12px rgba(0,0,0,0.2); }
        .btn-secondary { background: white; color: #667eea; border: 2px solid #667eea; }
        .btn-secondary:hover { background: #667eea; color: white; }
    </style>
 </head>
 <body>
    <div class="container">
        <div>
            <h1>🎨 Image to 3D scene</h1>
            <p class="subtitle">Convert any image to a 3D scene using Apple's SHARP</p>
        </div>
        <div class="note">⚠️ <strong>Note:</strong> SHARP generates basic RGB-only splats for speed. The output quality is limited compared to full training approaches, but processes quickly.</div>
        <div class="drop-zone" id="dropZone">
            <div class="drop-zone-text">📸 Drop an image here</div>
            <div class="drop-zone-subtext">or click to browse (JPG, PNG)</div>
            <input type="file" id="fileInput" accept="image/*" style="display: none;">
        </div>
        <img id="preview" alt="Preview">
        <div id="status" class="status"></div>
        <div id="resultBox" class="result-box">
            <div class="result-title">✨ 3D Model Ready!</div>
            <div class="btn-group">
                <a class="btn" id="downloadBtn" download>⬇️ Download .ply</a>
                <a class="btn btn-secondary" id="viewBtn" target="_blank">👁️ View in 3D</a>
            </div>
        </div>
    </div>

    <script>
        const dropZone = document.getElementById('dropZone');
        const fileInput = document.getElementById('fileInput');
        const preview = document.getElementById('preview');
        const status = document.getElementById('status');
        const resultBox = document.getElementById('resultBox');
        const downloadBtn = document.getElementById('downloadBtn');
        const viewBtn = document.getElementById('viewBtn');

        dropZone.addEventListener('click', () => fileInput.click());
        dropZone.addEventListener('dragover', (e) => { e.preventDefault(); dropZone.classList.add('dragover'); });
        dropZone.addEventListener('dragleave', () => { dropZone.classList.remove('dragover'); });
        dropZone.addEventListener('drop', (e) => { e.preventDefault(); dropZone.classList.remove('dragover'); if (e.dataTransfer.files.length) handleFile(e.dataTransfer.files[0]); });
        fileInput.addEventListener('change', (e) => { if (e.target.files.length) handleFile(e.target.files[0]); });

        function showStatus(message, type) { status.innerHTML = message; status.className = 'status ' + type; if (type !== 'success') resultBox.classList.remove('show'); }

        function handleFile(file) {
            if (!file.type.startsWith('image/')) { showStatus('❌ Please upload an image file', 'error'); return; }
            const reader = new FileReader();
            reader.onload = (e) => { preview.src = e.target.result; preview.style.display = 'block'; uploadImage(file); };
            reader.readAsDataURL(file);
        }

        function uploadImage(file) {
            const formData = new FormData(); formData.append('image', file);
            showStatus('⚙️ Processing image...', 'info');
            fetch('/process', { method: 'POST', body: formData })
            .then(response => response.json())
            .then(data => {
                if (data.success) {
                    showStatus('✅ 3D view generated!', 'success');
                    downloadBtn.href = '/static/' + data.filename; downloadBtn.download = data.filename; viewBtn.href = '/viewer?file=' + encodeURIComponent(data.filename); resultBox.classList.add('show');
                } else showStatus('❌ Error: ' + data.error, 'error');
            })
            .catch(error => { showStatus('❌ Error: ' + error.message, 'error'); });
        }
    </script>
 </body>
 </html>
 """

 @app.route('/')
 def index():
    return render_template_string(HTML_TEMPLATE)

 @app.route('/viewer')
 def viewer():
    filename = request.args.get('file', '')
    return render_template_string(VIEWER_TEMPLATE, filename=filename)

 @app.route('/process', methods=['POST'])
 def process_image():
    try:
        if 'image' not in request.files:
            return jsonify({'success': False, 'error': 'No image provided'})
        file = request.files['image']
        if file.filename == '':
            return jsonify({'success': False, 'error': 'No image selected'})
        image_data = file.read()
        image = Image.open(io.BytesIO(image_data))
        if image.mode != 'RGB':
            image = image.convert('RGB')
        image_np = np.array(image)
        height, width = image_np.shape[:2]
        f_px = float(max(width, height))
        LOGGER.info(f"Processing: {width}x{height}, f_px={f_px}")
        with torch.no_grad():
            gaussians = predict_image(predictor, image_np, f_px, device)
        output_filename = f"splat_{hash(file.filename) & 0x7FFFFFFF}.ply"
        output_path = Path('static') / output_filename
        output_path.parent.mkdir(exist_ok=True)
        save_ply(gaussians, f_px, (height, width), output_path)
        LOGGER.info(f"Saved to {output_path}")
        return jsonify({'success': True, 'filename': output_filename})
    except Exception as e:
        LOGGER.error(f"Error: {str(e)}", exc_info=True)
        return jsonify({'success': False, 'error': str(e)})


 if __name__ == '__main__':
    LOGGER.info("Starting Flask app...")
    load_model()
    LOGGER.info("Navigate to http://localhost:5050")
    app.run(debug=False, host='0.0.0.0', port=5050)
	"""
	Flask application for converting images to 3D using Apple's SHARP model.

	Installation:
	1. Clone ml-sharp repository:
	git clone https://github.com/apple/ml-sharp
	cd ml-sharp
	pip install -r requirements.txt

	2. Install Flask and Flask-CORS:
	pip install flask flask-cors

	3. Place this script in the ml-sharp directory

	4. Run: python app.py

	5. Navigate to http://localhost:5050
	"""

	from flask import Flask, render_template_string, request, jsonify, send_file, send_from_directory
	from flask_cors import CORS
	from pathlib import Path
	import torch
	import numpy as np
	from PIL import Image
	import io
	import logging

	app = Flask(__name__)
	CORS(app)

	@app.after_request
	def add_headers(response):
	response.headers['Cross-Origin-Opener-Policy'] = 'same-origin'
	response.headers['Cross-Origin-Embedder-Policy'] = 'require-corp'
	return response

	from sharp.models import create_predictor
	from sharp.models.params import PredictorParams
	from sharp.utils.gaussians import save_ply
	from sharp.cli.predict import predict_image

	logging.basicConfig(level=logging.INFO)
	LOGGER = logging.getLogger(__name__)

	predictor = None
	device = None

	def load_model():
	global predictor, device

	if torch.cuda.is_available():
	device = torch.device("cuda")
	elif torch.mps.is_available():
	device = torch.device("mps")
	else:
	device = torch.device("cpu")

	LOGGER.info(f"Using device: {device}")
	LOGGER.info("Loading SHARP model...")

	DEFAULT_MODEL_URL = "https://ml-site.cdn-apple.com/models/sharp/sharp_2572gikvuh.pt"
	state_dict = torch.hub.load_state_dict_from_url(DEFAULT_MODEL_URL, progress=True)

	predictor = create_predictor(PredictorParams())
	predictor.load_state_dict(state_dict)
	predictor.eval()
	predictor.to(device)

	LOGGER.info("Model loaded")

	VIEWER_TEMPLATE = """
	<!DOCTYPE html>
	<html>
	<head>
	<title>3D Viewer</title>
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width, initial-scale=1">
	<style>
	body { margin:0; overflow:hidden; font-family: monospace; background:#000; }
	#info { position:absolute; top:15px; left:15px; color:white; background:rgba(0,0,0,0.7); padding:15px; border-radius:8px; z-index:1000; font-size:14px; max-width:300px }
	#loading { position:absolute; top:50%; left:50%; transform:translate(-50%,-50%); color:white; font-size:18px; text-align:center }
	.spinner{ border:4px solid rgba(255,255,255,0.3); border-top:4px solid white; border-radius:50%; width:50px; height:50px; animation:spin 1s linear infinite; margin:0 auto 15px }
	@keyframes spin{0%{transform:rotate(0deg)}100%{transform:rotate(360deg)}}
	</style>
	</head>
	<body>
	<div id="info">
	<div><strong>GaussianSplats3D Viewer</strong></div>
	<div id="status">Loading...</div>
	<div style="margin-top:10px; font-size:12px; color:#aaa;">Mouse: Drag to orbit, scroll to zoom<br>File: {{ filename }}</div>
	</div>
	<div id="loading"><div class="spinner"></div><div>Loading 3D scene...</div></div>

	<script type="importmap">
	{
	"imports": {
	"three": "https://cdn.jsdelivr.net/npm/three@0.160.0/build/three.module.js",
	"@mkkellogg/gaussian-splats-3d": "https://cdn.jsdelivr.net/npm/@mkkellogg/gaussian-splats-3d@0.4.7/build/gaussian-splats-3d.module.js"
	}
	}
	</script>

	<script type="module">
	import * as GaussianSplats3D from '@mkkellogg/gaussian-splats-3d';

	const statusEl = document.getElementById('status');
	const loadingEl = document.getElementById('loading');
	statusEl.textContent = 'Initializing viewer...';

	const viewer = new GaussianSplats3D.Viewer({
	cameraUp: [0, -1, -0.6],
	initialCameraPosition: [0, -2, -5],
	initialCameraLookAt: [0, 0, 0],
	sphericalHarmonicsDegree: 0,
	enableSplatSort: true,
	freeIntermediateSplatData: true
	});

	viewer.addSplatScene('{{ url_for('static', filename=filename) }}', {
	progressiveLoad: true,
	showLoadingUI: false,
	position: [0,0,0], rotation: [0,0,0,1], scale: [1,1,1]
	})
	.then(()=>{
	statusEl.textContent = 'Loaded successfully!';
	loadingEl.style.display='none';
	viewer.start();
	setTimeout(()=>{document.getElementById('info').style.opacity='0.7'},3000);
	})
	.catch((err)=>{
	statusEl.textContent = 'Error: '+err.message; statusEl.style.color='#ff6b6b'; console.error('Error loading splat:', err);
	});
	</script>
	</body>
	</html>
	"""
	HTML_TEMPLATE = """
	<!DOCTYPE html>
	<html>
	<head>
	<title>Image to 3D</title>
	<style>
	* { margin: 0; padding: 0; box-sizing: border-box; }
	body {
	font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
	background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
	min-height: 100vh;
	display: flex;
	align-items: center;
	justify-content: center;
	padding: 20px;
	}
	.container {
	background: white;
	border-radius: 20px;
	box-shadow: 0 20px 60px rgba(0,0,0,0.3);
	max-width: 900px;
	width: 100%;
	padding: 40px;
	}
	h1 { font-size: 32px; color: #333; margin-bottom: 10px; }
	.subtitle { color: #666; font-size: 14px; margin-bottom: 30px; }
	.note { background: #fff3cd; border-left: 4px solid #ffc107; padding: 12px; margin-bottom: 20px; font-size: 13px; color: #856404; }
	.drop-zone { border: 3px dashed #667eea; border-radius: 12px; padding: 60px 20px; text-align: center; cursor: pointer; transition: all 0.3s; background: #f8f9ff; margin-bottom: 20px; }
	.drop-zone:hover, .drop-zone.dragover { border-color: #764ba2; background: #f0f0ff; transform: scale(1.02); }
	.drop-zone-text { color: #667eea; font-size: 18px; font-weight: 600; margin-bottom: 8px; }
	.drop-zone-subtext { color: #999; font-size: 14px; }
	#preview { max-width: 100%; max-height: 400px; border-radius: 8px; display: none; margin: 20px auto; }
	.status { padding: 15px; border-radius: 8px; display: none; margin-top: 15px; font-size: 14px; }
	.status.info { background: #e3f2fd; color: #1565c0; display: block; }
	.status.success { background: #e8f5e9; color: #2e7d32; display: block; }
	.status.error { background: #ffebee; color: #c62828; display: block; }
	.result-box { display: none; margin-top: 30px; padding: 20px; background: #f8f9ff; border-radius: 12px; }
	.result-box.show { display: block; }
	.result-title { font-size: 18px; font-weight: 600; color: #333; margin-bottom: 15px; }
	.btn-group { display: flex; gap: 10px; flex-wrap: wrap; }
	.btn { background: #667eea; border: none; padding: 12px 24px; border-radius: 8px; cursor: pointer; font-size: 14px; font-weight: 600; color: white; transition: all 0.2s; text-decoration: none; display: inline-block; }
	.btn:hover { background: #764ba2; transform: translateY(-2px); box-shadow: 0 4px 12px rgba(0,0,0,0.2); }
	.btn-secondary { background: white; color: #667eea; border: 2px solid #667eea; }
	.btn-secondary:hover { background: #667eea; color: white; }
	</style>
	</head>
	<body>
	<div class="container">
	<div>
	<h1>🎨 Image to 3D scene</h1>
	<p class="subtitle">Convert any image to a 3D scene using Apple's SHARP</p>
	</div>
	<div class="note">⚠️ <strong>Note:</strong> SHARP generates basic RGB-only splats for speed. The output quality is limited compared to full training approaches, but processes quickly.</div>
	<div class="drop-zone" id="dropZone">
	<div class="drop-zone-text">📸 Drop an image here</div>
	<div class="drop-zone-subtext">or click to browse (JPG, PNG)</div>
	<input type="file" id="fileInput" accept="image/*" style="display: none;">
	</div>
	<img id="preview" alt="Preview">
	<div id="status" class="status"></div>
	<div id="resultBox" class="result-box">
	<div class="result-title">✨ 3D Model Ready!</div>
	<div class="btn-group">
	<a class="btn" id="downloadBtn" download>⬇️ Download .ply</a>
	<a class="btn btn-secondary" id="viewBtn" target="_blank">👁️ View in 3D</a>
	</div>
	</div>
	</div>

	<script>
	const dropZone = document.getElementById('dropZone');
	const fileInput = document.getElementById('fileInput');
	const preview = document.getElementById('preview');
	const status = document.getElementById('status');
	const resultBox = document.getElementById('resultBox');
	const downloadBtn = document.getElementById('downloadBtn');
	const viewBtn = document.getElementById('viewBtn');

	dropZone.addEventListener('click', () => fileInput.click());
	dropZone.addEventListener('dragover', (e) => { e.preventDefault(); dropZone.classList.add('dragover'); });
	dropZone.addEventListener('dragleave', () => { dropZone.classList.remove('dragover'); });
	dropZone.addEventListener('drop', (e) => { e.preventDefault(); dropZone.classList.remove('dragover'); if (e.dataTransfer.files.length) handleFile(e.dataTransfer.files[0]); });
	fileInput.addEventListener('change', (e) => { if (e.target.files.length) handleFile(e.target.files[0]); });

	function showStatus(message, type) { status.innerHTML = message; status.className = 'status ' + type; if (type !== 'success') resultBox.classList.remove('show'); }

	function handleFile(file) {
	if (!file.type.startsWith('image/')) { showStatus('❌ Please upload an image file', 'error'); return; }
	const reader = new FileReader();
	reader.onload = (e) => { preview.src = e.target.result; preview.style.display = 'block'; uploadImage(file); };
	reader.readAsDataURL(file);
	}

	function uploadImage(file) {
	const formData = new FormData(); formData.append('image', file);
	showStatus('⚙️ Processing image...', 'info');
	fetch('/process', { method: 'POST', body: formData })
	.then(response => response.json())
	.then(data => {
	if (data.success) {
	showStatus('✅ 3D view generated!', 'success');
	downloadBtn.href = '/static/' + data.filename; downloadBtn.download = data.filename; viewBtn.href = '/viewer?file=' + encodeURIComponent(data.filename); resultBox.classList.add('show');
	} else showStatus('❌ Error: ' + data.error, 'error');
	})
	.catch(error => { showStatus('❌ Error: ' + error.message, 'error'); });
	}
	</script>
	</body>
	</html>
	"""

	@app.route('/')
	def index():
	return render_template_string(HTML_TEMPLATE)

	@app.route('/viewer')
	def viewer():
	filename = request.args.get('file', '')
	return render_template_string(VIEWER_TEMPLATE, filename=filename)

	@app.route('/process', methods=['POST'])
	def process_image():
	try:
	if 'image' not in request.files:
	return jsonify({'success': False, 'error': 'No image provided'})
	file = request.files['image']
	if file.filename == '':
	return jsonify({'success': False, 'error': 'No image selected'})
	image_data = file.read()
	image = Image.open(io.BytesIO(image_data))
	if image.mode != 'RGB':
	image = image.convert('RGB')
	image_np = np.array(image)
	height, width = image_np.shape[:2]
	f_px = float(max(width, height))
	LOGGER.info(f"Processing: {width}x{height}, f_px={f_px}")
	with torch.no_grad():
	gaussians = predict_image(predictor, image_np, f_px, device)
	output_filename = f"splat_{hash(file.filename) & 0x7FFFFFFF}.ply"
	output_path = Path('static') / output_filename
	output_path.parent.mkdir(exist_ok=True)
	save_ply(gaussians, f_px, (height, width), output_path)
	LOGGER.info(f"Saved to {output_path}")
	return jsonify({'success': True, 'filename': output_filename})
	except Exception as e:
	LOGGER.error(f"Error: {str(e)}", exc_info=True)
	return jsonify({'success': False, 'error': str(e)})


	if __name__ == '__main__':
	LOGGER.info("Starting Flask app...")
	load_model()
	LOGGER.info("Navigate to http://localhost:5050")
	app.run(debug=False, host='0.0.0.0', port=5050)
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