app.py

import os
import numpy as np
import requests
from flask import Flask, request, jsonify, send_file
from werkzeug.utils import secure_filename
import onnxruntime
from PIL import Image
import io
import base64
import logging

# إعداد التسجيل
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB max file size

# تحديث المسارات للعمل في Codespaces
# MODEL_URL = "https://huggingface.co/skytnt/anime-seg/resolve/main/isnetis.onnx"
MODEL_URL = "https://huggingface.co/briaai/RMBG-1.4/resolve/main/onnx/model_fp16.onnx?download=true"
MODEL_PATH = "isnetis.onnx"
UPLOAD_FOLDER = "uploads"
RESULT_FOLDER = "results"

# إنشاء المجلدات
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(RESULT_FOLDER, exist_ok=True)

class BackgroundRemover:
    def __init__(self):
        self.session = None
        self.input_name = None
        self.output_name = None
        
    def download_model(self):
        if os.path.exists(MODEL_PATH):
            logger.info("✅ Model already exists, skipping download")
            return True
            
        try:
            logger.info("📥 Downloading model from HuggingFace...")
            response = requests.get(MODEL_URL, stream=True)
            response.raise_for_status()
            
            total_size = int(response.headers.get('content-length', 0))
            downloaded = 0
            
            with open(MODEL_PATH, 'wb') as f:
                for chunk in response.iter_content(chunk_size=8192):
                    if chunk:
                        f.write(chunk)
                        downloaded += len(chunk)
                        if total_size > 0:
                            progress = (downloaded / total_size) * 100
                            print(f"\r📥 Downloading: {progress:.1f}%", end="", flush=True)
            
            print()  # سطر جديد
            logger.info("✅ Model downloaded successfully")
            return True
        except Exception as e:
            logger.error(f"❌ Failed to download model: {e}")
            return False
    
    def load_model(self):
        try:
            logger.info("🔄 Loading ONNX model...")
            providers = ['CPUExecutionProvider']
            
            # التحقق من دعم GPU (اختياري)
            try:
                available_providers = onnxruntime.get_available_providers()
                if 'CUDAExecutionProvider' in available_providers:
                    providers.insert(0, 'CUDAExecutionProvider')
                    logger.info("🚀 CUDA provider available")
                else:
                    logger.info("💻 Using CPU provider")
            except:
                logger.info("💻 Using CPU provider")
            
            self.session = onnxruntime.InferenceSession(MODEL_PATH, providers=providers)
            self.input_name = self.session.get_inputs()[0].name
            self.output_name = self.session.get_outputs()[0].name
            
            logger.info(f"✅ Model loaded successfully")
            logger.info(f"📊 Input: {self.input_name}, Output: {self.output_name}")
            return True
        except Exception as e:
            logger.error(f"❌ Failed to load model: {e}")
            return False
    
    def preprocess_image(self, image):
        original_size = image.size
        image = image.convert('RGB')
        image = image.resize((1024, 1024), Image.LANCZOS)
        image_array = np.array(image).astype(np.float32)
        image_array = image_array / 255.0
        image_array = np.transpose(image_array, (2, 0, 1))
        image_array = np.expand_dims(image_array, axis=0)
        return image_array, original_size
    
    def postprocess_mask(self, mask, original_size):
        mask = mask.squeeze()
        mask = (mask * 255).astype(np.uint8)
        mask = Image.fromarray(mask, mode='L')
        mask = mask.resize(original_size, Image.LANCZOS)
        return mask
    
    def remove_background(self, image_path):
        try:
            logger.info(f"🖼️ Processing image: {image_path}")
            image = Image.open(image_path)
            preprocessed, original_size = self.preprocess_image(image)
            
            logger.info("🤖 Running AI inference...")
            mask = self.session.run([self.output_name], {self.input_name: preprocessed})[0]
            mask = self.postprocess_mask(mask, original_size)
            
            logger.info("✂️ Applying mask to remove background...")
            image = image.convert('RGBA')
            image.putalpha(mask)
            
            logger.info("✅ Background removed successfully")
            return image
        except Exception as e:
            logger.error(f"❌ Failed to remove background: {e}")
            return None

# إنشاء كائن إزالة الخلفية
background_remover = BackgroundRemover()

@app.route('/', methods=['GET'])
def health_check():
    """فحص صحة السيرفر"""
    return jsonify({
        'status': '✅ Server is running',
        'model_loaded': background_remover.session is not None,
        'endpoints': {
            'health_check': '/ (GET)',
            'remove_background': '/remove-background (POST)'
        },
        'info': 'Background Remover API - Ready to use!'
    })

@app.route('/remove-background', methods=['POST'])
def remove_background_endpoint():
    """إزالة خلفية الصورة"""
    try:
        # التحقق من وجود الملف
        if 'file' not in request.files:
            return jsonify({'error': 'No file uploaded'}), 400
        
        file = request.files['file']
        if file.filename == '':
            return jsonify({'error': 'No file selected'}), 400
        
        # التحقق من نوع الملف
        if not file.content_type.startswith('image/'):
            return jsonify({'error': 'File must be an image'}), 400
        
        # حفظ الملف
        filename = secure_filename(file.filename)
        filepath = os.path.join(UPLOAD_FOLDER, filename)
        file.save(filepath)
        
        logger.info(f"📂 File saved: {filepath}")
        
        # معالجة الصورة
        result = background_remover.remove_background(filepath)
        
        if result is None:
            os.remove(filepath)  # حذف الملف في حالة الفشل
            return jsonify({'error': 'Failed to process image'}), 500
        
        # حفظ النتيجة
        result_filename = f"result_{filename.rsplit('.', 1)[0]}.png"
        result_path = os.path.join(RESULT_FOLDER, result_filename)
        result.save(result_path, 'PNG')
        
        # حذف الملف الأصلي
        os.remove(filepath)
        
        logger.info(f"✅ Result saved: {result_path}")
        
        return send_file(result_path, mimetype='image/png', as_attachment=False)
        
    except Exception as e:
        logger.error(f"❌ Error in remove_background_endpoint: {e}")
        return jsonify({'error': 'Internal server error'}), 500

if __name__ == '__main__':
    print("=" * 60)
    print("🎯 Background Remover API Server")
    print("=" * 60)
    
    logger.info("🚀 Starting Background Remover Server...")
    
    # تحميل وتشغيل النموذج
    if not background_remover.download_model():
        logger.error("❌ Failed to download model. Exiting...")
        exit(1)
    
    if not background_remover.load_model():
        logger.error("❌ Failed to load model. Exiting...")
        exit(1)
    
    # إعداد المنفذ
if __name__ == '__main__':
    if not background_remover.download_model():
        exit(1)
    if not background_remover.load_model():
        exit(1)
    
    # Hugging Face Spaces يستخدم المنفذ 7860
    port = int(os.environ.get('PORT', 7860))
    print(f'📡 Running on port: {port}')
    print(f'🌐 Local URL: http://localhost:{port}')
    print(f'🔍 Health check: http://localhost:{port}/')
    print(f'📱 API endpoint: http://localhost:{port}/remove-background')
    print('=' * 60)
    
    app.run(host='0.0.0.0', port=port, debug=False)