Create app.py

app.py

@@ -0,0 +1,217 @@
+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_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
+    port = int(os.environ.get('PORT', 5000))
+    
+    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("💡 In Codespaces: Go to PORTS tab and make port 5000 public")
+    print("=" * 60)
+    
+    # تشغيل السيرفر
+    app.run(host='0.0.0.0', port=port, debug=False, threaded=True)