Update app.py

app.py

@@ -1,21 +1,20 @@
-from flask import Flask, request, send_file, jsonify
 import os
 import sys
 import io
 import logging
-import torch
 from datetime import datetime
+from flask import Flask, request, send_file, jsonify
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
 from database import init_db, close_db, get_db_path
 
-# Import yollarını kontrol et
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
 # Environment setup
 os.environ['NUMBA_DISABLE_JIT'] = '1'
 os.environ['TORCH_HOME'] = '/tmp/torch_cache'
 os.environ['U2NET_HOME'] = '/tmp/.u2net'
 
-# Create cache directories if they don't exist
+# Create cache directories
 os.makedirs('/tmp/torch_cache', exist_ok=True)
 os.makedirs('/tmp/.u2net', exist_ok=True)
 
@@ -23,99 +22,98 @@ os.makedirs('/tmp/.u2net', exist_ok=True)
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Model Loading (Çalışan örnekten eklenen kısım)
+# Initialize Flask
+app = Flask(__name__)
+init_db(app)
+
+# --- AnimeGAN Model Initialization ---
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
 try:
-    logger.info("Loading model...")
-    model = torch.hub.load('bryandlee/animegan2-pytorch:main', 'generator', trust_repo=True).to(device)
-    model.load_state_dict(torch.load('face_paint_512_v2.pt', map_location=device))
+    logger.info("Loading AnimeGAN model...")
+    model = torch.hub.load(
+        'bryandlee/animegan2-pytorch:main',
+        'generator',
+        pretrained='face_paint_512_v2',  # Critical parameter
+        trust_repo=True
+    ).to(device)
     model.eval()
     logger.info("Model loaded successfully")
 except Exception as e:
-    logger.error(f"Error loading model: {str(e)}")
-    raise
-
-# Import methods
-try:
-    from get_methods import get_users, get_user
-    from post_methods import add_user
-    from consume_method import consume_user
-    from rembg_method import remove_background
-    from halftone_method import apply_halftone
-except ImportError as e:
-    logger.error(f"Import hatası: {e}")
+    logger.error(f"Model loading failed: {str(e)}")
     raise
 
-app = Flask(__name__)
-init_db(app)
+# --- Image Processing Functions ---
+def process_image(image):
+    transform = transforms.Compose([
+        transforms.Resize((512, 512)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+    ])
+    with torch.no_grad():
+        output = model(transform(image).unsqueeze(0).to(device))
+    return transforms.ToPILImage()((output * 0.5 + 0.5).squeeze().cpu())
 
+# --- Flask Routes ---
 @app.route('/')
 def home():
-    return "StableDiffusionAPI Türkçe"
-
-# Existing routes
-app.route('/users', methods=['GET'])(get_users)
-app.route('/user/<string:udid>', methods=['GET'])(get_user)
-app.route('/add-user', methods=['POST'])(add_user)
-app.route('/consume/<string:udid>', methods=['POST'])(consume_user)
+    return "AnimeGAN API - Türkçe"
 
-# New image processing routes (Çalışan örnekten güncellenen kısım)
 @app.route('/generate', methods=['POST'])
 def generate():
     start_time = datetime.now()
-    logger.info(f"/generate endpoint called - {start_time}")
+    logger.info(f"Generate request started at {start_time}")
     
     try:
         if 'image' not in request.files:
-            logger.error("No file provided")
             return jsonify({'error': 'No image provided'}), 400
             
         file = request.files['image']
-        if file.filename == '':
-            logger.error("Empty file provided")
-            return jsonify({'error': 'No selected file'}), 400
-            
+        if not file.filename:
+            return jsonify({'error': 'Empty filename'}), 400
+
         try:
-            logger.info(f"File received: {file.filename}")
             image = Image.open(io.BytesIO(file.read())).convert("RGB")
-            
-            logger.info("Processing image...")
-            transform = transforms.Compose([
-                transforms.Resize((512, 512)),
-                transforms.ToTensor(),
-                transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-            ])
-            with torch.no_grad():
-                output = model(transform(image).unsqueeze(0).to(device))
-            processed_img = transforms.ToPILImage()((output * 0.5 + 0.5).squeeze().cpu())
+            processed_img = process_image(image)
             
             img_io = io.BytesIO()
             processed_img.save(img_io, 'PNG')
             img_io.seek(0)
             
             duration = (datetime.now() - start_time).total_seconds()
-            logger.info(f"Successfully processed. Duration: {duration} seconds")
+            logger.info(f"Processed in {duration:.2f} seconds")
             return send_file(img_io, mimetype='image/png')
             
         except Exception as e:
-            logger.error(f"Processing error: {str(e)}", exc_info=True)
+            logger.error(f"Processing error: {str(e)}")
             return jsonify({'error': str(e)}), 500
             
     except Exception as e:
-        logger.error(f"Unexpected error: {str(e)}", exc_info=True)
+        logger.error(f"Unexpected error: {str(e)}")
         return jsonify({'error': 'Internal server error'}), 500
 
-# Diğer endpoint'ler (remove-bg ve halftone) aynı kalıyor...
-@app.route('/remove-bg', methods=['POST'])
-def remove_bg():
-    # ... (mevcut kodunuz aynen kalacak)
-    pass
+# --- Database Routes (Keep your existing routes) ---
+@app.route('/users', methods=['GET'])
+def get_users():
+    from get_methods import get_users
+    return get_users()
+
+@app.route('/user/<string:udid>', methods=['GET'])
+def get_user(udid):
+    from get_methods import get_user
+    return get_user(udid)
 
-@app.route('/halftone', methods=['POST'])
-def halftone():
-    # ... (mevcut kodunuz aynen kalacak)
-    pass
+@app.route('/add-user', methods=['POST'])
+def add_user():
+    from post_methods import add_user
+    return add_user()
+
+@app.route('/consume/<string:udid>', methods=['POST'])
+def consume_user(udid):
+    from consume_method import consume_user
+    return consume_user(udid)
 
+# --- Teardown ---
 @app.teardown_appcontext
 def shutdown_session(exception=None):
     close_db()