chore: remove custom handler – use default pipeline

handler.py

@@ -1,49 +0,0 @@
-import torch
-from torchvision import transforms
-from PIL import Image
-import os
-import json
-
-# لو النموذج PyTorch .pth
-class AIImageSourceHandler:
-    def __init__(self):
-        self.model = None
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.labels = ['Real', 'Midjourney', 'DALL·E', 'StableDiffusion']  # عدّل حسب عدد التصنيفات
-
-    def initialize(self, model_dir: str):
-        # تحميل النموذج
-        model_path = os.path.join(model_dir, "model.pth")  # عدّل الاسم لو مختلف
-        self.model = torch.load(model_path, map_location=self.device)
-        self.model.eval()
-
-        # إعداد التحويلات للصورة
-        self.transform = transforms.Compose([
-            transforms.Resize((224, 224)),
-            transforms.ToTensor(),
-        ])
-
-    def preprocess(self, image: Image.Image):
-        image = image.convert("RGB")
-        return self.transform(image).unsqueeze(0).to(self.device)
-
-    def predict(self, inputs):
-        image = inputs.get("image")
-        if image is None:
-            return {"error": "No image provided"}
-
-        img_tensor = self.preprocess(image)
-        with torch.no_grad():
-            outputs = self.model(img_tensor)
-            probs = torch.nn.functional.softmax(outputs[0], dim=0)
-
-        results = {
-            label: float(probs[i]) for i, label in enumerate(self.labels)
-        }
-        return results
-
-    def __call__(self, data):
-        image_data = data.get("inputs") or data.get("image")
-        if isinstance(image_data, Image.Image):
-            return self.predict({"image": image_data})
-        return {"error": "Invalid input"}