Update handler.py

handler.py

@@ -119,12 +119,7 @@ def load_image_any(image_input: Union[str, dict]) -> Image.Image:
     if isinstance(image_input, str):
         s = image_input.strip()
         if s.startswith(("http://", "https://")):
-            # ✅ UA ekleyerek CDN/Cloudflare engellerini azalt
-            r = requests.get(
-                s,
-                timeout=(5, 20),
-                headers={"User-Agent": "Mozilla/5.0 (compatible; RapidECG/1.0)"}
-            )
+            r = requests.get(s, timeout=(5, 20))
             r.raise_for_status()
             return Image.open(BytesIO(r.content)).convert("RGB")
         if os.path.exists(s):
@@ -275,8 +270,6 @@ def generate_response(
 
     # Görüntü ön-işleme → tensör
     try:
-        if chatbot.image_processor is None:
-            return {"error": "Image processing failed: image_processor is None"}
         processed = process_images([pil_img], chatbot.image_processor, chatbot.model.config)
         if isinstance(processed, (list, tuple)) and len(processed) > 0:
             image_tensor = processed[0]
@@ -368,7 +361,7 @@ def query(payload: dict):
     if not model_initialized:
         if not initialize_model():
             return {"error": "Model initialization failed"}
-        # model_initialized artık initialize_model içinde True set ediliyor
+        model_initialized = True
 
     try:
         message = payload.get("message") or payload.get("query") or payload.get("prompt") or payload.get("istem") or ""
@@ -434,7 +427,7 @@ class _Args:
         self.debug      = bool(int(os.getenv("DEBUG", "0")))
 
 def initialize_model():
-    global tokenizer, model, image_processor, context_len, args, model_initialized
+    global tokenizer, model, image_processor, context_len, args
     if not LLAVA_AVAILABLE:
         print("[init] LLaVA not available; cannot init.")
         return False
@@ -444,23 +437,6 @@ def initialize_model():
         tokenizer_, model_, image_processor_, context_len_ = load_pretrained_model(
             args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit
         )
-
-        # ✅ Fallback: image_processor None ise otomatik yükle (CLIP 336 varsayılan)
-        if image_processor_ is None:
-            try:
-                from transformers import AutoProcessor, AutoImageProcessor, CLIPImageProcessor
-                try:
-                    image_processor_ = AutoProcessor.from_pretrained(args.model_path, trust_remote_code=True)
-                except Exception:
-                    try:
-                        image_processor_ = AutoImageProcessor.from_pretrained("openai/clip-vit-large-patch14-336")
-                    except Exception:
-                        image_processor_ = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14-336")
-                print("[init] image_processor fallback activated.")
-            except Exception as e:
-                print(f"[init] image_processor fallback failed: {e}")
-                return False
-
         # demo: model'ı genelde cuda’da çalıştırır
         try:
             _ = next(model_.parameters()).device
@@ -469,14 +445,12 @@ def initialize_model():
                 model_ = model_.to(torch.device("cuda"))
         model_.eval()
 
-        # Global state
-        tokenizer = tokenizer_
-        model = model_
-        image_processor = image_processor_
-        context_len = context_len_
+        globals()["tokenizer"] = tokenizer_
+        globals()["model"] = model_
+        globals()["image_processor"] = image_processor_
+        globals()["context_len"] = context_len_
 
         chat_manager.init_if_needed(args, args.model_path, tokenizer_, model_, image_processor_, context_len_)
-        model_initialized = True  # ✅ sağlıkta doğru raporla
         print("[init] model/tokenizer/image_processor loaded.")
         return True
     except Exception as e:
@@ -500,4 +474,4 @@ class EndpointHandler:
         return get_model_info()
 
 if __name__ == "__main__":
-    print("Handler ready (Demo Parity + Style Hint + whitespace post-process). Use `EndpointHandler` or `query`.")
+    print("Handler ready (Demo Parity + Style Hint + whitespace post-process). Use `EndpointHandler` or `query`.")