Update handler.py

handler.py

@@ -436,87 +436,7 @@ class EndpointHandler:
                 print(f"🔥 Manual generation with PULSE demo logic: temp={temperature}, tokens={max_new_tokens}")
                 print(f"📝 Input text: '{text[:100]}...'")
 
-                # ... inputs/text/image ayrıştırmasını yaptığın yerin hemen altına ekle ...
-
-                use_multimodal = (
-                    LLAVA_AVAILABLE
-                    and hasattr(self, "model") and self.model is not None
-                    and hasattr(self, "image_processor") and self.image_processor is not None
-                    and image is not None
-                )
-                
-                if use_multimodal:
-                    try:
-                        # 1) LLaVA prompt (konuşma şablonu)
-                        from llava.constants import (
-                            IMAGE_TOKEN_INDEX,
-                            DEFAULT_IMAGE_TOKEN,
-                            DEFAULT_IM_START_TOKEN,
-                            DEFAULT_IM_END_TOKEN,
-                        )
-                        from llava.conversation import conv_templates
-                
-                        conv = conv_templates.get("llava_v1") or conv_templates[list(conv_templates.keys())[0]]
-                        conv = conv.copy()
-                        conv.append_message(conv.roles[0], text)
-                        conv.append_message(conv.roles[1], None)
-                        prompt = conv.get_prompt()
-                
-                        # 2) <image> sentinel'i başa ekle + gerekirse IM_START/END
-                        image_token = DEFAULT_IMAGE_TOKEN
-                        if getattr(getattr(self.model, "config", object()), "mm_use_im_start_end", False):
-                            image_token = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN
-                        prompt = image_token + "\n" + prompt
-                
-                        # 3) Görseli tensöre çevir
-                        imgs = process_images([image], self.image_processor, self.model.config)
-                        model_device = next(self.model.parameters()).device
-                        model_dtype  = next(self.model.parameters()).dtype
-                        if isinstance(imgs, list):
-                            images_tensor = [im.to(model_device, dtype=model_dtype) for im in imgs]
-                        else:
-                            images_tensor = imgs.to(model_device, dtype=model_dtype)
-                        image_sizes = [image.size]
-                
-                        # 4) Promptu tokenize et (image sentinel'ı için özel tokenizer)
-                        input_ids = tokenizer_image_token(
-                            prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt'
-                        ).unsqueeze(0).to(model_device)
-                
-                        # 5) Sağlam attention mask (bazı sürümler istiyor)
-                        attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=model_device)
-                
-                        # 6) Jenerasyon — medikal için deterministik önerilir
-                        gen_kwargs = dict(
-                            max_new_tokens=min(parameters.get("max_new_tokens", 512), 1024),
-                            temperature=0.0,
-                            top_p=1.0,
-                            do_sample=False,
-                            repetition_penalty=parameters.get("repetition_penalty", 1.0),
-                            pad_token_id=self.tokenizer.pad_token_id,
-                            eos_token_id=getattr(self.tokenizer, "eos_token_id", None),
-                        )
-                
-                        out = self.model.generate(
-                            inputs=input_ids,
-                            attention_mask=attention_mask,
-                            images=images_tensor,
-                            image_sizes=image_sizes,
-                            **gen_kwargs
-                        )
-                
-                        # 7) Decode
-                        new_tokens = out.shape[-1] - input_ids.shape[-1]
-                        resp_ids = out[:, -new_tokens:] if new_tokens > 0 else out
-                        generated_text = self.tokenizer.decode(resp_ids[0], skip_special_tokens=True).strip()
-                
-                        return [{"generated_text": generated_text, "mode": "multimodal"}]
-                
-                    except Exception as e:
-                        print(f"[⚠️] Multimodal path failed → falling back to text-only: {e}")
-                        # Buradan sonra senin mevcut metin-only yolun çalışmaya devam etsin
-                        # (hiçbir şey return etme; aşağıdaki text-only blok zaten var)
-
+               
                 
                 # Text-only generation with enhanced ECG context
                 print("🔤 Using enhanced text-only generation with ECG context")