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generation_config.json

@@ -1,49 +1,8 @@
 {
-  "_from_model_config": true,
   "attn_implementation": "flash_attention_2",
   "bos_token_id": 1,
   "eos_token_id": 2,
-  "pad_token_id": 0,
   "max_length": 4096,
-  "max_new_tokens": 2048,
-  "min_length": 1,
-  "min_new_tokens": 1,
-  "early_stopping": false,
-  "max_time": null,
-  "do_sample": true,
-  "num_beams": 1,
-  "num_beam_groups": 1,
-  "penalty_alpha": null,
-  "use_cache": true,
-  "temperature": 0.2,
-  "top_k": 50,
-  "top_p": 0.9,
-  "typical_p": 1.0,
-  "epsilon_cutoff": 0.0,
-  "eta_cutoff": 0.0,
-  "diversity_penalty": 0.0,
-  "repetition_penalty": 1.05,
-  "encoder_repetition_penalty": 1.0,
-  "length_penalty": 1.0,
-  "no_repeat_ngram_size": 0,
-  "bad_words_ids": null,
-  "force_words_ids": null,
-  "renormalize_logits": false,
-  "constraints": null,
-  "forced_bos_token_id": null,
-  "forced_eos_token_id": null,
-  "remove_invalid_values": false,
-  "exponential_decay_length_penalty": null,
-  "suppress_tokens": null,
-  "begin_suppress_tokens": null,
-  "forced_decoder_ids": null,
-  "sequence_bias": null,
-  "guidance_scale": null,
-  "low_memory": null,
-  "num_return_sequences": 1,
-  "output_attentions": false,
-  "output_hidden_states": false,
-  "output_scores": false,
-  "return_dict_in_generate": false,
-  "transformers_version": "4.40.0"
+  "pad_token_id": 0,
+  "transformers_version": "4.37.2"
 }

handler.py

@@ -1,450 +1,146 @@
-"""
-PULSE-7B Enhanced Handler
-Ubden® Team - Edited by https://github.com/ck-cankurt
-Support: Text, Image URLs, and Base64 encoded images
-"""
-
 import torch
 from typing import Dict, List, Any
-import base64
-from io import BytesIO
-from PIL import Image
-import requests
-import time
-
-# Import utilities if available
-try:
-    from utils import (
-        performance_monitor, 
-        validate_image_input, 
-        sanitize_parameters, 
-        get_system_info,
-        create_health_check,
-        deepseek_client
-    )
-    UTILS_AVAILABLE = True
-except ImportError:
-    UTILS_AVAILABLE = False
-    deepseek_client = None
-    print("⚠️ Utils module not found - performance monitoring and DeepSeek integration disabled")
-
-# Try to import LLaVA modules for proper conversation handling
-try:
-    from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
-    from llava.conversation import conv_templates, SeparatorStyle
-    from llava.mm_utils import tokenizer_image_token, process_images, KeywordsStoppingCriteria
-    LLAVA_AVAILABLE = True
-    print("✅ LLaVA modules imported successfully")
-except ImportError:
-    LLAVA_AVAILABLE = False
-    print("⚠️ LLaVA modules not available - using basic text processing")
 
 
 class EndpointHandler:
     def __init__(self, path=""):
         """
-        Hey there! Let's get this PULSE-7B model up and running.
-        We'll load it from the HuggingFace hub directly, so no worries about local files.
+        Initialize the handler for PULSE-7B model.
+        Direct reference to the original model.
         
         Args:
-            path: Model directory path (we actually ignore this and load from HF hub)
+            path: Path to the model directory (not used, we load from HF hub)
         """
-        print("🚀 Starting up PULSE-7B handler...")
-        print("📝 Enhanced by Ubden® Team - github.com/ck-cankurt")
-        import sys
-        print(f"🔧 Python version: {sys.version}")
-        print(f"🔧 PyTorch version: {torch.__version__}")
-        
-        # Check transformers version
-        try:
-            import transformers
-            print(f"🔧 Transformers version: {transformers.__version__}")
-            
-            # PULSE LLaVA works with transformers==4.37.2
-            if transformers.__version__ == "4.37.2":
-                print("✅ Using PULSE LLaVA compatible version (4.37.2)")
-            elif "dev" in transformers.__version__ or "git" in str(transformers.__version__):
-                print("⚠️ Using development version - may conflict with PULSE LLaVA")
-            else:
-                print("⚠️ Using different version - PULSE LLaVA prefers 4.37.2")
-        except Exception as e:
-            print(f"❌ Error checking transformers version: {e}")
+        print("Initializing PULSE-7B handler...")
         
-        print(f"🔧 CUDA available: {torch.cuda.is_available()}")
-        if torch.cuda.is_available():
-            print(f"🔧 CUDA device: {torch.cuda.get_device_name(0)}")
-        
-        # Let's see what hardware we're working with
+        # Device ayarla
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        print(f"🖥️ Running on: {self.device}")
+        print(f"Using device: {self.device}")
         
         try:
-            # First attempt - PULSE demo's exact approach
-            if LLAVA_AVAILABLE:
-                print("📦 Using PULSE demo's load_pretrained_model approach...")
-                from llava.model.builder import load_pretrained_model
-                from llava.mm_utils import get_model_name_from_path
-                
-                model_path = "PULSE-ECG/PULSE-7B"
-                model_name = get_model_name_from_path(model_path)
-                
-                self.tokenizer, self.model, self.image_processor, self.context_len = load_pretrained_model(
-                    model_path=model_path,
-                    model_base=None,
-                    model_name=model_name,
-                    load_8bit=False,
-                    load_4bit=False
-                )
-                
-                # Move model to device like demo
-                self.model = self.model.to(self.device)
-                self.use_pipeline = False
-                print("✅ Model loaded successfully with PULSE demo's approach!")
-                print(f"📸 Image processor: {type(self.image_processor).__name__}")
-                
-            else:
-                raise ImportError("LLaVA modules not available")
+            # Pipeline kullan - en basit ve güvenilir yöntem
+            from transformers import pipeline
+            
+            print("Loading model from HuggingFace Hub...")
+            self.pipe = pipeline(
+                "text-generation",
+                model="PULSE-ECG/PULSE-7B",
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                device=0 if torch.cuda.is_available() else -1,
+                trust_remote_code=True,
+                model_kwargs={
+                    "low_cpu_mem_usage": True,
+                    "use_safetensors": True
+                }
+            )
+            print("Model loaded successfully via pipeline!")
             
         except Exception as e:
-            print(f"⚠️ PULSE demo approach failed: {e}")
-            print("🔄 Falling back to pipeline...")
+            print(f"Pipeline loading failed: {e}")
+            print("Trying alternative loading method...")
             
             try:
-                # Fallback - using pipeline
-                from transformers import pipeline
+                # Alternatif: Model ve tokenizer'ı ayrı yükle
+                from transformers import AutoTokenizer, LlamaForCausalLM
+                
+                # Tokenizer'ı yükle
+                print("Loading tokenizer...")
+                self.tokenizer = AutoTokenizer.from_pretrained(
+                    "PULSE-ECG/PULSE-7B",
+                    trust_remote_code=True
+                )
                 
-                print("📦 Fetching model from HuggingFace Hub...")
-                self.pipe = pipeline(
-                    "text-generation",
-                    model="PULSE-ECG/PULSE-7B",
+                # Model'i Llama olarak yükle
+                print("Loading model as Llama...")
+                self.model = LlamaForCausalLM.from_pretrained(
+                    "PULSE-ECG/PULSE-7B",
                     torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-                    device=0 if torch.cuda.is_available() else -1,
-                    trust_remote_code=True,
-                    model_kwargs={
-                        "low_cpu_mem_usage": True,
-                        "use_safetensors": True
-                    }
+                    device_map="auto",
+                    low_cpu_mem_usage=True,
+                    trust_remote_code=True
                 )
-                self.use_pipeline = True
-                self.image_processor = None
-                print("✅ Model loaded successfully via pipeline!")
-                
-            except Exception as e2:
-                print(f"😓 Pipeline also failed: {e2}")
-                
-                try:
-                    # Last resort - manual loading
-                    from transformers import AutoTokenizer, LlamaForCausalLM
-                    
-                    print("📖 Manual loading as last resort...")
-                    self.tokenizer = AutoTokenizer.from_pretrained(
-                        "PULSE-ECG/PULSE-7B",
-                        trust_remote_code=True
-                    )
-                    
-                    self.model = LlamaForCausalLM.from_pretrained(
-                        "PULSE-ECG/PULSE-7B",
-                        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-                        device_map="auto",
-                        low_cpu_mem_usage=True,
-                        trust_remote_code=True
-                    )
-                    
-                    if self.tokenizer.pad_token is None:
-                        self.tokenizer.pad_token = self.tokenizer.eos_token
-                        self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
-                    
-                    self.model.eval()
-                    self.use_pipeline = False
-                    self.image_processor = None
-                    print("✅ Model loaded manually!")
-                    
-                except Exception as e3:
-                    print(f"😓 All approaches failed: {e3}")
-                    self.pipe = None
-                    self.model = None
-                    self.tokenizer = None
-                    self.image_processor = None
-                    self.use_pipeline = None
-        
-        # Final status report
-        print("\n🔍 Model Loading Status Report:")
-        print(f"   - use_pipeline: {self.use_pipeline}")
-        print(f"   - model: {'✅ Loaded' if hasattr(self, 'model') and self.model is not None else '❌ None'}")
-        print(f"   - tokenizer: {'✅ Loaded' if hasattr(self, 'tokenizer') and self.tokenizer is not None else '❌ None'}")
-        print(f"   - image_processor: {'✅ Loaded' if hasattr(self, 'image_processor') and self.image_processor is not None else '❌ None'}")
-        print(f"   - pipe: {'✅ Loaded' if hasattr(self, 'pipe') and self.pipe is not None else '❌ None'}")
-        
-        # Check if any model component loaded successfully
-        has_model = hasattr(self, 'model') and self.model is not None
-        has_tokenizer = hasattr(self, 'tokenizer') and self.tokenizer is not None
-        has_pipe = hasattr(self, 'pipe') and self.pipe is not None
-        has_image_processor = hasattr(self, 'image_processor') and self.image_processor is not None
-        
-        if not (has_model or has_tokenizer or has_pipe):
-            print("💥 CRITICAL: No model components loaded successfully!")
-        else:
-            print("✅ At least one model component loaded successfully")
-            if has_image_processor:
-                print("🖼️ Vision capabilities available!")
-            else:
-                print("⚠️ No image processor - text-only mode")
-
-    def is_valid_image_format(self, filename_or_url):
-        """Validate image format like PULSE demo"""
-        # Demo's supported formats
-        image_extensions = ["jpg", "jpeg", "png", "bmp", "gif", "tiff", "webp", "heic", "heif", "jfif", "svg", "eps", "raw"]
-        
-        if filename_or_url.startswith(('http://', 'https://')):
-            # For URLs, check the extension or content-type
-            ext = filename_or_url.split('.')[-1].split('?')[0].lower()
-            return ext in image_extensions
-        else:
-            # For base64 or local files
-            return True  # Base64 will be validated during decode
-    
-    def process_image_input(self, image_input):
-        """
-        Handle both URL and base64 image inputs exactly like PULSE demo
-        
-        Args:
-            image_input: Can be a URL string or base64 encoded image
-            
-        Returns:
-            PIL Image object or None if something goes wrong
-        """
-        try:
-            # Check if it's a URL (starts with http/https)
-            if isinstance(image_input, str) and (image_input.startswith('http://') or image_input.startswith('https://')):
-                print(f"🌐 Fetching image from URL: {image_input[:50]}...")
                 
-                # Validate format like demo
-                if not self.is_valid_image_format(image_input):
-                    print("❌ Invalid image format in URL")
-                    return None
+                # Padding token ayarla
+                if self.tokenizer.pad_token is None:
+                    self.tokenizer.pad_token = self.tokenizer.eos_token
+                    self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
                 
-                # Demo's exact image loading approach
-                response = requests.get(image_input, timeout=15)
-                if response.status_code == 200:
-                    image = Image.open(BytesIO(response.content)).convert("RGB")
-                    print(f"✅ Image downloaded successfully! Size: {image.size}")
-                    return image
-                else:
-                    print(f"❌ Failed to load image: status {response.status_code}")
-                    return None
-            
-            # Must be base64 then
-            elif isinstance(image_input, str):
-                print("🔍 Decoding base64 image...")
-                
-                # Remove the data URL prefix if it exists
-                base64_data = image_input
-                if "base64," in image_input:
-                    base64_data = image_input.split("base64,")[1]
-                
-                # Clean and validate base64 data
-                base64_data = base64_data.strip().replace('\n', '').replace('\r', '').replace(' ', '')
-                
-                try:
-                    image_data = base64.b64decode(base64_data)
-                    image = Image.open(BytesIO(image_data)).convert('RGB')
-                    print(f"✅ Base64 image decoded successfully! Size: {image.size}")
-                    return image
-                except Exception as decode_error:
-                    print(f"❌ Base64 decode error: {decode_error}")
-                    return None
+                self.model.eval()
+                self.use_pipeline = False
+                print("Model loaded successfully via direct loading!")
                 
-        except Exception as e:
-            print(f"❌ Couldn't process the image: {e}")
-            return None
-        
-        return None
-
-    def add_turkish_commentary(self, response: Dict[str, Any], enable_commentary: bool, timeout: int = 30) -> Dict[str, Any]:
-        """Add Turkish commentary to the response using DeepSeek API"""
-        if not enable_commentary:
-            return response
-            
-        if not UTILS_AVAILABLE or not deepseek_client:
-            print("⚠️ DeepSeek client not available - skipping Turkish commentary")
-            response["commentary_status"] = "unavailable"
-            return response
-            
-        if not deepseek_client.is_available():
-            print("⚠️ DeepSeek API key not configured - skipping Turkish commentary")
-            response["commentary_status"] = "api_key_missing"
-            return response
-            
-        generated_text = response.get("generated_text", "")
-        if not generated_text:
-            print("⚠️ No generated text to comment on")
-            response["commentary_status"] = "no_text"
-            return response
-            
-        print("🔄 DeepSeek ile Türkçe yorum ekleniyor...")
-        commentary_result = deepseek_client.get_turkish_commentary(generated_text, timeout)
-        
-        if commentary_result["success"]:
-            response["comment_text"] = commentary_result["comment_text"]
-            response["commentary_model"] = commentary_result.get("model", "deepseek-chat")
-            response["commentary_tokens"] = commentary_result.get("tokens_used", 0)
-            response["commentary_status"] = "success"
-            print("✅ Türkçe yorum başarıyla eklendi")
-        else:
-            response["comment_text"] = ""
-            response["commentary_error"] = commentary_result["error"]
-            response["commentary_status"] = "failed"
-            print(f"❌ Türkçe yorum eklenemedi: {commentary_result['error']}")
-            
-        return response
-
-    def health_check(self) -> Dict[str, Any]:
-        """Health check endpoint"""
-        if UTILS_AVAILABLE:
-            return create_health_check()
+            except Exception as e2:
+                print(f"Alternative loading also failed: {e2}")
+                # En son çare: Basit bir fallback mesajı
+                self.pipe = None
+                self.model = None
+                self.tokenizer = None
+                self.use_pipeline = None
         else:
-            return {
-                'status': 'healthy',
-                'model': 'PULSE-7B',
-                'timestamp': time.time(),
-                'handler_version': '2.0.0'
-            }
+            self.use_pipeline = True
 
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """
-        Main processing function - where the magic happens!
+        Process the inference request.
         
         Args:
-            data: Input data with 'inputs' and optional 'parameters'
+            data: Input data containing 'inputs' and optional 'parameters'
         
         Returns:
-            List with the generated response
+            List containing the generated response
         """
-        # Quick check - is our model ready?
+        # Model yüklenemediyse hata döndür
         if self.use_pipeline is None:
             return [{
-                "generated_text": "Oops! Model couldn't load properly. Please check the deployment settings.",
-                "error": "Model initialization failed",
-                "handler": "Ubden® Team Enhanced Handler"
+                "generated_text": "Model could not be loaded. Please check the deployment configuration.",
+                "error": "Model initialization failed"
             }]
         
         try:
-            # Parse the inputs - flexible format support
+            # Input'ları al
             inputs = data.get("inputs", "")
-            text = ""
-            image = None
-            
             if isinstance(inputs, dict):
-                # Dictionary input - check for text and image
-                # Support query field (new) plus original text/prompt fields
-                text = inputs.get("query", inputs.get("text", inputs.get("prompt", str(inputs))))
-                
-                # Check for image in various formats
-                image_input = inputs.get("image", inputs.get("image_url", inputs.get("image_base64", None)))
-                if image_input:
-                    image = self.process_image_input(image_input)
-                    if image:
-                        # Since we're in text-only mode, create smart ECG context
-                        print(f"🖼️ Image loaded: {image.size[0]}x{image.size[1]} pixels - using text-only ECG analysis mode")
-                        
-                        # Create ECG-specific prompt that mimics visual analysis
-                        ecg_context = f"Analyzing an ECG image ({image.size[0]}x{image.size[1]} pixels). "
-                        
-                        # Use demo's exact approach - no additional context, just the query
-                        # Model is trained to understand ECG images from text queries
-                        pass  # Keep text exactly as received
+                text = inputs.get("text", inputs.get("prompt", str(inputs)))
             else:
-                # Simple string input
                 text = str(inputs)
             
             if not text:
-                return [{"generated_text": "Hey, I need some text to work with! Please provide an input."}]
+                return [{"generated_text": "Please provide an input text."}]
             
-            # Get generation parameters - using PULSE-7B demo's exact settings
+            # Parametreleri al
             parameters = data.get("parameters", {})
-            max_new_tokens = min(parameters.get("max_new_tokens", 1024), 8192)  # Demo uses 1024 default
-            temperature = parameters.get("temperature", 0.05)  # Demo uses 0.05 for precise medical analysis
-            top_p = parameters.get("top_p", 1.0)  # Demo uses 1.0 for full vocabulary access
-            do_sample = parameters.get("do_sample", True)  # Demo uses sampling
-            repetition_penalty = parameters.get("repetition_penalty", 1.0)  # Demo default
-            
-            print(f"🎛️ Generation params: max_tokens={max_new_tokens}, temp={temperature}, top_p={top_p}, do_sample={do_sample}, rep_penalty={repetition_penalty}")
-            
-            # Check if Turkish commentary is requested (NEW FEATURE)
-            enable_turkish_commentary = parameters.get("enable_turkish_commentary", False)  # Default false
+            max_new_tokens = min(parameters.get("max_new_tokens", 256), 1024)
+            temperature = parameters.get("temperature", 0.7)
+            top_p = parameters.get("top_p", 0.95)
+            do_sample = parameters.get("do_sample", True)
+            repetition_penalty = parameters.get("repetition_penalty", 1.0)
             
-            # Using pipeline? Let's go!
+            # Pipeline kullanıyorsak
             if self.use_pipeline:
-                print(f"🎛️ Pipeline generation: temp={temperature}, tokens={max_new_tokens}")
-                print(f"📝 Input text: '{text[:100]}...'")
-                
                 result = self.pipe(
                     text,
                     max_new_tokens=max_new_tokens,
-                                            min_new_tokens=200,  # Force very detailed analysis to match demo
                     temperature=temperature,
                     top_p=top_p,
                     do_sample=do_sample,
                     repetition_penalty=repetition_penalty,
-                    return_full_text=False  # Just the new stuff, not the input
+                    return_full_text=False  # Sadece yeni üretilen metni döndür
                 )
                 
-                # Pipeline returns a list, let's handle it
+                # Pipeline list döndürür
                 if isinstance(result, list) and len(result) > 0:
-                    generated_text = result[0].get("generated_text", "").strip()
-                    
-                    print(f"🔍 Pipeline debug:")
-                    print(f"   - Raw result: '{str(result[0])[:200]}...'")
-                    print(f"   - Generated text length: {len(generated_text)}")
-                    
-                    # Clean up common issues
-                    if generated_text.startswith(text):
-                        generated_text = generated_text[len(text):].strip()
-                        print("🔧 Removed input text from output")
-                    
-                    # Remove common artifacts
-                    generated_text = generated_text.replace("</s>", "").strip()
-                    
-                    if not generated_text:
-                        print("❌ Pipeline generated empty text!")
-                        generated_text = "Empty response from pipeline. Please try different parameters."
-                    
-                    print(f"✅ Final pipeline text: '{generated_text[:100]}...' (length: {len(generated_text)})")
-                    
-                    # Create response
-                    response = {"generated_text": generated_text}
-                    
-                    # Add Turkish commentary if requested (NEW FEATURE)
-                    if enable_turkish_commentary:
-                        response = self.add_turkish_commentary(response, True)
-                    
-                    return [response]
+                    return [{"generated_text": result[0].get("generated_text", "")}]
                 else:
-                    generated_text = str(result).strip()
-                    
-                    # Create response
-                    response = {"generated_text": generated_text}
-                    
-                    # Add Turkish commentary if requested (NEW FEATURE)
-                    if enable_turkish_commentary:
-                        response = self.add_turkish_commentary(response, True)
-                    
-                    return [response]
+                    return [{"generated_text": str(result)}]
             
-            # Manual generation mode - using PULSE demo's exact approach
+            # Manuel generation kullanıyorsak
             else:
-                print(f"🔥 Manual generation with PULSE demo logic: temp={temperature}, tokens={max_new_tokens}")
-                print(f"📝 Input text: '{text[:100]}...'")
-                
-                # Text-only generation with enhanced ECG context
-                print("🔤 Using enhanced text-only generation with ECG context")
-                
-                # Tokenize the enhanced prompt
+                # Tokenize
                 encoded = self.tokenizer(
                     text,
                     return_tensors="pt",
                     truncation=True,
-                    max_length=4096  # Increased for longer prompts
+                    max_length=2048
                 )
                 
                 input_ids = encoded["input_ids"].to(self.device)
@@ -452,89 +148,34 @@ class EndpointHandler:
                 if attention_mask is not None:
                     attention_mask = attention_mask.to(self.device)
                 
-                print(f"🔍 Enhanced generation debug:")
-                print(f"   - Enhanced prompt length: {len(text)} chars")
-                print(f"   - Input tokens: {input_ids.shape[-1]}")
-                print(f"   - Prompt preview: '{text[:150]}...'")
-                
-                # Generate with enhanced settings for medical analysis
+                # Generate
                 with torch.no_grad():
                     outputs = self.model.generate(
                         input_ids,
                         attention_mask=attention_mask,
                         max_new_tokens=max_new_tokens,
-                        min_new_tokens=200,  # Force detailed response like demo
                         temperature=temperature,
                         top_p=top_p,
                         do_sample=do_sample,
                         repetition_penalty=repetition_penalty,
                         pad_token_id=self.tokenizer.pad_token_id,
-                        eos_token_id=self.tokenizer.eos_token_id,
-                        early_stopping=False
+                        eos_token_id=self.tokenizer.eos_token_id
                     )
                 
-                # Decode and clean response
+                # Decode - sadece yeni tokenleri al
                 generated_ids = outputs[0][input_ids.shape[-1]:]
                 generated_text = self.tokenizer.decode(
                     generated_ids,
                     skip_special_tokens=True,
                     clean_up_tokenization_spaces=True
-                ).strip()
-                
-                # Aggressive cleanup of artifacts
-                generated_text = generated_text.replace("</s>", "").strip()
-                
-                # Simple cleanup - just remove Answer prefix and parentheses
-                if generated_text.startswith("(Answer:") and ")" in generated_text:
-                    # Just remove the parentheses and Answer: prefix
-                    end_paren = generated_text.find(")")
-                    answer_content = generated_text[8:end_paren].strip()  # Remove "(Answer:"
-                    # Keep the rest of the response if there is any
-                    rest_of_response = generated_text[end_paren+1:].strip()
-                    
-                    if rest_of_response:
-                        generated_text = f"{answer_content}. {rest_of_response}"
-                    else:
-                        generated_text = answer_content
-                
-                elif generated_text.startswith("Answer:"):
-                    generated_text = generated_text[7:].strip()
-                
-                # Remove only clear training artifacts
-                cleanup_patterns = [
-                    "In this task",
-                    "I'm asking the respondent",
-                    "The respondent should"
-                ]
-                
-                for pattern in cleanup_patterns:
-                    if pattern in generated_text:
-                        parts = generated_text.split(pattern)
-                        generated_text = parts[0].strip()
-                        break
-                
-                # Only provide fallback if response is truly empty or malformed
-                if len(generated_text) < 10 or generated_text.startswith("7)"):
-                    print("⚠️ Malformed response detected, providing fallback...")
-                    generated_text = "This ECG shows cardiac electrical activity. For accurate interpretation, please consult with a qualified cardiologist who can analyze the specific waveforms, intervals, and morphology patterns."
-                
-                print(f"✅ Enhanced text-only generation: '{generated_text[:100]}...' (length: {len(generated_text)})")
-                
-                # Create response
-                response = {"generated_text": generated_text}
-                
-                # Add Turkish commentary if requested (NEW FEATURE)
-                if enable_turkish_commentary:
-                    response = self.add_turkish_commentary(response, True)
+                )
                 
-                return [response]
-            
+                return [{"generated_text": generated_text}]
             
         except Exception as e:
-            error_msg = f"Something went wrong during generation: {str(e)}"
-            print(f"❌ {error_msg}")
+            error_msg = f"Error during generation: {str(e)}"
+            print(error_msg)
             return [{
                 "generated_text": "",
-                "error": error_msg,
-                "handler": "Ubden® Team Enhanced Handler"
+                "error": error_msg
             }]

requirements.txt

@@ -3,8 +3,4 @@ torch>=2.1.0
 accelerate>=0.25.0
 sentencepiece
 safetensors
-protobuf
-Pillow>=9.0.0
-requests>=2.28.0
-numpy>=1.21.0
-psutil>=5.8.0
+protobuf