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handler.bak.py

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+"""
+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.
+        
+        Args:
+            path: Model directory path (we actually ignore this and 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(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
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"🖥️ Running on: {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")
+            
+        except Exception as e:
+            print(f"⚠️ PULSE demo approach failed: {e}")
+            print("🔄 Falling back to pipeline...")
+            
+            try:
+                # Fallback - using pipeline
+                from transformers import pipeline
+                
+                print("📦 Fetching 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
+                    }
+                )
+                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
+                
+                # 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
+                
+        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()
+        else:
+            return {
+                'status': 'healthy',
+                'model': 'PULSE-7B',
+                'timestamp': time.time(),
+                'handler_version': '2.0.0'
+            }
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        Main processing function - where the magic happens!
+        
+        Args:
+            data: Input data with 'inputs' and optional 'parameters'
+        
+        Returns:
+            List with the generated response
+        """
+        # Quick check - is our model ready?
+        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"
+            }]
+        
+        try:
+            # Parse the inputs - flexible format support
+            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
+            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."}]
+            
+            # Get generation parameters - using PULSE-7B demo's exact settings
+            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
+            
+            # Using pipeline? Let's go!
+            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
+                )
+                
+                # Pipeline returns a list, let's handle it
+                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]
+                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]
+            
+            # Manual generation mode - using PULSE demo's exact approach
+            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
+                encoded = self.tokenizer(
+                    text,
+                    return_tensors="pt",
+                    truncation=True,
+                    max_length=4096  # Increased for longer prompts
+                )
+                
+                input_ids = encoded["input_ids"].to(self.device)
+                attention_mask = encoded.get("attention_mask")
+                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
+                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
+                    )
+                
+                # Decode and clean response
+                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]
+            
+            
+        except Exception as e:
+            error_msg = f"Something went wrong during generation: {str(e)}"
+            print(f"❌ {error_msg}")
+            return [{
+                "generated_text": "",
+                "error": error_msg,
+                "handler": "Ubden® Team Enhanced Handler"
+            }]

handler.py

@@ -1,540 +1,245 @@
-"""
-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.
-        
-        Args:
-            path: Model directory path (we actually ignore this and 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(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
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        print(f"🖥️ Running on: {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")
-            
-        except Exception as e:
-            print(f"⚠️ PULSE demo approach failed: {e}")
-            print("🔄 Falling back to pipeline...")
-            
-            try:
-                # Fallback - using pipeline
-                from transformers import pipeline
-                
-                print("📦 Fetching 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
-                    }
-                )
-                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
-                
-                # 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
-                
-        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()
-        else:
-            return {
-                'status': 'healthy',
-                'model': 'PULSE-7B',
-                'timestamp': time.time(),
-                'handler_version': '2.0.0'
-            }
-
-    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """
-        Main processing function - where the magic happens!
-        
-        Args:
-            data: Input data with 'inputs' and optional 'parameters'
-        
-        Returns:
-            List with the generated response
-        """
-        # Quick check - is our model ready?
-        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"
-            }]
-        
-        try:
-            # Parse the inputs - flexible format support
-            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
-            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."}]
-            
-            # Get generation parameters - using PULSE-7B demo's exact settings
-            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
-            
-            # Using pipeline? Let's go!
-            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
-                )
-                
-                # Pipeline returns a list, let's handle it
-                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]
-                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]
-            
-            # Manual generation mode - using PULSE demo's exact approach
-            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
-                encoded = self.tokenizer(
-                    text,
-                    return_tensors="pt",
-                    truncation=True,
-                    max_length=4096  # Increased for longer prompts
-                )
-                
-                input_ids = encoded["input_ids"].to(self.device)
-                attention_mask = encoded.get("attention_mask")
-                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
-                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
-                    )
-                
-                # Decode and clean response
-                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]
-            
-            
-        except Exception as e:
-            error_msg = f"Something went wrong during generation: {str(e)}"
-            print(f"❌ {error_msg}")
-            return [{
-                "generated_text": "",
-                "error": error_msg,
-                "handler": "Ubden® Team Enhanced Handler"
+"""
+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
+
+
+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.
+        
+        Args:
+            path: Model directory path (we actually ignore this and load from HF hub)
+        """
+        print("🚀 Starting up PULSE-7B handler...")
+        print("📝 Enhanced by Ubden® Team - github.com/ck-cankurt")
+        
+        # Let's see what hardware we're working with
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"🖥️ Running on: {self.device}")
+        
+        try:
+            # First attempt - using pipeline (easiest and most stable way)
+            from transformers import pipeline
+            
+            print("📦 Fetching 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"⚠️ Pipeline didn't work out: {e}")
+            print("🔄 Let me try a different approach...")
+            
+            try:
+                # Plan B - load model and tokenizer separately
+                from transformers import AutoTokenizer, LlamaForCausalLM
+                
+                # Get the tokenizer ready
+                print("📖 Setting up tokenizer...")
+                self.tokenizer = AutoTokenizer.from_pretrained(
+                    "PULSE-ECG/PULSE-7B",
+                    trust_remote_code=True
+                )
+                
+                # Load the model as Llama (it works, trust me!)
+                print("🧠 Loading the 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_map="auto",
+                    low_cpu_mem_usage=True,
+                    trust_remote_code=True
+                )
+                
+                # Quick fix for padding token if it's missing
+                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
+                print("✅ Model loaded successfully via direct loading!")
+                
+            except Exception as e2:
+                print(f"😓 That didn't work either: {e2}")
+                # If all else fails, we'll handle it gracefully
+                self.pipe = None
+                self.model = None
+                self.tokenizer = None
+                self.use_pipeline = None
+        else:
+            self.use_pipeline = True
+
+    def process_image_input(self, image_input):
+        """
+        Handle both URL and base64 image inputs like a champ!
+        
+        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]}...")
+                response = requests.get(image_input, timeout=10)
+                response.raise_for_status()
+                image = Image.open(BytesIO(response.content)).convert('RGB')
+                print("✅ Image downloaded successfully!")
+                return image
+            
+            # Must be base64 then
+            elif isinstance(image_input, str):
+                print("🔍 Decoding base64 image...")
+                # Remove the data URL prefix if it exists
+                if "base64," in image_input:
+                    image_input = image_input.split("base64,")[1]
+                
+                image_data = base64.b64decode(image_input)
+                image = Image.open(BytesIO(image_data)).convert('RGB')
+                print("✅ Image decoded successfully!")
+                return image
+                
+        except Exception as e:
+            print(f"❌ Couldn't process the image: {e}")
+            return None
+        
+        return None
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        Main processing function - where the magic happens!
+        
+        Args:
+            data: Input data with 'inputs' and optional 'parameters'
+        
+        Returns:
+            List with the generated response
+        """
+        # Quick check - is our model ready?
+        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"
+            }]
+        
+        try:
+            # Parse the inputs - flexible format support
+            inputs = data.get("inputs", "")
+            text = ""
+            image = None
+            
+            if isinstance(inputs, dict):
+                # Dictionary input - check for text and image
+                text = 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:
+                        # For now, we'll add a note about the image since we're text-only
+                        text = f"[Image provided - {image.size[0]}x{image.size[1]} pixels] {text}"
+            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."}]
+            
+            # Get generation parameters with sensible defaults
+            parameters = data.get("parameters", {})
+            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!
+            if self.use_pipeline:
+                result = self.pipe(
+                    text,
+                    max_new_tokens=max_new_tokens,
+                    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
+                )
+                
+                # Pipeline returns a list, let's handle it
+                if isinstance(result, list) and len(result) > 0:
+                    return [{"generated_text": result[0].get("generated_text", "")}]
+                else:
+                    return [{"generated_text": str(result)}]
+            
+            # Manual generation mode
+            else:
+                # Tokenize the input
+                encoded = self.tokenizer(
+                    text,
+                    return_tensors="pt",
+                    truncation=True,
+                    max_length=2048
+                )
+                
+                input_ids = encoded["input_ids"].to(self.device)
+                attention_mask = encoded.get("attention_mask")
+                if attention_mask is not None:
+                    attention_mask = attention_mask.to(self.device)
+                
+                # Generate the response
+                with torch.no_grad():
+                    outputs = self.model.generate(
+                        input_ids,
+                        attention_mask=attention_mask,
+                        max_new_tokens=max_new_tokens,
+                        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
+                    )
+                
+                # Decode only the new tokens (not the input)
+                generated_ids = outputs[0][input_ids.shape[-1]:]
+                generated_text = self.tokenizer.decode(
+                    generated_ids,
+                    skip_special_tokens=True,
+                    clean_up_tokenization_spaces=True
+                )
+                
+                return [{"generated_text": generated_text}]
+            
+        except Exception as e:
+            error_msg = f"Something went wrong during generation: {str(e)}"
+            print(f"❌ {error_msg}")
+            return [{
+                "generated_text": "",
+                "error": error_msg,
+                "handler": "Ubden® Team Enhanced Handler"
             }]