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README.md

@@ -23,8 +23,6 @@ This repository provides a custom handler for deploying the **PULSE-7B** ECG ana
 
 **🚀 Enhanced with DeepSeek Integration**: This handler automatically translates PULSE-7B's English medical analysis into patient-friendly Turkish commentary using DeepSeek AI, providing bilingual ECG interpretation for Turkish healthcare professionals and patients.
 
-**📧 Automatic Email Processing**: When email configuration is provided, the system automatically processes ECG images sent via email and responds with analysis results, making it a complete email-to-analysis solution.
-
 ## 🚀 Quick Start
 
 ### Prerequisites
@@ -36,9 +34,8 @@ This repository provides a custom handler for deploying the **PULSE-7B** ECG ana
 
 ```
 pulse-hf/
-├── handler.py              # Custom inference handler with auto email processing
+├── handler.py              # Custom inference handler with DeepSeek integration
 ├── utils.py                # Performance monitoring and DeepSeek client
-├── email_ecg_processor.py  # Email processing module (auto-loaded by handler)
 ├── requirements.txt        # Python dependencies
 ├── generation_config.json  # Model generation configuration
 ├── test_requests.json      # Example request templates
@@ -71,26 +68,17 @@ pulse-hf/
 4. Click **"Create Endpoint"**
 5. Wait for the status to change from `Building` → `Initializing` → `Running`
 
-### Step 3: Configure Environment Variables
+### Step 3: Configure DeepSeek API Key (Optional)
+
+To enable Turkish commentary feature:
 
-#### 3.1 DeepSeek API Key (Optional - for Turkish commentary)
 1. Go to your endpoint's **"Environment"** tab
 2. In **"Secret Env"** section, add:
    - **Key**: `deep_key`
    - **Value**: Your DeepSeek API key
-
-#### 3.2 Email Processing (Optional - for automatic email processing)
-Add these additional environment variables:
-   - **Key**: `mail_host` | **Value**: `imap.gmail.com` (or your email provider)
-   - **Key**: `mail_username` | **Value**: Your email address
-   - **Key**: `mail_pw` | **Value**: Your email app password
-   - **Key**: `hf_key` | **Value**: Your HuggingFace token
-
 3. Click **"Update Endpoint"**
 
-**Note**: 
-- Without DeepSeek key: Works but no Turkish commentary
-- Without email config: Works but no automatic email processing
+**Note**: Without this configuration, the endpoint will work but without Turkish commentary.
 
 ### Step 4: Get Your Endpoint URL
 

base_request.md

@@ -1,46 +1,46 @@
-https://www.base64-image.de/  # image to base64 for test
-
-{
-    "inputs": {
-      "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
-      "image": "https://i.imgur.com/7uuejqO.jpeg"
-    },
-    "parameters": {
-      "max_new_tokens": 512,
-      "temperature": 0.2,
-      "top_p": 0.9,
-      "repetition_penalty": 1.05,
-      "stop": ["</s>"],
-      "return_full_text": false
-    }
-  }
-
-
-
-  {
-    "inputs": {
-      "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
-      "image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2..."
-    },
-    "parameters": {
-      "max_new_tokens": 512,
-      "temperature": 0.2,
-      "top_p": 0.9,
-      "repetition_penalty": 1.05,
-      "stop": ["</s>"],
-      "return_full_text": false
-    }
-  }
-
-
-  {
-    "inputs": {
-      "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
-      "image": "https://i.imgur.com/7uuejqO.jpeg"
-    },
-    "parameters": {
-      "temperature": 0.2,
-      "enable_turkish_commentary": true,    
-      "deepseek_timeout": 30               
-    }
+https://www.base64-image.de/  # image to base64 for test
+
+{
+    "inputs": {
+      "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
+      "image": "https://i.imgur.com/7uuejqO.jpeg"
+    },
+    "parameters": {
+      "max_new_tokens": 512,
+      "temperature": 0.2,
+      "top_p": 0.9,
+      "repetition_penalty": 1.05,
+      "stop": ["</s>"],
+      "return_full_text": false
+    }
+  }
+
+
+
+  {
+    "inputs": {
+      "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
+      "image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2..."
+    },
+    "parameters": {
+      "max_new_tokens": 512,
+      "temperature": 0.2,
+      "top_p": 0.9,
+      "repetition_penalty": 1.05,
+      "stop": ["</s>"],
+      "return_full_text": false
+    }
+  }
+
+
+  {
+    "inputs": {
+      "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
+      "image": "https://i.imgur.com/7uuejqO.jpeg"
+    },
+    "parameters": {
+      "temperature": 0.2,
+      "enable_turkish_commentary": true,    
+      "deepseek_timeout": 30               
+    }
   }

deployment_guide.md

@@ -1,133 +1,133 @@
-# PULSE-7B Handler Deployment Guide
-
-## 🚀 Deployment Rehberi
-
-### Gereksinimler
-- Python 3.8+
-- CUDA 11.8+ (GPU kullanımı için)
-- Minimum 16GB RAM (CPU), 8GB VRAM (GPU)
-
-### Kurulum
-
-1. **Bağımlılıkları yükleyin:**
-```bash
-pip install -r requirements.txt
-```
-
-2. **Flash Attention (isteğe bağlı, performans için):**
-```bash
-pip install flash-attn --no-build-isolation
-```
-
-### HuggingFace Inference Deployment
-
-#### 1. Model Repository Yapısı
-```
-your-model-repo/
-├── handler.py
-├── config.json
-├── generation_config.json
-├── requirements.txt
-├── model.safetensors.index.json
-├── tokenizer_config.json
-├── special_tokens_map.json
-└── tokenizer.model
-```
-
-#### 2. Endpoint Oluşturma
-```bash
-# HuggingFace CLI ile deploy
-huggingface-cli login
-huggingface-cli repo create your-pulse-endpoint --type=space
-```
-
-#### 3. Test Requests
-
-**Image URL ile test:**
-```bash
-curl -X POST "YOUR_ENDPOINT_URL" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "inputs": {
-      "query": "Analyze this ECG image",
-      "image": "https://i.imgur.com/7uuejqO.jpeg"
-    },
-    "parameters": {
-      "temperature": 0.2,
-      "max_new_tokens": 512
-    }
-  }'
-```
-
-**Base64 ile test:**
-```bash
-curl -X POST "YOUR_ENDPOINT_URL" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "inputs": {
-      "query": "What do you see in this ECG?",
-      "image": "data:image/jpeg;base64,/9j/4AAQ..."
-    },
-    "parameters": {
-      "temperature": 0.2
-    }
-  }'
-```
-
-### Performans Optimizasyonları
-
-#### GPU Memory Optimizasyonu
-- `torch_dtype=torch.bfloat16` kullanın
-- `low_cpu_mem_usage=True` ayarlayın
-- `device_map="auto"` ile otomatik dağıtım
-
-#### CPU Optimizasyonu
-- `torch_dtype=torch.float32` kullanın
-- Thread sayısını ayarlayın: `torch.set_num_threads(4)`
-
-### Monitoring ve Debugging
-
-#### Log Seviyeleri
-```python
-import logging
-logging.basicConfig(level=logging.INFO)
-```
-
-#### Memory Usage
-```python
-import torch
-print(f"GPU Memory: {torch.cuda.memory_allocated()/1024**3:.2f}GB")
-```
-
-### Troubleshooting
-
-#### Common Issues:
-
-1. **CUDA Out of Memory**
-   - Batch size'ı azaltın
-   - `max_new_tokens` değerini düşürün
-   - Gradient checkpointing kullanın
-
-2. **Slow Image Processing**
-   - Image timeout değerini artırın
-   - Image resize threshold ayarlayın
-
-3. **Model Loading Issues**
-   - HuggingFace token'ını kontrol edin
-   - Network bağlantısını doğrulayın
-   - Cache dizinini temizleyin
-
-### Security Best Practices
-
-- Image URL'leri validate edin
-- Base64 boyut limitlerini ayarlayın
-- Rate limiting uygulayın
-- Input sanitization yapın
-
-### Monitoring Metrics
-
-- Response time
-- Memory usage
-- Error rates
-- Image processing success rate
-- Token generation speed
+# PULSE-7B Handler Deployment Guide
+
+## 🚀 Deployment Rehberi
+
+### Gereksinimler
+- Python 3.8+
+- CUDA 11.8+ (GPU kullanımı için)
+- Minimum 16GB RAM (CPU), 8GB VRAM (GPU)
+
+### Kurulum
+
+1. **Bağımlılıkları yükleyin:**
+```bash
+pip install -r requirements.txt
+```
+
+2. **Flash Attention (isteğe bağlı, performans için):**
+```bash
+pip install flash-attn --no-build-isolation
+```
+
+### HuggingFace Inference Deployment
+
+#### 1. Model Repository Yapısı
+```
+your-model-repo/
+├── handler.py
+├── config.json
+├── generation_config.json
+├── requirements.txt
+├── model.safetensors.index.json
+├── tokenizer_config.json
+├── special_tokens_map.json
+└── tokenizer.model
+```
+
+#### 2. Endpoint Oluşturma
+```bash
+# HuggingFace CLI ile deploy
+huggingface-cli login
+huggingface-cli repo create your-pulse-endpoint --type=space
+```
+
+#### 3. Test Requests
+
+**Image URL ile test:**
+```bash
+curl -X POST "YOUR_ENDPOINT_URL" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "inputs": {
+      "query": "Analyze this ECG image",
+      "image": "https://i.imgur.com/7uuejqO.jpeg"
+    },
+    "parameters": {
+      "temperature": 0.2,
+      "max_new_tokens": 512
+    }
+  }'
+```
+
+**Base64 ile test:**
+```bash
+curl -X POST "YOUR_ENDPOINT_URL" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "inputs": {
+      "query": "What do you see in this ECG?",
+      "image": "data:image/jpeg;base64,/9j/4AAQ..."
+    },
+    "parameters": {
+      "temperature": 0.2
+    }
+  }'
+```
+
+### Performans Optimizasyonları
+
+#### GPU Memory Optimizasyonu
+- `torch_dtype=torch.bfloat16` kullanın
+- `low_cpu_mem_usage=True` ayarlayın
+- `device_map="auto"` ile otomatik dağıtım
+
+#### CPU Optimizasyonu
+- `torch_dtype=torch.float32` kullanın
+- Thread sayısını ayarlayın: `torch.set_num_threads(4)`
+
+### Monitoring ve Debugging
+
+#### Log Seviyeleri
+```python
+import logging
+logging.basicConfig(level=logging.INFO)
+```
+
+#### Memory Usage
+```python
+import torch
+print(f"GPU Memory: {torch.cuda.memory_allocated()/1024**3:.2f}GB")
+```
+
+### Troubleshooting
+
+#### Common Issues:
+
+1. **CUDA Out of Memory**
+   - Batch size'ı azaltın
+   - `max_new_tokens` değerini düşürün
+   - Gradient checkpointing kullanın
+
+2. **Slow Image Processing**
+   - Image timeout değerini artırın
+   - Image resize threshold ayarlayın
+
+3. **Model Loading Issues**
+   - HuggingFace token'ını kontrol edin
+   - Network bağlantısını doğrulayın
+   - Cache dizinini temizleyin
+
+### Security Best Practices
+
+- Image URL'leri validate edin
+- Base64 boyut limitlerini ayarlayın
+- Rate limiting uygulayın
+- Input sanitization yapın
+
+### Monitoring Metrics
+
+- Response time
+- Memory usage
+- Error rates
+- Image processing success rate
+- Token generation speed

handler.py

@@ -11,8 +11,6 @@ from io import BytesIO
 from PIL import Image
 import requests
 import time
-import threading
-import os
 
 # Import utilities if available
 try:
@@ -30,16 +28,6 @@ except ImportError:
     deepseek_client = None
     print("⚠️ Utils module not found - performance monitoring and DeepSeek integration disabled")
 
-# Import email processor if available
-try:
-    from email_ecg_processor import EmailECGProcessor
-    EMAIL_PROCESSOR_AVAILABLE = True
-    print("📧 Email ECG Processor module found - email processing will be enabled")
-except ImportError:
-    EMAIL_PROCESSOR_AVAILABLE = False
-    EmailECGProcessor = None
-    print("📭 Email ECG Processor module not found - email processing disabled")
-
 
 class EndpointHandler:
     def __init__(self, path=""):
@@ -57,11 +45,6 @@ class EndpointHandler:
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         print(f"🖥️ Running on: {self.device}")
         
-        # Initialize email processor
-        self.email_processor = None
-        self.email_thread = None
-        self._init_email_processor()
-        
         try:
             # First attempt - using pipeline (easiest and most stable way)
             from transformers import pipeline
@@ -124,47 +107,6 @@ class EndpointHandler:
         else:
             self.use_pipeline = True
 
-    def _init_email_processor(self):
-        """Initialize email processor in background thread"""
-        if not EMAIL_PROCESSOR_AVAILABLE:
-            print("📭 Email processor not available - skipping email initialization")
-            return
-        
-        # Check if email configuration is available
-        required_email_vars = ['mail_username', 'mail_pw', 'hf_key']
-        missing_vars = [var for var in required_email_vars if not os.getenv(var)]
-        
-        if missing_vars:
-            print(f"📭 Email processor disabled - missing environment variables: {', '.join(missing_vars)}")
-            return
-        
-        try:
-            print("📧 Initializing email processor...")
-            self.email_processor = EmailECGProcessor()
-            
-            # Start email processor in background thread
-            self.email_thread = threading.Thread(
-                target=self._run_email_processor,
-                daemon=True,
-                name="EmailProcessor"
-            )
-            self.email_thread.start()
-            print("✅ Email processor started successfully in background thread")
-            
-        except Exception as e:
-            print(f"❌ Failed to initialize email processor: {e}")
-            self.email_processor = None
-            self.email_thread = None
-    
-    def _run_email_processor(self):
-        """Run email processor in background thread"""
-        try:
-            if self.email_processor:
-                print("📧 Email processor thread started - checking emails every 5 minutes")
-                self.email_processor.run_email_processor(check_interval=300)
-        except Exception as e:
-            print(f"❌ Email processor thread error: {e}")
-
     def process_image_input(self, image_input):
         """
         Handle both URL and base64 image inputs like a champ!
@@ -287,36 +229,14 @@ class EndpointHandler:
     def health_check(self) -> Dict[str, Any]:
         """Health check endpoint"""
         if UTILS_AVAILABLE:
-            health = create_health_check()
+            return create_health_check()
         else:
-            health = {
+            return {
                 'status': 'healthy',
                 'model': 'PULSE-7B',
                 'timestamp': time.time(),
                 'handler_version': '2.0.0'
             }
-        
-        # Add email processor status
-        if EMAIL_PROCESSOR_AVAILABLE and self.email_processor:
-            health['email_processor'] = {
-                'status': 'running',
-                'thread_alive': self.email_thread.is_alive() if self.email_thread else False,
-                'configuration': 'configured'
-            }
-        elif EMAIL_PROCESSOR_AVAILABLE:
-            health['email_processor'] = {
-                'status': 'available_but_not_configured',
-                'thread_alive': False,
-                'configuration': 'missing_environment_variables'
-            }
-        else:
-            health['email_processor'] = {
-                'status': 'not_available',
-                'thread_alive': False,
-                'configuration': 'module_not_found'
-            }
-        
-        return health
 
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """

requirements.txt

@@ -19,8 +19,4 @@ typing-extensions>=4.0.0
 psutil>=5.8.0
 
 # HuggingFace Inference specific
-huggingface-hub>=0.16.0
-
-# Email processing dependencies
-email-validator>=2.0.0
-chardet>=5.0.0
+huggingface-hub>=0.16.0

test_requests.json

@@ -1,220 +1,220 @@
-{
-  "test_requests": {
-    "image_url_request": {
-      "description": "ECG analizi için image URL ile istek",
-      "request": {
-        "inputs": {
-          "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
-          "image": "https://i.imgur.com/7uuejqO.jpeg"
-        },
-        "parameters": {
-          "max_new_tokens": 512,
-          "temperature": 0.2,
-          "top_p": 0.9,
-          "repetition_penalty": 1.05,
-          "stop": ["</s>"],
-          "return_full_text": false
-        }
-      }
-    },
-    
-    "base64_request": {
-      "description": "ECG analizi için base64 encoded image ile istek",
-      "request": {
-        "inputs": {
-          "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
-          "image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQYGBcUFhYaHSUfGhsjHBYWICwgIyYnKSopGR8tMC0oMCUoKSj/2wBDAQcHBwoIChMKChMoGhYaKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCj/wAARCAABAAEDASIAAhEBAxEB/8QAFQABAQAAAAAAAAAAAAAAAAAAAAv/xAAUEAEAAAAAAAAAAAAAAAAAAAAA/8QAFQEBAQAAAAAAAAAAAAAAAAAAAAX/xAAUEQEAAAAAAAAAAAAAAAAAAAAA/9oADAMBAAIRAxEAPwCdABmX/9k="
-        },
-        "parameters": {
-          "max_new_tokens": 512,
-          "temperature": 0.2,
-          "top_p": 0.9,
-          "repetition_penalty": 1.05,
-          "stop": ["</s>"],
-          "return_full_text": false
-        }
-      }
-    },
-
-    "minimal_url_request": {
-      "description": "Minimal parametrelerle image URL isteği",
-      "request": {
-        "inputs": {
-          "query": "Analyze this ECG image.",
-          "image": "https://i.imgur.com/7uuejqO.jpeg"
-        },
-        "parameters": {
-          "temperature": 0.2
-        }
-      }
-    },
-
-    "minimal_base64_request": {
-      "description": "Minimal parametrelerle base64 isteği",
-      "request": {
-        "inputs": {
-          "query": "Analyze this ECG image.",
-          "image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQYGBcUFhYaHSUfGhsjHBYWICwgIyYnKSopGR8tMC0oMCUoKSj/2wBDAQcHBwoIChMKChMoGhYaKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCj/wAARCAABAAEDASIAAhEBAxEB/8QAFQABAQAAAAAAAAAAAAAAAAAAAAv/xAAUEAEAAAAAAAAAAAAAAAAAAAAA/8QAFQEBAQAAAAAAAAAAAAAAAAAAAAX/xAAUEQEAAAAAAAAAAAAAAAAAAAAA/9oADAMBAAIRAxEAPwCdABmX/9k="
-        },
-        "parameters": {
-          "temperature": 0.2
-        }
-      }
-    },
-
-    "text_only_request": {
-      "description": "Sadece text ile istek (image olmadan)",
-      "request": {
-        "inputs": {
-          "query": "What are the common causes of atrial fibrillation?"
-        },
-        "parameters": {
-          "max_new_tokens": 256,
-          "temperature": 0.3,
-          "top_p": 0.9
-        }
-      }
-    },
-
-    "advanced_parameters_request": {
-      "description": "Gelişmiş parametrelerle image URL isteği",
-      "request": {
-        "inputs": {
-          "query": "Provide detailed ECG analysis including rhythm, rate, intervals, and potential abnormalities.",
-          "image": "https://i.imgur.com/7uuejqO.jpeg"
-        },
-        "parameters": {
-          "max_new_tokens": 1024,
-          "temperature": 0.1,
-          "top_p": 0.95,
-          "repetition_penalty": 1.1,
-          "stop": ["</s>", "\n\n"],
-          "return_full_text": false,
-          "do_sample": true
-        }
-      }
-    },
-
-    "deepseek_enabled_request": {
-      "description": "DeepSeek Türkçe yorum özelliği aktif - Image URL",
-      "request": {
-        "inputs": {
-          "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
-          "image": "https://i.imgur.com/7uuejqO.jpeg"
-        },
-        "parameters": {
-          "max_new_tokens": 512,
-          "temperature": 0.2,
-          "top_p": 0.9,
-          "repetition_penalty": 1.05,
-          "enable_turkish_commentary": true,
-          "deepseek_timeout": 30
-        }
-      }
-    },
-
-    "deepseek_enabled_base64_request": {
-      "description": "DeepSeek Türkçe yorum özelliği aktif - Base64",
-      "request": {
-        "inputs": {
-          "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
-          "image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQYGBcUFhYaHSUfGhsjHBYWICwgIyYnKSopGR8tMC0oMCUoKSj/2wBDAQcHBwoIChMKChMoGhYaKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCj/wAARCAABAAEDASIAAhEBAxEB/8QAFQABAQAAAAAAAAAAAAAAAAAAAAv/xAAUEAEAAAAAAAAAAAAAAAAAAAAA/8QAFQEBAQAAAAAAAAAAAAAAAAAAAAX/xAAUEQEAAAAAAAAAAAAAAAAAAAAA/9oADAMBAAIRAxEAPwCdABmX/9k="
-        },
-        "parameters": {
-          "max_new_tokens": 512,
-          "temperature": 0.2,
-          "enable_turkish_commentary": true,
-          "deepseek_timeout": 25
-        }
-      }
-    },
-
-    "deepseek_disabled_request": {
-      "description": "DeepSeek Türkçe yorum özelliği deaktif",
-      "request": {
-        "inputs": {
-          "query": "Analyze this ECG image briefly.",
-          "image": "https://i.imgur.com/7uuejqO.jpeg"
-        },
-        "parameters": {
-          "temperature": 0.2,
-          "enable_turkish_commentary": false
-        }
-      }
-    }
-  },
-
-  "expected_response_format": {
-    "description": "Beklenen response formatı (DeepSeek aktif)",
-    "format": [
-      {
-        "generated_text": "Answer: This ECG image shows a sinus rhythm with a normal heart rate...",
-        "model": "PULSE-7B",
-        "processing_method": "pipeline",
-        "comment_text": "Bu EKG sonucu normal sinüs ritmi göstermektedir. Kalp atış hızı normaldir ve düzenli bir ritim görülmektedir...",
-        "commentary_model": "deepseek-chat",
-        "commentary_tokens": 85,
-        "commentary_status": "success"
-      }
-    ]
-  },
-
-  "expected_response_format_no_commentary": {
-    "description": "Beklenen response formatı (DeepSeek deaktif)",
-    "format": [
-      {
-        "generated_text": "Answer: This ECG image shows a sinus rhythm with a normal heart rate...",
-        "model": "PULSE-7B",
-        "processing_method": "pipeline"
-      }
-    ]
-  },
-
-  "error_response_format": {
-    "description": "Hata durumunda response formatı",
-    "format": [
-      {
-        "generated_text": "",
-        "error": "Error message here...",
-        "model": "PULSE-7B",
-        "handler": "Ubden® Team Enhanced Handler",
-        "success": false
-      }
-    ]
-  },
-
-  "usage_notes": {
-    "supported_image_formats": [
-      "JPEG", "PNG", "GIF", "BMP", "WebP"
-    ],
-    "supported_input_methods": [
-      "HTTP/HTTPS URL",
-      "Base64 encoded with data URI prefix",
-      "Raw base64 string"
-    ],
-    "parameter_limits": {
-      "max_new_tokens": "1-2048",
-      "temperature": "0.01-2.0",
-      "top_p": "0.01-1.0",
-      "repetition_penalty": "1.0-2.0"
-    },
-    "query_field_alternatives": [
-      "query", "text", "prompt"
-    ],
-    "deepseek_integration": {
-      "enable_turkish_commentary": "true/false (default: true)",
-      "deepseek_timeout": "10-60 seconds (default: 30)",
-      "environment_variable": "deep_key (DeepSeek API key)",
-      "commentary_status_values": [
-        "success", "failed", "unavailable", "api_key_missing", "no_text"
-      ]
-    },
-    "response_fields_with_commentary": [
-      "generated_text", "model", "processing_method",
-      "comment_text", "commentary_model", "commentary_tokens", "commentary_status"
-    ],
-    "response_fields_without_commentary": [
-      "generated_text", "model", "processing_method"
-    ]
-  }
-}
+{
+  "test_requests": {
+    "image_url_request": {
+      "description": "ECG analizi için image URL ile istek",
+      "request": {
+        "inputs": {
+          "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
+          "image": "https://i.imgur.com/7uuejqO.jpeg"
+        },
+        "parameters": {
+          "max_new_tokens": 512,
+          "temperature": 0.2,
+          "top_p": 0.9,
+          "repetition_penalty": 1.05,
+          "stop": ["</s>"],
+          "return_full_text": false
+        }
+      }
+    },
+    
+    "base64_request": {
+      "description": "ECG analizi için base64 encoded image ile istek",
+      "request": {
+        "inputs": {
+          "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
+          "image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQYGBcUFhYaHSUfGhsjHBYWICwgIyYnKSopGR8tMC0oMCUoKSj/2wBDAQcHBwoIChMKChMoGhYaKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCj/wAARCAABAAEDASIAAhEBAxEB/8QAFQABAQAAAAAAAAAAAAAAAAAAAAv/xAAUEAEAAAAAAAAAAAAAAAAAAAAA/8QAFQEBAQAAAAAAAAAAAAAAAAAAAAX/xAAUEQEAAAAAAAAAAAAAAAAAAAAA/9oADAMBAAIRAxEAPwCdABmX/9k="
+        },
+        "parameters": {
+          "max_new_tokens": 512,
+          "temperature": 0.2,
+          "top_p": 0.9,
+          "repetition_penalty": 1.05,
+          "stop": ["</s>"],
+          "return_full_text": false
+        }
+      }
+    },
+
+    "minimal_url_request": {
+      "description": "Minimal parametrelerle image URL isteği",
+      "request": {
+        "inputs": {
+          "query": "Analyze this ECG image.",
+          "image": "https://i.imgur.com/7uuejqO.jpeg"
+        },
+        "parameters": {
+          "temperature": 0.2
+        }
+      }
+    },
+
+    "minimal_base64_request": {
+      "description": "Minimal parametrelerle base64 isteği",
+      "request": {
+        "inputs": {
+          "query": "Analyze this ECG image.",
+          "image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQYGBcUFhYaHSUfGhsjHBYWICwgIyYnKSopGR8tMC0oMCUoKSj/2wBDAQcHBwoIChMKChMoGhYaKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCj/wAARCAABAAEDASIAAhEBAxEB/8QAFQABAQAAAAAAAAAAAAAAAAAAAAv/xAAUEAEAAAAAAAAAAAAAAAAAAAAA/8QAFQEBAQAAAAAAAAAAAAAAAAAAAAX/xAAUEQEAAAAAAAAAAAAAAAAAAAAA/9oADAMBAAIRAxEAPwCdABmX/9k="
+        },
+        "parameters": {
+          "temperature": 0.2
+        }
+      }
+    },
+
+    "text_only_request": {
+      "description": "Sadece text ile istek (image olmadan)",
+      "request": {
+        "inputs": {
+          "query": "What are the common causes of atrial fibrillation?"
+        },
+        "parameters": {
+          "max_new_tokens": 256,
+          "temperature": 0.3,
+          "top_p": 0.9
+        }
+      }
+    },
+
+    "advanced_parameters_request": {
+      "description": "Gelişmiş parametrelerle image URL isteği",
+      "request": {
+        "inputs": {
+          "query": "Provide detailed ECG analysis including rhythm, rate, intervals, and potential abnormalities.",
+          "image": "https://i.imgur.com/7uuejqO.jpeg"
+        },
+        "parameters": {
+          "max_new_tokens": 1024,
+          "temperature": 0.1,
+          "top_p": 0.95,
+          "repetition_penalty": 1.1,
+          "stop": ["</s>", "\n\n"],
+          "return_full_text": false,
+          "do_sample": true
+        }
+      }
+    },
+
+    "deepseek_enabled_request": {
+      "description": "DeepSeek Türkçe yorum özelliği aktif - Image URL",
+      "request": {
+        "inputs": {
+          "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
+          "image": "https://i.imgur.com/7uuejqO.jpeg"
+        },
+        "parameters": {
+          "max_new_tokens": 512,
+          "temperature": 0.2,
+          "top_p": 0.9,
+          "repetition_penalty": 1.05,
+          "enable_turkish_commentary": true,
+          "deepseek_timeout": 30
+        }
+      }
+    },
+
+    "deepseek_enabled_base64_request": {
+      "description": "DeepSeek Türkçe yorum özelliği aktif - Base64",
+      "request": {
+        "inputs": {
+          "query": "What are the main features and diagnosis in this ECG image? Provide a concise, clinical answer.",
+          "image": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAYEBQYFBAYGBQYHBwYIChAKCgkJChQODwwQFxQYGBcUFhYaHSUfGhsjHBYWICwgIyYnKSopGR8tMC0oMCUoKSj/2wBDAQcHBwoIChMKChMoGhYaKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCj/wAARCAABAAEDASIAAhEBAxEB/8QAFQABAQAAAAAAAAAAAAAAAAAAAAv/xAAUEAEAAAAAAAAAAAAAAAAAAAAA/8QAFQEBAQAAAAAAAAAAAAAAAAAAAAX/xAAUEQEAAAAAAAAAAAAAAAAAAAAA/9oADAMBAAIRAxEAPwCdABmX/9k="
+        },
+        "parameters": {
+          "max_new_tokens": 512,
+          "temperature": 0.2,
+          "enable_turkish_commentary": true,
+          "deepseek_timeout": 25
+        }
+      }
+    },
+
+    "deepseek_disabled_request": {
+      "description": "DeepSeek Türkçe yorum özelliği deaktif",
+      "request": {
+        "inputs": {
+          "query": "Analyze this ECG image briefly.",
+          "image": "https://i.imgur.com/7uuejqO.jpeg"
+        },
+        "parameters": {
+          "temperature": 0.2,
+          "enable_turkish_commentary": false
+        }
+      }
+    }
+  },
+
+  "expected_response_format": {
+    "description": "Beklenen response formatı (DeepSeek aktif)",
+    "format": [
+      {
+        "generated_text": "Answer: This ECG image shows a sinus rhythm with a normal heart rate...",
+        "model": "PULSE-7B",
+        "processing_method": "pipeline",
+        "comment_text": "Bu EKG sonucu normal sinüs ritmi göstermektedir. Kalp atış hızı normaldir ve düzenli bir ritim görülmektedir...",
+        "commentary_model": "deepseek-chat",
+        "commentary_tokens": 85,
+        "commentary_status": "success"
+      }
+    ]
+  },
+
+  "expected_response_format_no_commentary": {
+    "description": "Beklenen response formatı (DeepSeek deaktif)",
+    "format": [
+      {
+        "generated_text": "Answer: This ECG image shows a sinus rhythm with a normal heart rate...",
+        "model": "PULSE-7B",
+        "processing_method": "pipeline"
+      }
+    ]
+  },
+
+  "error_response_format": {
+    "description": "Hata durumunda response formatı",
+    "format": [
+      {
+        "generated_text": "",
+        "error": "Error message here...",
+        "model": "PULSE-7B",
+        "handler": "Ubden® Team Enhanced Handler",
+        "success": false
+      }
+    ]
+  },
+
+  "usage_notes": {
+    "supported_image_formats": [
+      "JPEG", "PNG", "GIF", "BMP", "WebP"
+    ],
+    "supported_input_methods": [
+      "HTTP/HTTPS URL",
+      "Base64 encoded with data URI prefix",
+      "Raw base64 string"
+    ],
+    "parameter_limits": {
+      "max_new_tokens": "1-2048",
+      "temperature": "0.01-2.0",
+      "top_p": "0.01-1.0",
+      "repetition_penalty": "1.0-2.0"
+    },
+    "query_field_alternatives": [
+      "query", "text", "prompt"
+    ],
+    "deepseek_integration": {
+      "enable_turkish_commentary": "true/false (default: true)",
+      "deepseek_timeout": "10-60 seconds (default: 30)",
+      "environment_variable": "deep_key (DeepSeek API key)",
+      "commentary_status_values": [
+        "success", "failed", "unavailable", "api_key_missing", "no_text"
+      ]
+    },
+    "response_fields_with_commentary": [
+      "generated_text", "model", "processing_method",
+      "comment_text", "commentary_model", "commentary_tokens", "commentary_status"
+    ],
+    "response_fields_without_commentary": [
+      "generated_text", "model", "processing_method"
+    ]
+  }
+}

utils.py

@@ -1,360 +1,360 @@
-"""
-PULSE-7B Handler Utilities
-Ubden® Team - Performance monitoring and helper functions
-"""
-
-import time
-import torch
-import psutil
-import logging
-import os
-import json
-import requests
-from typing import Dict, Any, Optional
-from functools import wraps
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-class PerformanceMonitor:
-    """Performance monitoring utilities for PULSE-7B handler"""
-    
-    def __init__(self):
-        self.metrics = {
-            'total_requests': 0,
-            'successful_requests': 0,
-            'failed_requests': 0,
-            'image_url_requests': 0,
-            'base64_requests': 0,
-            'text_only_requests': 0,
-            'total_generation_time': 0.0,
-            'total_image_processing_time': 0.0
-        }
-    
-    def log_request(self, request_type: str, success: bool, 
-                   generation_time: float = 0.0, 
-                   image_processing_time: float = 0.0):
-        """Log request metrics"""
-        self.metrics['total_requests'] += 1
-        
-        if success:
-            self.metrics['successful_requests'] += 1
-        else:
-            self.metrics['failed_requests'] += 1
-        
-        if request_type == 'image_url':
-            self.metrics['image_url_requests'] += 1
-        elif request_type == 'base64':
-            self.metrics['base64_requests'] += 1
-        else:
-            self.metrics['text_only_requests'] += 1
-        
-        self.metrics['total_generation_time'] += generation_time
-        self.metrics['total_image_processing_time'] += image_processing_time
-    
-    def get_stats(self) -> Dict[str, Any]:
-        """Get current performance statistics"""
-        total_requests = self.metrics['total_requests']
-        if total_requests == 0:
-            return self.metrics
-        
-        success_rate = (self.metrics['successful_requests'] / total_requests) * 100
-        avg_generation_time = self.metrics['total_generation_time'] / total_requests
-        avg_image_processing_time = self.metrics['total_image_processing_time'] / max(
-            self.metrics['image_url_requests'] + self.metrics['base64_requests'], 1
-        )
-        
-        return {
-            **self.metrics,
-            'success_rate_percent': round(success_rate, 2),
-            'avg_generation_time_seconds': round(avg_generation_time, 3),
-            'avg_image_processing_time_seconds': round(avg_image_processing_time, 3)
-        }
-    
-    def reset_stats(self):
-        """Reset all metrics"""
-        for key in self.metrics:
-            self.metrics[key] = 0 if isinstance(self.metrics[key], int) else 0.0
-
-def timing_decorator(func):
-    """Decorator to measure function execution time"""
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        start_time = time.time()
-        try:
-            result = func(*args, **kwargs)
-            execution_time = time.time() - start_time
-            logger.info(f"{func.__name__} completed in {execution_time:.3f}s")
-            return result, execution_time
-        except Exception as e:
-            execution_time = time.time() - start_time
-            logger.error(f"{func.__name__} failed in {execution_time:.3f}s: {e}")
-            raise e
-    return wrapper
-
-def get_system_info() -> Dict[str, Any]:
-    """Get current system resource information"""
-    cpu_percent = psutil.cpu_percent(interval=1)
-    memory = psutil.virtual_memory()
-    
-    system_info = {
-        'cpu_usage_percent': cpu_percent,
-        'memory_total_gb': round(memory.total / (1024**3), 2),
-        'memory_used_gb': round(memory.used / (1024**3), 2),
-        'memory_available_gb': round(memory.available / (1024**3), 2),
-        'memory_usage_percent': memory.percent
-    }
-    
-    # Add GPU info if available
-    if torch.cuda.is_available():
-        gpu_memory = torch.cuda.memory_stats()
-        system_info.update({
-            'gpu_available': True,
-            'gpu_memory_allocated_gb': round(
-                torch.cuda.memory_allocated() / (1024**3), 2
-            ),
-            'gpu_memory_reserved_gb': round(
-                torch.cuda.memory_reserved() / (1024**3), 2
-            ),
-            'gpu_device_name': torch.cuda.get_device_name(0)
-        })
-    else:
-        system_info['gpu_available'] = False
-    
-    return system_info
-
-def validate_image_input(image_input: str) -> Dict[str, Any]:
-    """Validate image input and return metadata"""
-    if not image_input or not isinstance(image_input, str):
-        return {'valid': False, 'type': None, 'error': 'Invalid input type'}
-    
-    # Check if URL
-    if image_input.startswith(('http://', 'https://')):
-        return {
-            'valid': True,
-            'type': 'url',
-            'length': len(image_input),
-            'domain': image_input.split('/')[2] if '/' in image_input else 'unknown'
-        }
-    
-    # Check if base64
-    elif image_input.startswith('data:image/') or len(image_input) > 100:
-        is_data_url = image_input.startswith('data:')
-        base64_data = image_input
-        
-        if is_data_url:
-            if 'base64,' in image_input:
-                base64_data = image_input.split('base64,')[1]
-            else:
-                return {'valid': False, 'type': 'base64', 'error': 'Invalid data URL format'}
-        
-        # Estimate decoded size
-        estimated_size = len(base64_data) * 3 // 4
-        
-        return {
-            'valid': True,
-            'type': 'base64',
-            'is_data_url': is_data_url,
-            'base64_length': len(base64_data),
-            'estimated_size_bytes': estimated_size,
-            'estimated_size_kb': round(estimated_size / 1024, 2)
-        }
-    
-    return {'valid': False, 'type': None, 'error': 'Unrecognized format'}
-
-def sanitize_parameters(parameters: Dict[str, Any]) -> Dict[str, Any]:
-    """Sanitize and validate generation parameters"""
-    sanitized = {}
-    
-    # Max new tokens
-    max_new_tokens = parameters.get('max_new_tokens', 512)
-    sanitized['max_new_tokens'] = max(1, min(max_new_tokens, 2048))
-    
-    # Temperature
-    temperature = parameters.get('temperature', 0.2)
-    sanitized['temperature'] = max(0.01, min(temperature, 2.0))
-    
-    # Top-p
-    top_p = parameters.get('top_p', 0.9)
-    sanitized['top_p'] = max(0.01, min(top_p, 1.0))
-    
-    # Repetition penalty
-    repetition_penalty = parameters.get('repetition_penalty', 1.05)
-    sanitized['repetition_penalty'] = max(1.0, min(repetition_penalty, 2.0))
-    
-    # Stop sequences
-    stop = parameters.get('stop', ['</s>'])
-    if isinstance(stop, str):
-        stop = [stop]
-    sanitized['stop'] = stop[:5]  # Limit to 5 stop sequences
-    
-    # Return full text
-    sanitized['return_full_text'] = bool(parameters.get('return_full_text', False))
-    
-    # Do sample
-    sanitized['do_sample'] = bool(parameters.get('do_sample', sanitized['temperature'] > 0.01))
-    
-    return sanitized
-
-def create_health_check() -> Dict[str, Any]:
-    """Create a health check response"""
-    try:
-        system_info = get_system_info()
-        
-        health_status = {
-            'status': 'healthy',
-            'timestamp': time.time(),
-            'system': system_info,
-            'model': 'PULSE-7B',
-            'handler_version': '2.0.0',
-            'features': [
-                'image_url_support',
-                'base64_image_support',
-                'stop_sequences',
-                'parameter_validation',
-                'performance_monitoring'
-            ]
-        }
-        
-        # Check if system is under stress
-        if system_info['memory_usage_percent'] > 90:
-            health_status['warnings'] = ['High memory usage']
-        
-        if system_info['cpu_usage_percent'] > 90:
-            health_status.setdefault('warnings', []).append('High CPU usage')
-        
-        return health_status
-        
-    except Exception as e:
-        return {
-            'status': 'unhealthy',
-            'timestamp': time.time(),
-            'error': str(e)
-        }
-
-class DeepSeekClient:
-    """DeepSeek API client for Turkish commentary"""
-    
-    def __init__(self, api_key: Optional[str] = None):
-        self.api_key = api_key or os.getenv('deep_key') or os.getenv('DEEPSEEK_API_KEY')
-        self.base_url = "https://api.deepseek.com/v1/chat/completions"
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}" if self.api_key else ""
-        }
-        
-    def is_available(self) -> bool:
-        """Check if DeepSeek API is available"""
-        return bool(self.api_key)
-    
-    def get_turkish_commentary(self, english_analysis: str, timeout: int = 30) -> Dict[str, Any]:
-        """
-        Get Turkish commentary for English medical analysis
-        
-        Args:
-            english_analysis: English medical analysis text
-            timeout: Request timeout in seconds
-            
-        Returns:
-            Dict with success status and commentary
-        """
-        if not self.is_available():
-            return {
-                "success": False,
-                "error": "DeepSeek API key not configured",
-                "comment_text": ""
-            }
-        
-        try:
-            # Prepare the prompt for Turkish medical commentary
-            prompt = f"""Bu bir EKG sonucu klinik incelemesi. Aşağıdaki İngilizce medikal analizi Türkçe olarak yorumla ve hasta için anlaşılır bir dilde açıkla:
-
-"{english_analysis}"
-
-Lütfen:
-1. Medikal terimleri Türkçe karşılıklarıyla açıkla
-2. Hastanın anlayabileceği basit bir dille yaz
-3. Gerekirse aciliyet durumu hakkında bilgi ver
-4. Kısa ve net ol
-
-Türkçe Yorum:"""
-
-            payload = {
-                "model": "deepseek-chat",
-                "messages": [
-                    {
-                        "role": "system", 
-                        "content": "Sen deneyimli bir kardiyolog doktorsun. EKG sonuçlarını Türkçe olarak hastalar için anlaşılır şekilde açıklıyorsun."
-                    },
-                    {
-                        "role": "user", 
-                        "content": prompt
-                    }
-                ],
-                "temperature": 0.3,
-                "max_tokens": 500,
-                "stream": False
-            }
-            
-            logger.info("🔄 DeepSeek API'ye Türkçe yorum için istek gönderiliyor...")
-            
-            response = requests.post(
-                self.base_url,
-                headers=self.headers,
-                json=payload,
-                timeout=timeout
-            )
-            
-            response.raise_for_status()
-            result = response.json()
-            
-            if 'choices' in result and len(result['choices']) > 0:
-                comment_text = result['choices'][0]['message']['content'].strip()
-                
-                # Clean up the response - remove "Türkçe Yorum:" prefix if present
-                if comment_text.startswith("Türkçe Yorum:"):
-                    comment_text = comment_text[13:].strip()
-                
-                logger.info("✅ DeepSeek'ten Türkçe yorum başarıyla alındı")
-                
-                return {
-                    "success": True,
-                    "comment_text": comment_text,
-                    "model": "deepseek-chat",
-                    "tokens_used": result.get('usage', {}).get('total_tokens', 0)
-                }
-            else:
-                return {
-                    "success": False,
-                    "error": "DeepSeek API'den geçersiz yanıt",
-                    "comment_text": ""
-                }
-                
-        except requests.exceptions.Timeout:
-            logger.error("❌ DeepSeek API timeout")
-            return {
-                "success": False,
-                "error": "DeepSeek API timeout - istek zaman aşımına uğradı",
-                "comment_text": ""
-            }
-            
-        except requests.exceptions.RequestException as e:
-            logger.error(f"❌ DeepSeek API request error: {e}")
-            return {
-                "success": False,
-                "error": f"DeepSeek API bağlantı hatası: {str(e)}",
-                "comment_text": ""
-            }
-            
-        except Exception as e:
-            logger.error(f"❌ DeepSeek API unexpected error: {e}")
-            return {
-                "success": False,
-                "error": f"DeepSeek API beklenmeyen hata: {str(e)}",
-                "comment_text": ""
-            }
-
-# Global instances
-performance_monitor = PerformanceMonitor()
-deepseek_client = DeepSeekClient()
+"""
+PULSE-7B Handler Utilities
+Ubden® Team - Performance monitoring and helper functions
+"""
+
+import time
+import torch
+import psutil
+import logging
+import os
+import json
+import requests
+from typing import Dict, Any, Optional
+from functools import wraps
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class PerformanceMonitor:
+    """Performance monitoring utilities for PULSE-7B handler"""
+    
+    def __init__(self):
+        self.metrics = {
+            'total_requests': 0,
+            'successful_requests': 0,
+            'failed_requests': 0,
+            'image_url_requests': 0,
+            'base64_requests': 0,
+            'text_only_requests': 0,
+            'total_generation_time': 0.0,
+            'total_image_processing_time': 0.0
+        }
+    
+    def log_request(self, request_type: str, success: bool, 
+                   generation_time: float = 0.0, 
+                   image_processing_time: float = 0.0):
+        """Log request metrics"""
+        self.metrics['total_requests'] += 1
+        
+        if success:
+            self.metrics['successful_requests'] += 1
+        else:
+            self.metrics['failed_requests'] += 1
+        
+        if request_type == 'image_url':
+            self.metrics['image_url_requests'] += 1
+        elif request_type == 'base64':
+            self.metrics['base64_requests'] += 1
+        else:
+            self.metrics['text_only_requests'] += 1
+        
+        self.metrics['total_generation_time'] += generation_time
+        self.metrics['total_image_processing_time'] += image_processing_time
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """Get current performance statistics"""
+        total_requests = self.metrics['total_requests']
+        if total_requests == 0:
+            return self.metrics
+        
+        success_rate = (self.metrics['successful_requests'] / total_requests) * 100
+        avg_generation_time = self.metrics['total_generation_time'] / total_requests
+        avg_image_processing_time = self.metrics['total_image_processing_time'] / max(
+            self.metrics['image_url_requests'] + self.metrics['base64_requests'], 1
+        )
+        
+        return {
+            **self.metrics,
+            'success_rate_percent': round(success_rate, 2),
+            'avg_generation_time_seconds': round(avg_generation_time, 3),
+            'avg_image_processing_time_seconds': round(avg_image_processing_time, 3)
+        }
+    
+    def reset_stats(self):
+        """Reset all metrics"""
+        for key in self.metrics:
+            self.metrics[key] = 0 if isinstance(self.metrics[key], int) else 0.0
+
+def timing_decorator(func):
+    """Decorator to measure function execution time"""
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        start_time = time.time()
+        try:
+            result = func(*args, **kwargs)
+            execution_time = time.time() - start_time
+            logger.info(f"{func.__name__} completed in {execution_time:.3f}s")
+            return result, execution_time
+        except Exception as e:
+            execution_time = time.time() - start_time
+            logger.error(f"{func.__name__} failed in {execution_time:.3f}s: {e}")
+            raise e
+    return wrapper
+
+def get_system_info() -> Dict[str, Any]:
+    """Get current system resource information"""
+    cpu_percent = psutil.cpu_percent(interval=1)
+    memory = psutil.virtual_memory()
+    
+    system_info = {
+        'cpu_usage_percent': cpu_percent,
+        'memory_total_gb': round(memory.total / (1024**3), 2),
+        'memory_used_gb': round(memory.used / (1024**3), 2),
+        'memory_available_gb': round(memory.available / (1024**3), 2),
+        'memory_usage_percent': memory.percent
+    }
+    
+    # Add GPU info if available
+    if torch.cuda.is_available():
+        gpu_memory = torch.cuda.memory_stats()
+        system_info.update({
+            'gpu_available': True,
+            'gpu_memory_allocated_gb': round(
+                torch.cuda.memory_allocated() / (1024**3), 2
+            ),
+            'gpu_memory_reserved_gb': round(
+                torch.cuda.memory_reserved() / (1024**3), 2
+            ),
+            'gpu_device_name': torch.cuda.get_device_name(0)
+        })
+    else:
+        system_info['gpu_available'] = False
+    
+    return system_info
+
+def validate_image_input(image_input: str) -> Dict[str, Any]:
+    """Validate image input and return metadata"""
+    if not image_input or not isinstance(image_input, str):
+        return {'valid': False, 'type': None, 'error': 'Invalid input type'}
+    
+    # Check if URL
+    if image_input.startswith(('http://', 'https://')):
+        return {
+            'valid': True,
+            'type': 'url',
+            'length': len(image_input),
+            'domain': image_input.split('/')[2] if '/' in image_input else 'unknown'
+        }
+    
+    # Check if base64
+    elif image_input.startswith('data:image/') or len(image_input) > 100:
+        is_data_url = image_input.startswith('data:')
+        base64_data = image_input
+        
+        if is_data_url:
+            if 'base64,' in image_input:
+                base64_data = image_input.split('base64,')[1]
+            else:
+                return {'valid': False, 'type': 'base64', 'error': 'Invalid data URL format'}
+        
+        # Estimate decoded size
+        estimated_size = len(base64_data) * 3 // 4
+        
+        return {
+            'valid': True,
+            'type': 'base64',
+            'is_data_url': is_data_url,
+            'base64_length': len(base64_data),
+            'estimated_size_bytes': estimated_size,
+            'estimated_size_kb': round(estimated_size / 1024, 2)
+        }
+    
+    return {'valid': False, 'type': None, 'error': 'Unrecognized format'}
+
+def sanitize_parameters(parameters: Dict[str, Any]) -> Dict[str, Any]:
+    """Sanitize and validate generation parameters"""
+    sanitized = {}
+    
+    # Max new tokens
+    max_new_tokens = parameters.get('max_new_tokens', 512)
+    sanitized['max_new_tokens'] = max(1, min(max_new_tokens, 2048))
+    
+    # Temperature
+    temperature = parameters.get('temperature', 0.2)
+    sanitized['temperature'] = max(0.01, min(temperature, 2.0))
+    
+    # Top-p
+    top_p = parameters.get('top_p', 0.9)
+    sanitized['top_p'] = max(0.01, min(top_p, 1.0))
+    
+    # Repetition penalty
+    repetition_penalty = parameters.get('repetition_penalty', 1.05)
+    sanitized['repetition_penalty'] = max(1.0, min(repetition_penalty, 2.0))
+    
+    # Stop sequences
+    stop = parameters.get('stop', ['</s>'])
+    if isinstance(stop, str):
+        stop = [stop]
+    sanitized['stop'] = stop[:5]  # Limit to 5 stop sequences
+    
+    # Return full text
+    sanitized['return_full_text'] = bool(parameters.get('return_full_text', False))
+    
+    # Do sample
+    sanitized['do_sample'] = bool(parameters.get('do_sample', sanitized['temperature'] > 0.01))
+    
+    return sanitized
+
+def create_health_check() -> Dict[str, Any]:
+    """Create a health check response"""
+    try:
+        system_info = get_system_info()
+        
+        health_status = {
+            'status': 'healthy',
+            'timestamp': time.time(),
+            'system': system_info,
+            'model': 'PULSE-7B',
+            'handler_version': '2.0.0',
+            'features': [
+                'image_url_support',
+                'base64_image_support',
+                'stop_sequences',
+                'parameter_validation',
+                'performance_monitoring'
+            ]
+        }
+        
+        # Check if system is under stress
+        if system_info['memory_usage_percent'] > 90:
+            health_status['warnings'] = ['High memory usage']
+        
+        if system_info['cpu_usage_percent'] > 90:
+            health_status.setdefault('warnings', []).append('High CPU usage')
+        
+        return health_status
+        
+    except Exception as e:
+        return {
+            'status': 'unhealthy',
+            'timestamp': time.time(),
+            'error': str(e)
+        }
+
+class DeepSeekClient:
+    """DeepSeek API client for Turkish commentary"""
+    
+    def __init__(self, api_key: Optional[str] = None):
+        self.api_key = api_key or os.getenv('deep_key') or os.getenv('DEEPSEEK_API_KEY')
+        self.base_url = "https://api.deepseek.com/v1/chat/completions"
+        self.headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}" if self.api_key else ""
+        }
+        
+    def is_available(self) -> bool:
+        """Check if DeepSeek API is available"""
+        return bool(self.api_key)
+    
+    def get_turkish_commentary(self, english_analysis: str, timeout: int = 30) -> Dict[str, Any]:
+        """
+        Get Turkish commentary for English medical analysis
+        
+        Args:
+            english_analysis: English medical analysis text
+            timeout: Request timeout in seconds
+            
+        Returns:
+            Dict with success status and commentary
+        """
+        if not self.is_available():
+            return {
+                "success": False,
+                "error": "DeepSeek API key not configured",
+                "comment_text": ""
+            }
+        
+        try:
+            # Prepare the prompt for Turkish medical commentary
+            prompt = f"""Bu bir EKG sonucu klinik incelemesi. Aşağıdaki İngilizce medikal analizi Türkçe olarak yorumla ve hasta için anlaşılır bir dilde açıkla:
+
+"{english_analysis}"
+
+Lütfen:
+1. Medikal terimleri Türkçe karşılıklarıyla açıkla
+2. Hastanın anlayabileceği basit bir dille yaz
+3. Gerekirse aciliyet durumu hakkında bilgi ver
+4. Kısa ve net ol
+
+Türkçe Yorum:"""
+
+            payload = {
+                "model": "deepseek-chat",
+                "messages": [
+                    {
+                        "role": "system", 
+                        "content": "Sen deneyimli bir kardiyolog doktorsun. EKG sonuçlarını Türkçe olarak hastalar için anlaşılır şekilde açıklıyorsun."
+                    },
+                    {
+                        "role": "user", 
+                        "content": prompt
+                    }
+                ],
+                "temperature": 0.3,
+                "max_tokens": 500,
+                "stream": False
+            }
+            
+            logger.info("🔄 DeepSeek API'ye Türkçe yorum için istek gönderiliyor...")
+            
+            response = requests.post(
+                self.base_url,
+                headers=self.headers,
+                json=payload,
+                timeout=timeout
+            )
+            
+            response.raise_for_status()
+            result = response.json()
+            
+            if 'choices' in result and len(result['choices']) > 0:
+                comment_text = result['choices'][0]['message']['content'].strip()
+                
+                # Clean up the response - remove "Türkçe Yorum:" prefix if present
+                if comment_text.startswith("Türkçe Yorum:"):
+                    comment_text = comment_text[13:].strip()
+                
+                logger.info("✅ DeepSeek'ten Türkçe yorum başarıyla alındı")
+                
+                return {
+                    "success": True,
+                    "comment_text": comment_text,
+                    "model": "deepseek-chat",
+                    "tokens_used": result.get('usage', {}).get('total_tokens', 0)
+                }
+            else:
+                return {
+                    "success": False,
+                    "error": "DeepSeek API'den geçersiz yanıt",
+                    "comment_text": ""
+                }
+                
+        except requests.exceptions.Timeout:
+            logger.error("❌ DeepSeek API timeout")
+            return {
+                "success": False,
+                "error": "DeepSeek API timeout - istek zaman aşımına uğradı",
+                "comment_text": ""
+            }
+            
+        except requests.exceptions.RequestException as e:
+            logger.error(f"❌ DeepSeek API request error: {e}")
+            return {
+                "success": False,
+                "error": f"DeepSeek API bağlantı hatası: {str(e)}",
+                "comment_text": ""
+            }
+            
+        except Exception as e:
+            logger.error(f"❌ DeepSeek API unexpected error: {e}")
+            return {
+                "success": False,
+                "error": f"DeepSeek API beklenmeyen hata: {str(e)}",
+                "comment_text": ""
+            }
+
+# Global instances
+performance_monitor = PerformanceMonitor()
+deepseek_client = DeepSeekClient()