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model_options.py

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+# model_options.py
+"""
+Centralized model catalog for Glossarion UIs.
+Returned list should mirror the main GUI model dropdown.
+"""
+from typing import List
+
+def get_model_options() -> List[str]:
+    return [
+    
+        # OpenAI Models
+        "gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4.1-nano", "gpt-4.1-mini", "gpt-4.1",
+        "gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4", "gpt-4-32k",
+        "gpt-5-mini","gpt-5","gpt-5-nano",
+        "o1-preview", "o1-mini", "o3", "o4-mini",
+        
+        # Google Gemini Models
+        "gemini-2.0-flash","gemini-2.0-flash-lite",
+        "gemini-2.5-flash","gemini-2.5-flash-lite", "gemini-2.5-pro", "gemini-pro", "gemini-pro-vision",
+        
+        # Anthropic Claude Models
+        "claude-opus-4-20250514", "claude-sonnet-4-20250514",
+        "claude-3-5-sonnet-20241022", "claude-3-7-sonnet-20250219",
+        "claude-3-opus-20240229", "claude-3-sonnet-20240229", "claude-3-haiku-20240307",
+        "claude-2.1", "claude-2", "claude-instant-1.2",
+        
+        # Grok Models
+        "grok-4-0709", "grok-4-fast", "grok-4-fast-reasoning",  "grok-4-fast-reasoning-latest", "grok-3", "grok-3-mini",
+        
+        # Vertex AI Model Garden - Claude models (confirmed)
+        "claude-4-opus@20250514",
+        "claude-4-sonnet@20250514",
+        "claude-opus-4@20250514",
+        "claude-sonnet-4@20250514",
+        "claude-3-7-sonnet@20250219",
+        "claude-3-5-sonnet@20240620",
+        "claude-3-5-sonnet-v2@20241022",
+        "claude-3-opus@20240229",
+        "claude-3-sonnet@20240229",
+        "claude-3-haiku@20240307",
+
+        
+        # Alternative format with vertex_ai prefix
+        "vertex/claude-3-7-sonnet@20250219",
+        "vertex/claude-3-5-sonnet@20240620",
+        "vertex/claude-3-opus@20240229",
+        "vertex/claude-4-opus@20250514",
+        "vertex/claude-4-sonnet@20250514",
+        "vertex/gemini-1.5-pro",
+        "vertex/gemini-1.5-flash",
+        "vertex/gemini-2.0-flash",
+        "vertex/gemini-2.5-pro",
+        "vertex/gemini-2.5-flash",
+        "vertex/gemini-2.5-flash-lite",
+
+        # Chute AI
+        "chutes/openai/gpt-oss-120b",
+        "chutes/deepseek-ai/DeepSeek-V3.1",
+        
+        # DeepSeek Models
+        "deepseek-chat", "deepseek-coder", "deepseek-coder-33b-instruct",
+        
+        # Mistral Models
+        "mistral-large", "mistral-medium", "mistral-small", "mistral-tiny",
+        "mixtral-8x7b-instruct", "mixtral-8x22b", "codestral-latest",
+        
+        # Meta Llama Models (via Together/other providers)
+        "llama-2-7b-chat", "llama-2-13b-chat", "llama-2-70b-chat",
+        "llama-3-8b-instruct", "llama-3-70b-instruct", "codellama-34b-instruct",
+        
+        # Yi Models
+        "yi-34b-chat", "yi-34b-chat-200k", "yi-6b-chat",
+        
+        # Qwen Models
+        "qwen-72b-chat", "qwen-14b-chat", "qwen-7b-chat", "qwen-plus", "qwen-turbo",
+        
+        # Cohere Models
+        "command", "command-light", "command-nightly", "command-r", "command-r-plus",
+        
+        # AI21 Models
+        "j2-ultra", "j2-mid", "j2-light", "jamba-instruct",
+        
+        # Perplexity Models
+        "perplexity-70b-online", "perplexity-7b-online", "pplx-70b-online", "pplx-7b-online",
+        
+        # Groq Models (usually with suffix)
+        "llama-3-70b-groq", "llama-3-8b-groq", "mixtral-8x7b-groq",
+        
+        # Chinese Models
+        "glm-4", "glm-3-turbo", "chatglm-6b", "chatglm2-6b", "chatglm3-6b",
+        "baichuan-13b-chat", "baichuan2-13b-chat",
+        "moonshot-v1-8k", "moonshot-v1-32k", "moonshot-v1-128k",
+        
+        # Other Models
+        "falcon-40b-instruct", "falcon-7b-instruct",
+        "phi-2", "phi-3-mini", "phi-3-small", "phi-3-medium",
+        "orca-2-13b", "orca-2-7b",
+        "vicuna-13b", "vicuna-7b",
+        "alpaca-7b",
+        "wizardlm-70b", "wizardlm-13b",
+        "openchat-3.5",
+        
+        # For POE, prefix with 'poe/'
+        "poe/gpt-4", "poe/gpt-4o", "poe/gpt-4.5", "poe/gpt-4.1",
+        "poe/claude-3-opus", "poe/claude-4-opus", "poe/claude-3-sonnet", "poe/claude-4-sonnet",
+        "poe/claude", "poe/Assistant",
+        "poe/gemini-2.5-flash", "poe/gemini-2.5-pro",
+        
+        # For OR, prevfix with 'or/'
+        "or/google/gemini-2.5-pro",
+        "or/google/gemini-2.5-flash",
+        "or/google/gemini-2.5-flash-lite",
+        "or/openai/gpt-5",
+        "or/openai/gpt-5-mini",
+        "or/openai/gpt-5-nano",
+        "or/openai/chatgpt-4o-latest",   
+        "or/deepseek/deepseek-r1-0528:free", 
+        "or/google/gemma-3-27b-it:free",
+        
+        # For ElectronHub, prefix with 'eh/'
+        "eh/gpt-4", "eh/gpt-3.5-turbo", "eh/claude-3-opus", "eh/claude-3-sonnet",
+        "eh/llama-2-70b-chat", "eh/yi-34b-chat-200k", "eh/mistral-large",
+        "eh/gemini-pro", "eh/deepseek-coder-33b",
+        
+        # Last Resort
+        "deepl",  # Will use DeepL API
+        "google-translate-free",  # Uses free web endpoint (no key)
+        "google-translate",  # Will use Google Cloud Translate
+    ]

ocr_manager.py

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+# ocr_manager.py
+"""
+OCR Manager for handling multiple OCR providers
+Handles installation, model downloading, and OCR processing
+Updated with HuggingFace donut model and proper bubble detection integration
+"""
+import os
+import sys
+import cv2
+import json
+import subprocess
+import threading
+import traceback
+from typing import List, Dict, Optional, Tuple, Any
+import numpy as np
+from dataclasses import dataclass
+from PIL import Image
+import logging
+import time
+import random
+import base64
+import io
+import requests
+
+try:
+    import gptqmodel
+    HAS_GPTQ = True
+except ImportError:
+    try:
+        import auto_gptq
+        HAS_GPTQ = True
+    except ImportError:
+        HAS_GPTQ = False
+
+try:
+    import optimum
+    HAS_OPTIMUM = True
+except ImportError:
+    HAS_OPTIMUM = False
+
+try:
+    import accelerate
+    HAS_ACCELERATE = True
+except ImportError:
+    HAS_ACCELERATE = False
+
+logger = logging.getLogger(__name__)
+
+@dataclass
+class OCRResult:
+    """Unified OCR result format with built-in sanitization to prevent data corruption."""
+    text: str
+    bbox: Tuple[int, int, int, int]  # x, y, w, h
+    confidence: float
+    vertices: Optional[List[Tuple[int, int]]] = None
+
+    def __post_init__(self):
+        """
+        This special method is called automatically after the object is created.
+        It acts as a final safeguard to ensure the 'text' attribute is ALWAYS a clean string.
+        """
+        # --- THIS IS THE DEFINITIVE FIX ---
+        # If the text we received is a tuple, we extract the first element.
+        # This makes it impossible for a tuple to exist in a finished object.
+        if isinstance(self.text, tuple):
+            # Log that we are fixing a critical data error.
+            print(f"CRITICAL WARNING: Corrupted tuple detected in OCRResult. Sanitizing '{self.text}' to '{self.text[0]}'.")
+            self.text = self.text[0]
+        
+        # Ensure the final result is always a stripped string.
+        self.text = str(self.text).strip()
+    
+class OCRProvider:
+    """Base class for OCR providers"""
+    
+    def __init__(self, log_callback=None):
+        # Set thread limits early if environment indicates single-threaded mode
+        try:
+            if os.environ.get('OMP_NUM_THREADS') == '1':
+                # Already in single-threaded mode, ensure it's applied to this process
+                try:
+                    import sys
+                    if 'torch' in sys.modules:
+                        import torch
+                        torch.set_num_threads(1)
+                except (ImportError, RuntimeError, AttributeError):
+                    pass
+                try:
+                    import cv2
+                    cv2.setNumThreads(1)
+                except (ImportError, AttributeError):
+                    pass
+        except Exception:
+            pass
+        
+        self.log_callback = log_callback
+        self.is_installed = False
+        self.is_loaded = False
+        self.model = None
+        self.stop_flag = None
+        self._stopped = False
+        
+    def _log(self, message: str, level: str = "info"):
+        """Log message with stop suppression"""
+        # Suppress logs when stopped (allow only essential stop confirmation messages)
+        if self._check_stop():
+            essential_stop_keywords = [
+                "⏹️ Translation stopped by user",
+                "⏹️ OCR processing stopped",
+                "cleanup", "🧹"
+            ]
+            if not any(keyword in message for keyword in essential_stop_keywords):
+                return
+        
+        if self.log_callback:
+            self.log_callback(message, level)
+        else:
+            print(f"[{level.upper()}] {message}")
+    
+    def set_stop_flag(self, stop_flag):
+        """Set the stop flag for checking interruptions"""
+        self.stop_flag = stop_flag
+        self._stopped = False
+    
+    def _check_stop(self) -> bool:
+        """Check if stop has been requested"""
+        if self._stopped:
+            return True
+        if self.stop_flag and self.stop_flag.is_set():
+            self._stopped = True
+            return True
+        # Check global manga translator cancellation
+        try:
+            from manga_translator import MangaTranslator
+            if MangaTranslator.is_globally_cancelled():
+                self._stopped = True
+                return True
+        except Exception:
+            pass
+        return False
+    
+    def reset_stop_flags(self):
+        """Reset stop flags when starting new processing"""
+        self._stopped = False
+    
+    def check_installation(self) -> bool:
+        """Check if provider is installed"""
+        raise NotImplementedError
+    
+    def install(self, progress_callback=None) -> bool:
+        """Install the provider"""
+        raise NotImplementedError
+    
+    def load_model(self, **kwargs) -> bool:
+        """Load the OCR model"""
+        raise NotImplementedError
+    
+    def detect_text(self, image: np.ndarray, **kwargs) -> List[OCRResult]:
+        """Detect text in image"""
+        raise NotImplementedError
+
+class CustomAPIProvider(OCRProvider):
+    """Custom API OCR provider that uses existing GUI variables"""
+    
+    def __init__(self, log_callback=None):
+        super().__init__(log_callback)
+        
+        # Use EXISTING environment variables from TranslatorGUI
+        self.api_url = os.environ.get('OPENAI_CUSTOM_BASE_URL', '')
+        self.api_key = os.environ.get('API_KEY', '') or os.environ.get('OPENAI_API_KEY', '')
+        self.model_name = os.environ.get('MODEL', 'gpt-4o-mini')
+        
+        # OCR prompt - use system prompt or a dedicated OCR prompt variable
+        self.ocr_prompt = os.environ.get('OCR_SYSTEM_PROMPT', 
+            os.environ.get('SYSTEM_PROMPT', 
+            "YOU ARE A TEXT EXTRACTION MACHINE. EXTRACT EXACTLY WHAT YOU SEE.\n\n"
+            "ABSOLUTE RULES:\n"
+            "1. OUTPUT ONLY THE VISIBLE TEXT/SYMBOLS - NOTHING ELSE\n"
+            "2. NEVER TRANSLATE OR MODIFY\n"
+            "3. NEVER EXPLAIN, DESCRIBE, OR COMMENT\n"
+            "4. NEVER SAY \"I can't\" or \"I cannot\" or \"no text\" or \"blank image\"\n"
+            "5. IF YOU SEE DOTS, OUTPUT THE DOTS: .\n"
+            "6. IF YOU SEE PUNCTUATION, OUTPUT THE PUNCTUATION\n"
+            "7. IF YOU SEE A SINGLE CHARACTER, OUTPUT THAT CHARACTER\n"
+            "8. IF YOU SEE NOTHING, OUTPUT NOTHING (empty response)\n\n"
+            "LANGUAGE PRESERVATION:\n"
+            "- Korean text → Output in Korean\n"
+            "- Japanese text → Output in Japanese\n"
+            "- Chinese text → Output in Chinese\n"
+            "- English text → Output in English\n"
+            "- CJK quotation marks (「」『』【】《》〈〉) → Preserve exactly as shown\n\n"
+            "FORMATTING:\n"
+            "- OUTPUT ALL TEXT ON A SINGLE LINE WITH NO LINE BREAKS\n"
+            "- NEVER use \\n or line breaks in your output\n\n"
+            "FORBIDDEN RESPONSES:\n"
+            "- \"I can see this appears to be...\"\n"
+            "- \"I cannot make out any clear text...\"\n"
+            "- \"This appears to be blank...\"\n"
+            "- \"If there is text present...\"\n"
+            "- ANY explanatory text\n\n"
+            "YOUR ONLY OUTPUT: The exact visible text. Nothing more. Nothing less.\n"
+            "If image has a dot → Output: .\n"
+            "If image has two dots → Output: . .\n"
+            "If image has text → Output: [that text]\n"
+            "If image is truly blank → Output: [empty/no response]"
+            ))
+        
+        # Use existing temperature and token settings  
+        self.temperature = float(os.environ.get('TRANSLATION_TEMPERATURE', '0.01'))
+        # NOTE: max_tokens is NOT cached here - it's read fresh from environment in detect_text()
+        # to ensure we always get the latest value from the GUI
+        
+        # Image settings from existing compression variables
+        self.image_format = 'jpeg' if os.environ.get('IMAGE_COMPRESSION_FORMAT', 'auto') != 'png' else 'png'
+        self.image_quality = int(os.environ.get('JPEG_QUALITY', '100'))
+        
+        # Simple defaults
+        self.api_format = 'openai'  # Most custom endpoints are OpenAI-compatible
+        self.timeout = int(os.environ.get('CHUNK_TIMEOUT', '30'))
+        self.api_headers = {}  # Additional custom headers
+        
+        # Retry configuration for Custom API OCR calls
+        self.max_retries = int(os.environ.get('CUSTOM_OCR_MAX_RETRIES', '3'))
+        self.retry_initial_delay = float(os.environ.get('CUSTOM_OCR_RETRY_INITIAL_DELAY', '0.8'))
+        self.retry_backoff = float(os.environ.get('CUSTOM_OCR_RETRY_BACKOFF', '1.8'))
+        self.retry_jitter = float(os.environ.get('CUSTOM_OCR_RETRY_JITTER', '0.4'))
+        self.retry_on_empty = os.environ.get('CUSTOM_OCR_RETRY_ON_EMPTY', '1') == '1'
+        
+    def check_installation(self) -> bool:
+        """Always installed - uses UnifiedClient"""
+        self.is_installed = True
+        return True
+    
+    def install(self, progress_callback=None) -> bool:
+        """No installation needed for API-based provider"""
+        return self.check_installation()
+    
+    def load_model(self, **kwargs) -> bool:
+        """Initialize UnifiedClient with current settings"""
+        try:
+            from unified_api_client import UnifiedClient
+            
+            # Support passing API key from GUI if available
+            if 'api_key' in kwargs:
+                api_key = kwargs['api_key']
+            else:
+                api_key = os.environ.get('API_KEY', '') or os.environ.get('OPENAI_API_KEY', '')
+            
+            if 'model' in kwargs:
+                model = kwargs['model']
+            else:
+                model = os.environ.get('MODEL', 'gpt-4o-mini')
+            
+            if not api_key:
+                self._log("❌ No API key configured", "error")
+                return False
+            
+            # Create UnifiedClient just like translations do
+            self.client = UnifiedClient(model=model, api_key=api_key)
+            
+            #self._log(f"✅ Using {model} for OCR via UnifiedClient")
+            self.is_loaded = True
+            return True
+            
+        except Exception as e:
+            self._log(f"❌ Failed to initialize UnifiedClient: {str(e)}", "error")
+            return False
+    
+    def _test_connection(self) -> bool:
+        """Test API connection with a simple request"""
+        try:
+            # Create a small test image
+            test_image = np.ones((100, 100, 3), dtype=np.uint8) * 255
+            cv2.putText(test_image, "TEST", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
+            
+            # Encode image
+            image_base64 = self._encode_image(test_image)
+            
+            # Prepare test request based on API format
+            if self.api_format == 'openai':
+                test_payload = {
+                    "model": self.model_name,
+                    "messages": [
+                        {
+                            "role": "user",
+                            "content": [
+                                {"type": "text", "text": "What text do you see?"},
+                                {"type": "image_url", "image_url": {"url": f"data:image/{self.image_format};base64,{image_base64}"}}
+                            ]
+                        }
+                    ],
+                    "max_tokens": 50
+                }
+            else:
+                # For other formats, just try a basic health check
+                return True
+            
+            headers = self._prepare_headers()
+            response = requests.post(
+                self.api_url,
+                headers=headers,
+                json=test_payload,
+                timeout=10
+            )
+            
+            return response.status_code == 200
+            
+        except Exception:
+            return False
+    
+    def _encode_image(self, image: np.ndarray) -> str:
+        """Encode numpy array to base64 string"""
+        # Convert BGR to RGB if needed
+        if len(image.shape) == 3 and image.shape[2] == 3:
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        else:
+            image_rgb = image
+        
+        # Convert to PIL Image
+        pil_image = Image.fromarray(image_rgb)
+        
+        # Save to bytes buffer
+        buffer = io.BytesIO()
+        if self.image_format.lower() == 'png':
+            pil_image.save(buffer, format='PNG')
+        else:
+            pil_image.save(buffer, format='JPEG', quality=self.image_quality)
+        
+        # Encode to base64
+        buffer.seek(0)
+        image_base64 = base64.b64encode(buffer.read()).decode('utf-8')
+        
+        return image_base64
+    
+    def _prepare_headers(self) -> dict:
+        """Prepare request headers"""
+        headers = {
+            "Content-Type": "application/json"
+        }
+        
+        # Add API key if configured
+        if self.api_key:
+            if self.api_format == 'anthropic':
+                headers["x-api-key"] = self.api_key
+            else:
+                headers["Authorization"] = f"Bearer {self.api_key}"
+        
+        # Add any custom headers
+        headers.update(self.api_headers)
+        
+        return headers
+    
+    def _prepare_request_payload(self, image_base64: str) -> dict:
+        """Prepare request payload based on API format"""
+        if self.api_format == 'openai':
+            return {
+                "model": self.model_name,
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "text", "text": self.ocr_prompt},
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/{self.image_format};base64,{image_base64}"
+                                }
+                            }
+                        ]
+                    }
+                ],
+                "max_tokens": self.max_tokens,
+                "temperature": self.temperature
+            }
+        
+        elif self.api_format == 'anthropic':
+            return {
+                "model": self.model_name,
+                "max_tokens": self.max_tokens,
+                "temperature": self.temperature,
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": self.ocr_prompt
+                            },
+                            {
+                                "type": "image",
+                                "source": {
+                                    "type": "base64",
+                                    "media_type": f"image/{self.image_format}",
+                                    "data": image_base64
+                                }
+                            }
+                        ]
+                    }
+                ]
+            }
+        
+        else:
+            # Custom format - use environment variable for template
+            template = os.environ.get('CUSTOM_OCR_REQUEST_TEMPLATE', '{}')
+            payload = json.loads(template)
+            
+            # Replace placeholders
+            payload_str = json.dumps(payload)
+            payload_str = payload_str.replace('{{IMAGE_BASE64}}', image_base64)
+            payload_str = payload_str.replace('{{PROMPT}}', self.ocr_prompt)
+            payload_str = payload_str.replace('{{MODEL}}', self.model_name)
+            payload_str = payload_str.replace('{{MAX_TOKENS}}', str(self.max_tokens))
+            payload_str = payload_str.replace('{{TEMPERATURE}}', str(self.temperature))
+            
+            return json.loads(payload_str)
+    
+    def _extract_text_from_response(self, response_data: dict) -> str:
+        """Extract text from API response based on format"""
+        try:
+            if self.api_format == 'openai':
+                # OpenAI format: response.choices[0].message.content
+                return response_data.get('choices', [{}])[0].get('message', {}).get('content', '')
+            
+            elif self.api_format == 'anthropic':
+                # Anthropic format: response.content[0].text
+                content = response_data.get('content', [])
+                if content and isinstance(content, list):
+                    return content[0].get('text', '')
+                return ''
+            
+            else:
+                # Custom format - use environment variable for path
+                response_path = os.environ.get('CUSTOM_OCR_RESPONSE_PATH', 'text')
+                
+                # Navigate through the response using the path
+                result = response_data
+                for key in response_path.split('.'):
+                    if isinstance(result, dict):
+                        result = result.get(key, '')
+                    elif isinstance(result, list) and key.isdigit():
+                        idx = int(key)
+                        result = result[idx] if idx < len(result) else ''
+                    else:
+                        result = ''
+                        break
+                
+                return str(result)
+                
+        except Exception as e:
+            self._log(f"Failed to extract text from response: {e}", "error")
+            return ''
+    
+    def detect_text(self, image: np.ndarray, **kwargs) -> List[OCRResult]:
+        """Process image using UnifiedClient.send_image()"""
+        results = []
+        
+        try:
+            # CRITICAL: Reload OCR prompt from environment before each detection
+            # This ensures we use the latest prompt set by manga_integration.py
+            self.ocr_prompt = os.environ.get('OCR_SYSTEM_PROMPT', self.ocr_prompt)
+            
+            # Get fresh max_tokens from environment - GUI will have set this
+            max_tokens = int(os.environ.get('MAX_OUTPUT_TOKENS', '8192'))
+            if not self.is_loaded:
+                if not self.load_model():
+                    return results
+            
+            import cv2
+            from PIL import Image
+            import base64
+            import io
+            
+            # Validate and resize image if too small (consistent with Google/Azure logic)
+            h, w = image.shape[:2]
+            MIN_SIZE = 50  # Minimum dimension for good OCR quality
+            MIN_AREA = 2500  # Minimum area (50x50)
+            
+            # Skip completely invalid/corrupted images (0 or negative dimensions)
+            if h <= 0 or w <= 0:
+                self._log(f"⚠️ Invalid image dimensions ({w}x{h}px), skipping", "warning")
+                return results
+            
+            if h < MIN_SIZE or w < MIN_SIZE or h * w < MIN_AREA:
+                # Image too small - resize it
+                scale_w = MIN_SIZE / w if w < MIN_SIZE else 1.0
+                scale_h = MIN_SIZE / h if h < MIN_SIZE else 1.0
+                scale = max(scale_w, scale_h)
+                
+                if scale > 1.0:
+                    new_w = int(w * scale)
+                    new_h = int(h * scale)
+                    image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_CUBIC)
+                    self._log(f"🔍 Image resized from {w}x{h}px to {new_w}x{new_h}px for Custom API OCR", "debug")
+                    h, w = new_h, new_w
+            
+            # Convert numpy array to PIL Image
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(image_rgb)
+            
+            # Convert PIL Image to base64 string
+            buffer = io.BytesIO()
+            
+            # Use the image format from settings
+            if self.image_format.lower() == 'png':
+                pil_image.save(buffer, format='PNG')
+            else:
+                pil_image.save(buffer, format='JPEG', quality=self.image_quality)
+            
+            buffer.seek(0)
+            image_base64 = base64.b64encode(buffer.read()).decode('utf-8')
+            
+            # For OpenAI vision models, we need BOTH:
+            # 1. System prompt with instructions
+            # 2. User message that includes the image
+            messages = [
+                {
+                    "role": "system",
+                    "content": self.ocr_prompt  # The OCR instruction as system prompt
+                },
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": "Image:"  # Minimal text, just to have something
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/jpeg;base64,{image_base64}"
+                            }
+                        }
+                    ]
+                }
+            ]
+            
+            # Now send this properly formatted message
+            # The UnifiedClient should handle this correctly
+            # But we're NOT using send_image, we're using regular send
+
+            # Retry-aware call
+            from unified_api_client import UnifiedClientError  # local import to avoid hard dependency at module import time
+            max_attempts = max(1, self.max_retries)
+            attempt = 0
+            last_error = None
+
+            # Common refusal/error phrases that indicate a non-OCR response (expanded list)
+            refusal_phrases = [
+                "I can't extract", "I cannot extract",
+                "I'm sorry", "I am sorry",
+                "I'm unable", "I am unable",
+                "cannot process images",
+                "I can't help with that",
+                "cannot view images",
+                "no text in the image",
+                "I can see this appears",
+                "I cannot make out",
+                "appears to be blank",
+                "appears to be a mostly blank",
+                "mostly blank or white image",
+                "If there is text present",
+                "too small, faint, or unclear",
+                "cannot accurately extract",
+                "may be too",
+                "However, I cannot",
+                "I don't see any",
+                "no clear text",
+                "no visible text",
+                "does not contain",
+                "doesn't contain",
+                "I do not see"
+            ]
+
+            while attempt < max_attempts:
+                # Check for stop before each attempt
+                if self._check_stop():
+                    self._log("⏹️ OCR processing stopped by user", "warning")
+                    return results
+                
+                try:
+                    response = self.client.send(
+                        messages=messages,
+                        temperature=self.temperature,
+                        max_tokens=max_tokens
+                    )
+
+                    # Extract content from response object
+                    content, finish_reason = response
+
+                    # Validate content
+                    has_content = bool(content and str(content).strip())
+                    refused = False
+                    if has_content:
+                        # Filter out explicit failure markers
+                        if "[" in content and "FAILED]" in content:
+                            refused = True
+                        elif any(phrase.lower() in content.lower() for phrase in refusal_phrases):
+                            refused = True
+
+                    # Decide success or retry
+                    if has_content and not refused:
+                        text = str(content).strip()
+                        results.append(OCRResult(
+                            text=text,
+                            bbox=(0, 0, w, h),
+                            confidence=kwargs.get('confidence', 0.85),
+                            vertices=[(0, 0), (w, 0), (w, h), (0, h)]
+                        ))
+                        self._log(f"✅ Detected: {text[:50]}...")
+                        break  # success
+                    else:
+                        reason = "empty result" if not has_content else "refusal/non-OCR response"
+                        last_error = f"{reason} (finish_reason: {finish_reason})"
+                        # Check if we should retry on empty or refusal
+                        should_retry = (not has_content and self.retry_on_empty) or refused
+                        attempt += 1
+                        if attempt >= max_attempts or not should_retry:
+                            # No more retries or shouldn't retry
+                            if not has_content:
+                                self._log(f"⚠️ No text detected (finish_reason: {finish_reason})")
+                            else:
+                                self._log(f"❌ Model returned non-OCR response: {str(content)[:120]}", "warning")
+                            break
+                        # Backoff before retrying
+                        delay = self.retry_initial_delay * (self.retry_backoff ** (attempt - 1)) + random.uniform(0, self.retry_jitter)
+                        self._log(f"🔄 Retry {attempt}/{max_attempts - 1} after {delay:.1f}s due to {reason}...", "warning")
+                        time.sleep(delay)
+                        time.sleep(0.1)  # Brief pause for stability
+                        self._log("💤 OCR retry pausing briefly for stability", "debug")
+                        continue
+
+                except UnifiedClientError as ue:
+                    msg = str(ue)
+                    last_error = msg
+                    # Do not retry on explicit user cancellation
+                    if 'cancelled' in msg.lower() or 'stopped by user' in msg.lower():
+                        self._log(f"❌ OCR cancelled: {msg}", "error")
+                        break
+                    attempt += 1
+                    if attempt >= max_attempts:
+                        self._log(f"❌ OCR failed after {attempt} attempts: {msg}", "error")
+                        break
+                    delay = self.retry_initial_delay * (self.retry_backoff ** (attempt - 1)) + random.uniform(0, self.retry_jitter)
+                    self._log(f"🔄 API error, retry {attempt}/{max_attempts - 1} after {delay:.1f}s: {msg}", "warning")
+                    time.sleep(delay)
+                    time.sleep(0.1)  # Brief pause for stability
+                    self._log("💤 OCR API error retry pausing briefly for stability", "debug")
+                    continue
+                except Exception as e_inner:
+                    last_error = str(e_inner)
+                    attempt += 1
+                    if attempt >= max_attempts:
+                        self._log(f"❌ OCR exception after {attempt} attempts: {last_error}", "error")
+                        break
+                    delay = self.retry_initial_delay * (self.retry_backoff ** (attempt - 1)) + random.uniform(0, self.retry_jitter)
+                    self._log(f"🔄 Exception, retry {attempt}/{max_attempts - 1} after {delay:.1f}s: {last_error}", "warning")
+                    time.sleep(delay)
+                    time.sleep(0.1)  # Brief pause for stability
+                    self._log("💤 OCR exception retry pausing briefly for stability", "debug")
+                    continue
+        
+        except Exception as e:
+            self._log(f"❌ Error: {str(e)}", "error")
+            import traceback
+            self._log(traceback.format_exc(), "debug")
+        
+        return results
+
+class MangaOCRProvider(OCRProvider):
+    """Manga OCR provider using HuggingFace model directly"""
+    
+    def __init__(self, log_callback=None):
+        super().__init__(log_callback)
+        self.processor = None
+        self.model = None
+        self.tokenizer = None
+        
+    def check_installation(self) -> bool:
+        """Check if transformers is installed"""
+        try:
+            import transformers
+            import torch
+            self.is_installed = True
+            return True
+        except ImportError:
+            return False 
+    
+    def install(self, progress_callback=None) -> bool:
+        """Install transformers and torch"""
+        pass
+    
+    def _is_valid_local_model_dir(self, path: str) -> bool:
+        """Check that a local HF model directory has required files."""
+        try:
+            if not path or not os.path.isdir(path):
+                return False
+            needed_any_weights = any(
+                os.path.exists(os.path.join(path, name)) for name in (
+                    'pytorch_model.bin',
+                    'model.safetensors'
+                )
+            )
+            has_config = os.path.exists(os.path.join(path, 'config.json'))
+            has_processor = (
+                os.path.exists(os.path.join(path, 'preprocessor_config.json')) or
+                os.path.exists(os.path.join(path, 'processor_config.json'))
+            )
+            has_tokenizer = (
+                os.path.exists(os.path.join(path, 'tokenizer.json')) or
+                os.path.exists(os.path.join(path, 'tokenizer_config.json'))
+            )
+            return has_config and needed_any_weights and has_processor and has_tokenizer
+        except Exception:
+            return False
+    
+    def load_model(self, **kwargs) -> bool:
+        """Load the manga-ocr model, preferring a local directory to avoid re-downloading"""
+        print("\n>>> MangaOCRProvider.load_model() called")
+        try:
+            if not self.is_installed and not self.check_installation():
+                print("ERROR: Transformers not installed")
+                self._log("❌ Transformers not installed", "error")
+                return False
+
+            # Always disable progress bars to avoid tqdm issues in some environments
+            import os
+            os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
+
+            from transformers import VisionEncoderDecoderModel, AutoTokenizer, AutoImageProcessor
+            import torch
+
+            # Prefer a local model directory if present to avoid any Hub access
+            candidates = []
+            env_local = os.environ.get("MANGA_OCR_LOCAL_DIR")
+            if env_local:
+                candidates.append(env_local)
+
+            # Project root one level up from this file
+            root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
+            candidates.append(os.path.join(root_dir, 'models', 'manga-ocr-base'))
+            candidates.append(os.path.join(root_dir, 'models', 'kha-white', 'manga-ocr-base'))
+
+            model_source = None
+            local_only = False
+            # Find a valid local dir
+            for cand in candidates:
+                if self._is_valid_local_model_dir(cand):
+                    model_source = cand
+                    local_only = True
+                    break
+
+            # If no valid local dir, use Hub
+            if not model_source:
+                model_source = "kha-white/manga-ocr-base"
+                # Make sure we are not forcing offline mode
+                if os.environ.get("HF_HUB_OFFLINE") == "1":
+                    try:
+                        del os.environ["HF_HUB_OFFLINE"]
+                    except Exception:
+                        pass
+                self._log("🔥 Loading manga-ocr model from Hugging Face Hub")
+                self._log(f"   Repo: {model_source}")
+            else:
+                # Only set offline when local dir is fully valid
+                os.environ.setdefault("HF_HUB_OFFLINE", "1")
+                self._log("🔥 Loading manga-ocr model from local directory")
+                self._log(f"   Local path: {model_source}")
+
+            # Decide target device once; we will move after full CPU load to avoid meta tensors
+            use_cuda = torch.cuda.is_available()
+
+            # Try loading components, falling back to Hub if local-only fails
+            def _load_components(source: str, local_flag: bool):
+                self._log("   Loading tokenizer...")
+                tok = AutoTokenizer.from_pretrained(source, local_files_only=local_flag)
+
+                self._log("   Loading image processor...")
+                try:
+                    from transformers import AutoProcessor
+                except Exception:
+                    AutoProcessor = None
+                try:
+                    proc = AutoImageProcessor.from_pretrained(source, local_files_only=local_flag)
+                except Exception as e_proc:
+                    if AutoProcessor is not None:
+                        self._log(f"   ⚠️ AutoImageProcessor failed: {e_proc}. Trying AutoProcessor...", "warning")
+                        proc = AutoProcessor.from_pretrained(source, local_files_only=local_flag)
+                    else:
+                        raise
+
+                self._log("   Loading model...")
+                # Prevent meta tensors by forcing full materialization on CPU at load time
+                os.environ.setdefault('TORCHDYNAMO_DISABLE', '1')
+                mdl = VisionEncoderDecoderModel.from_pretrained(
+                    source,
+                    local_files_only=local_flag,
+                    low_cpu_mem_usage=False,
+                    device_map=None,
+                    torch_dtype=torch.float32  # Use torch_dtype instead of dtype
+                )
+                return tok, proc, mdl
+
+            try:
+                self.tokenizer, self.processor, self.model = _load_components(model_source, local_only)
+            except Exception as e_local:
+                if local_only:
+                    # Fallback to Hub once if local fails
+                    self._log(f"   ⚠️ Local model load failed: {e_local}", "warning")
+                    try:
+                        if os.environ.get("HF_HUB_OFFLINE") == "1":
+                            del os.environ["HF_HUB_OFFLINE"]
+                    except Exception:
+                        pass
+                    model_source = "kha-white/manga-ocr-base"
+                    local_only = False
+                    self._log("   Retrying from Hugging Face Hub...")
+                    self.tokenizer, self.processor, self.model = _load_components(model_source, local_only)
+                else:
+                    raise
+
+            # Move to CUDA only after full CPU materialization
+            target_device = 'cpu'
+            if use_cuda:
+                try:
+                    self.model = self.model.to('cuda')
+                    target_device = 'cuda'
+                except Exception as move_err:
+                    self._log(f"   ⚠️ Could not move model to CUDA: {move_err}", "warning")
+                    target_device = 'cpu'
+
+            # Finalize eval mode
+            self.model.eval()
+
+            # Sanity-check: ensure no parameter remains on 'meta' device
+            try:
+                for n, p in self.model.named_parameters():
+                    dev = getattr(p, 'device', None)
+                    if dev is not None and getattr(dev, 'type', '') == 'meta':
+                        raise RuntimeError(f"Parameter {n} is on 'meta' after load")
+            except Exception as sanity_err:
+                self._log(f"❌ Manga-OCR model load sanity check failed: {sanity_err}", "error")
+                return False
+
+            print(f"SUCCESS: Model loaded on {target_device.upper()}")
+            self._log(f"   ✅ Model loaded on {target_device.upper()}")
+            self.is_loaded = True
+            self._log("✅ Manga OCR model ready")
+            print(">>> Returning True from load_model()")
+            return True
+
+        except Exception as e:
+            print(f"\nEXCEPTION in load_model: {e}")
+            import traceback
+            print(traceback.format_exc())
+            self._log(f"❌ Failed to load manga-ocr model: {str(e)}", "error")
+            self._log(traceback.format_exc(), "error")
+            try:
+                if 'local_only' in locals() and local_only:
+                    self._log("Hint: Local load failed. Ensure your models/manga-ocr-base contains required files (config.json, preprocessor_config.json, tokenizer.json or tokenizer_config.json, and model weights).", "warning")
+            except Exception:
+                pass
+            return False
+    
+    def _run_ocr(self, pil_image):
+        """Run OCR on a PIL image using the HuggingFace model"""
+        import torch
+        
+        # Process image (keyword arg for broader compatibility across transformers versions)
+        inputs = self.processor(images=pil_image, return_tensors="pt")
+        pixel_values = inputs["pixel_values"]
+        
+        # Move to same device as model
+        try:
+            model_device = next(self.model.parameters()).device
+        except StopIteration:
+            model_device = torch.device('cpu')
+        pixel_values = pixel_values.to(model_device)
+        
+        # Generate text
+        with torch.no_grad():
+            generated_ids = self.model.generate(pixel_values)
+        
+        # Decode
+        generated_text = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        
+        return generated_text
+    
+    def detect_text(self, image: np.ndarray, **kwargs) -> List[OCRResult]:
+        """
+        Process the image region passed to it.
+        This could be a bubble region or the full image.
+        """
+        results = []
+        
+        # Check for stop at start
+        if self._check_stop():
+            self._log("⏹️ Manga-OCR processing stopped by user", "warning")
+            return results
+        
+        try:
+            if not self.is_loaded:
+                if not self.load_model():
+                    return results
+            
+            import cv2
+            from PIL import Image
+            
+            # Get confidence from kwargs
+            confidence = kwargs.get('confidence', 0.7)
+            
+            # Convert numpy array to PIL
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(image_rgb)
+            h, w = image.shape[:2]
+            
+            self._log("🔍 Processing region with manga-ocr...")
+            
+            # Check for stop before inference
+            if self._check_stop():
+                self._log("⏹️ Manga-OCR inference stopped by user", "warning")
+                return results
+            
+            # Run OCR on the image region
+            text = self._run_ocr(pil_image)
+            
+            if text and text.strip():
+                # Return result for this region with its actual bbox
+                results.append(OCRResult(
+                    text=text.strip(),
+                    bbox=(0, 0, w, h),  # Relative to the region passed in
+                    confidence=confidence,
+                    vertices=[(0, 0), (w, 0), (w, h), (0, h)]
+                ))
+                self._log(f"✅ Detected text: {text[:50]}...")
+            
+        except Exception as e:
+            self._log(f"❌ Error in manga-ocr: {str(e)}", "error")
+            
+        return results
+
+class Qwen2VL(OCRProvider):
+    """OCR using Qwen2-VL - Vision Language Model that can read Korean text"""
+    
+    def __init__(self, log_callback=None):
+        super().__init__(log_callback)
+        self.processor = None
+        self.model = None
+        self.tokenizer = None
+        
+        # Get OCR prompt from environment or use default (UPDATED: Improved prompt)
+        self.ocr_prompt = os.environ.get('OCR_SYSTEM_PROMPT', 
+            "YOU ARE A TEXT EXTRACTION MACHINE. EXTRACT EXACTLY WHAT YOU SEE.\n\n"
+            "ABSOLUTE RULES:\n"
+            "1. OUTPUT ONLY THE VISIBLE TEXT/SYMBOLS - NOTHING ELSE\n"
+            "2. NEVER TRANSLATE OR MODIFY\n"
+            "3. NEVER EXPLAIN, DESCRIBE, OR COMMENT\n"
+            "4. NEVER SAY \"I can't\" or \"I cannot\" or \"no text\" or \"blank image\"\n"
+            "5. IF YOU SEE DOTS, OUTPUT THE DOTS: .\n"
+            "6. IF YOU SEE PUNCTUATION, OUTPUT THE PUNCTUATION\n"
+            "7. IF YOU SEE A SINGLE CHARACTER, OUTPUT THAT CHARACTER\n"
+            "8. IF YOU SEE NOTHING, OUTPUT NOTHING (empty response)\n\n"
+            "LANGUAGE PRESERVATION:\n"
+            "- Korean text → Output in Korean\n"
+            "- Japanese text → Output in Japanese\n"
+            "- Chinese text → Output in Chinese\n"
+            "- English text → Output in English\n"
+            "- CJK quotation marks (「」『』【】《》〈〉) → Preserve exactly as shown\n\n"
+            "FORMATTING:\n"
+            "- OUTPUT ALL TEXT ON A SINGLE LINE WITH NO LINE BREAKS\n"
+            "- NEVER use \\n or line breaks in your output\n\n"
+            "FORBIDDEN RESPONSES:\n"
+            "- \"I can see this appears to be...\"\n"
+            "- \"I cannot make out any clear text...\"\n"
+            "- \"This appears to be blank...\"\n"
+            "- \"If there is text present...\"\n"
+            "- ANY explanatory text\n\n"
+            "YOUR ONLY OUTPUT: The exact visible text. Nothing more. Nothing less.\n"
+            "If image has a dot → Output: .\n"
+            "If image has two dots → Output: . .\n"
+            "If image has text → Output: [that text]\n"
+            "If image is truly blank → Output: [empty/no response]"
+        )
+    
+    def set_ocr_prompt(self, prompt: str):
+        """Allow setting the OCR prompt dynamically"""
+        self.ocr_prompt = prompt
+        
+    def check_installation(self) -> bool:
+        """Check if required packages are installed"""
+        try:
+            import transformers
+            import torch
+            self.is_installed = True
+            return True
+        except ImportError:
+            return False
+    
+    def install(self, progress_callback=None) -> bool:
+        """Install requirements for Qwen2-VL"""
+        pass
+    
+    def load_model(self, model_size=None, **kwargs) -> bool:
+        """Load Qwen2-VL model with size selection"""
+        self._log(f"DEBUG: load_model called with model_size={model_size}")
+
+        try:
+            if not self.is_installed and not self.check_installation():
+                self._log("❌ Not installed", "error")
+                return False
+            
+            self._log("🔥 Loading Qwen2-VL for Advanced OCR...")
+
+
+            
+            from transformers import AutoProcessor, AutoTokenizer
+            import torch
+            
+            # Model options
+            model_options = {
+                "1": "Qwen/Qwen2-VL-2B-Instruct",
+                "2": "Qwen/Qwen2-VL-7B-Instruct",
+                "3": "Qwen/Qwen2-VL-72B-Instruct",
+                "4": "custom"
+            }
+            # CHANGE: Default to 7B instead of 2B
+            # Check for saved preference first
+            if model_size is None:
+                # Try to get from environment or config
+                import os
+                model_size = os.environ.get('QWEN2VL_MODEL_SIZE', '1')
+            
+            # Determine which model to load
+            if model_size and str(model_size).startswith("custom:"):
+                # Custom model passed with ID
+                model_id = str(model_size).replace("custom:", "")
+                self.loaded_model_size = "Custom"
+                self.model_id = model_id
+                self._log(f"Loading custom model: {model_id}")
+            elif model_size == "4":
+                # Custom option selected but no ID - shouldn't happen
+                self._log("❌ Custom model selected but no ID provided", "error")
+                return False
+            elif model_size and str(model_size) in model_options:
+                # Standard model option
+                option = model_options[str(model_size)]
+                if option == "custom":
+                    self._log("❌ Custom model needs an ID", "error")
+                    return False
+                model_id = option
+                # Set loaded_model_size for status display
+                if model_size == "1":
+                    self.loaded_model_size = "2B"
+                elif model_size == "2":
+                    self.loaded_model_size = "7B"
+                elif model_size == "3":
+                    self.loaded_model_size = "72B"
+            else:
+                # CHANGE: Default to 7B (option "2") instead of 2B
+                model_id = model_options["1"]  # Changed from "1" to "2"
+                self.loaded_model_size = "2B"   # Changed from "2B" to "7B"
+                self._log("No model size specified, defaulting to 2B")  # Changed message
+            
+            self._log(f"Loading model: {model_id}")
+            
+            # Load processor and tokenizer
+            self.processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+            self.tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+            
+            # Load the model - let it figure out the class dynamically
+            if torch.cuda.is_available():
+                self._log(f"GPU: {torch.cuda.get_device_name(0)}")
+                # Use auto model class
+                from transformers import AutoModelForVision2Seq
+                self.model = AutoModelForVision2Seq.from_pretrained(
+                    model_id,
+                    dtype=torch.float16,
+                    device_map="auto",
+                    trust_remote_code=True
+                )
+                self._log("✅ Model loaded on GPU")
+            else:
+                self._log("Loading on CPU...")
+                from transformers import AutoModelForVision2Seq
+                self.model = AutoModelForVision2Seq.from_pretrained(
+                    model_id,
+                    dtype=torch.float32,
+                    trust_remote_code=True
+                )
+                self._log("✅ Model loaded on CPU")
+            
+            self.model.eval()
+            self.is_loaded = True
+            self._log("✅ Qwen2-VL ready for Advanced OCR!")
+            return True
+            
+        except Exception as e:
+            self._log(f"❌ Failed to load: {str(e)}", "error")
+            import traceback
+            self._log(traceback.format_exc(), "debug")
+            return False       
+
+    def detect_text(self, image: np.ndarray, **kwargs) -> List[OCRResult]:
+        """Process image with Qwen2-VL for Korean text extraction"""
+        results = []
+        if hasattr(self, 'model_id'):
+            self._log(f"DEBUG: Using model: {self.model_id}", "debug")
+            
+        # Check if OCR prompt was passed in kwargs (for dynamic updates)
+        if 'ocr_prompt' in kwargs:
+            self.ocr_prompt = kwargs['ocr_prompt']
+        
+        try:
+            if not self.is_loaded:
+                if not self.load_model():
+                    return results
+            
+            import cv2
+            from PIL import Image
+            import torch
+            
+            # Convert to PIL
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(image_rgb)
+            h, w = image.shape[:2]
+            
+            self._log(f"🔍 Processing with Qwen2-VL ({w}x{h} pixels)...")
+            
+            # Use the configurable OCR prompt
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "image": pil_image,
+                        },
+                        {
+                            "type": "text", 
+                            "text": self.ocr_prompt  # Use the configurable prompt
+                        }
+                    ]
+                }
+            ]
+            
+            # Alternative simpler prompt if the above still causes issues:
+            # "text": "OCR: Extract text as-is"
+            
+            # Process with Qwen2-VL
+            text = self.processor.apply_chat_template(
+                messages, 
+                tokenize=False, 
+                add_generation_prompt=True
+            )
+
+            inputs = self.processor(
+                text=[text],
+                images=[pil_image],
+                padding=True,
+                return_tensors="pt"
+            )
+
+            # Get the device and dtype the model is currently on
+            model_device = next(self.model.parameters()).device
+            model_dtype = next(self.model.parameters()).dtype
+
+            # Move inputs to the same device as the model and cast float tensors to model dtype
+            try:
+                # Move first
+                inputs = inputs.to(model_device)
+                # Then align dtypes only for floating tensors (e.g., pixel_values)
+                for k, v in inputs.items():
+                    if isinstance(v, torch.Tensor) and torch.is_floating_point(v):
+                        inputs[k] = v.to(model_dtype)
+            except Exception:
+                # Fallback: ensure at least pixel_values is correct if present
+                try:
+                    if isinstance(inputs, dict) and "pixel_values" in inputs:
+                        pv = inputs["pixel_values"].to(model_device)
+                        if torch.is_floating_point(pv):
+                            inputs["pixel_values"] = pv.to(model_dtype)
+                except Exception:
+                    pass
+
+            # Ensure pixel_values explicitly matches model dtype if present
+            try:
+                if isinstance(inputs, dict) and "pixel_values" in inputs:
+                    inputs["pixel_values"] = inputs["pixel_values"].to(device=model_device, dtype=model_dtype)
+            except Exception:
+                pass
+
+            # Generate text with stricter parameters to avoid creative responses
+            use_amp = (hasattr(torch, 'cuda') and model_device.type == 'cuda' and model_dtype in (torch.float16, torch.bfloat16))
+            autocast_dev = 'cuda' if model_device.type == 'cuda' else 'cpu'
+            autocast_dtype = model_dtype if model_dtype in (torch.float16, torch.bfloat16) else None
+
+            with torch.no_grad():
+                if use_amp and autocast_dtype is not None:
+                    with torch.autocast(autocast_dev, dtype=autocast_dtype):
+                        generated_ids = self.model.generate(
+                            **inputs,
+                            max_new_tokens=128,      # Reduced from 512 - manga bubbles are typically short
+                            do_sample=False,        # Keep deterministic
+                            temperature=0.01,       # Keep your very low temperature
+                            top_p=1.0,             # Keep no nucleus sampling
+                            repetition_penalty=1.0, # Keep no repetition penalty
+                            num_beams=1,           # Ensure greedy decoding (faster than beam search)
+                            use_cache=True,        # Enable KV cache for speed
+                            early_stopping=True,   # Stop at EOS token
+                            pad_token_id=self.tokenizer.pad_token_id,      # Proper padding
+                            eos_token_id=self.tokenizer.eos_token_id,      # Proper stopping
+                        )
+                else:
+                    generated_ids = self.model.generate(
+                        **inputs,
+                        max_new_tokens=128,      # Reduced from 512 - manga bubbles are typically short
+                        do_sample=False,        # Keep deterministic
+                        temperature=0.01,       # Keep your very low temperature
+                        top_p=1.0,             # Keep no nucleus sampling
+                        repetition_penalty=1.0, # Keep no repetition penalty
+                        num_beams=1,           # Ensure greedy decoding (faster than beam search)
+                        use_cache=True,        # Enable KV cache for speed
+                        early_stopping=True,   # Stop at EOS token
+                        pad_token_id=self.tokenizer.pad_token_id,      # Proper padding
+                        eos_token_id=self.tokenizer.eos_token_id,      # Proper stopping
+                    )
+            
+            # Decode the output
+            generated_ids_trimmed = [
+                out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+            ]
+            output_text = self.processor.batch_decode(
+                generated_ids_trimmed, 
+                skip_special_tokens=True, 
+                clean_up_tokenization_spaces=False
+            )[0]
+            
+            if output_text and output_text.strip():
+                text = output_text.strip()
+                
+                # ADDED: Filter out any response that looks like an explanation or apology
+                # Common patterns that indicate the model is being "helpful" instead of just extracting
+                unwanted_patterns = [
+                    "죄송합니다",  # "I apologize"
+                    "sorry",
+                    "apologize",
+                    "이미지에는",  # "in this image"
+                    "텍스트가 없습니다",  # "there is no text"
+                    "I cannot",
+                    "I don't see",
+                    "There is no",
+                    "질문이 있으시면",  # "if you have questions"
+                ]
+                
+                # Check if response contains unwanted patterns
+                text_lower = text.lower()
+                is_explanation = any(pattern.lower() in text_lower for pattern in unwanted_patterns)
+                
+                # Also check if the response is suspiciously long for a bubble
+                # Most manga bubbles are short, if we get 50+ chars it might be an explanation
+                is_too_long = len(text) > 100 and ('.' in text or ',' in text or '!' in text)
+                
+                if is_explanation or is_too_long:
+                    self._log(f"⚠️ Model returned explanation instead of text, ignoring", "warning")
+                    # Return empty result or just skip this region
+                    return results
+                
+                # Check language
+                has_korean = any('\uAC00' <= c <= '\uD7AF' for c in text)
+                has_japanese = any('\u3040' <= c <= '\u309F' or '\u30A0' <= c <= '\u30FF' for c in text)
+                has_chinese = any('\u4E00' <= c <= '\u9FFF' for c in text)
+                
+                if has_korean:
+                    self._log(f"✅ Korean detected: {text[:50]}...")
+                elif has_japanese:
+                    self._log(f"✅ Japanese detected: {text[:50]}...")
+                elif has_chinese:
+                    self._log(f"✅ Chinese detected: {text[:50]}...")
+                else:
+                    self._log(f"✅ Text: {text[:50]}...")
+                
+                results.append(OCRResult(
+                    text=text,
+                    bbox=(0, 0, w, h),
+                    confidence=0.9,
+                    vertices=[(0, 0), (w, 0), (w, h), (0, h)]
+                ))
+            else:
+                self._log("⚠️ No text detected", "warning")
+        
+        except Exception as e:
+            self._log(f"❌ Error: {str(e)}", "error")
+            import traceback
+            self._log(traceback.format_exc(), "debug")
+        
+        return results
+    
+class EasyOCRProvider(OCRProvider):
+    """EasyOCR provider for multiple languages"""
+    
+    def __init__(self, log_callback=None, languages=None):
+        super().__init__(log_callback)
+        # Default to safe language combination
+        self.languages = languages or ['ja', 'en']  # Safe default
+        self._validate_language_combination()
+
+    def _validate_language_combination(self):
+        """Validate and fix EasyOCR language combinations"""
+        # EasyOCR language compatibility rules
+        incompatible_pairs = [
+            (['ja', 'ko'], 'Japanese and Korean cannot be used together'),
+            (['ja', 'zh'], 'Japanese and Chinese cannot be used together'),
+            (['ko', 'zh'], 'Korean and Chinese cannot be used together')
+        ]
+        
+        for incompatible, reason in incompatible_pairs:
+            if all(lang in self.languages for lang in incompatible):
+                self._log(f"⚠️ EasyOCR: {reason}", "warning")
+                # Keep first language + English
+                self.languages = [self.languages[0], 'en']
+                self._log(f"🔧 Auto-adjusted to: {self.languages}", "info")
+                break
+    
+    def check_installation(self) -> bool:
+        """Check if easyocr is installed"""
+        try:
+            import easyocr
+            self.is_installed = True
+            return True
+        except ImportError:
+            return False
+    
+    def install(self, progress_callback=None) -> bool:
+        """Install easyocr"""
+        pass
+    
+    def load_model(self, **kwargs) -> bool:
+        """Load easyocr model"""
+        try:
+            if not self.is_installed and not self.check_installation():
+                self._log("❌ easyocr not installed", "error")
+                return False
+            
+            self._log(f"🔥 Loading easyocr model for languages: {self.languages}...")
+            import easyocr
+            
+            # This will download models on first run
+            self.model = easyocr.Reader(self.languages, gpu=True)
+            self.is_loaded = True
+            
+            self._log("✅ easyocr model loaded successfully")
+            return True
+            
+        except Exception as e:
+            self._log(f"❌ Failed to load easyocr: {str(e)}", "error")
+            # Try CPU mode if GPU fails
+            try:
+                import easyocr
+                self.model = easyocr.Reader(self.languages, gpu=False)
+                self.is_loaded = True
+                self._log("✅ easyocr loaded in CPU mode")
+                return True
+            except:
+                return False
+    
+    def detect_text(self, image: np.ndarray, **kwargs) -> List[OCRResult]:
+        """Detect text using easyocr"""
+        results = []
+        
+        try:
+            if not self.is_loaded:
+                if not self.load_model():
+                    return results
+            
+            # EasyOCR can work directly with numpy arrays
+            ocr_results = self.model.readtext(image, detail=1)
+            
+            # Parse results
+            for (bbox, text, confidence) in ocr_results:
+                # bbox is a list of 4 points
+                xs = [point[0] for point in bbox]
+                ys = [point[1] for point in bbox]
+                x_min, x_max = min(xs), max(xs)
+                y_min, y_max = min(ys), max(ys)
+                
+                results.append(OCRResult(
+                    text=text,
+                    bbox=(int(x_min), int(y_min), int(x_max - x_min), int(y_max - y_min)),
+                    confidence=confidence,
+                    vertices=[(int(p[0]), int(p[1])) for p in bbox]
+                ))
+            
+            self._log(f"✅ Detected {len(results)} text regions")
+            
+        except Exception as e:
+            self._log(f"❌ Error in easyocr detection: {str(e)}", "error")
+        
+        return results
+
+
+class PaddleOCRProvider(OCRProvider):
+    """PaddleOCR provider with memory safety measures"""
+    
+    def check_installation(self) -> bool:
+        """Check if paddleocr is installed"""
+        try:
+            from paddleocr import PaddleOCR
+            self.is_installed = True
+            return True
+        except ImportError:
+            return False
+    
+    def install(self, progress_callback=None) -> bool:
+        """Install paddleocr"""
+        pass
+    
+    def load_model(self, **kwargs) -> bool:
+        """Load paddleocr model with memory-safe configurations"""
+        try:
+            if not self.is_installed and not self.check_installation():
+                self._log("❌ paddleocr not installed", "error")
+                return False
+            
+            self._log("🔥 Loading PaddleOCR model...")
+            
+            # Set memory-safe environment variables BEFORE importing
+            import os
+            os.environ['OMP_NUM_THREADS'] = '1'  # Prevent OpenMP conflicts
+            os.environ['MKL_NUM_THREADS'] = '1'  # Prevent MKL conflicts
+            os.environ['OPENBLAS_NUM_THREADS'] = '1'  # Prevent OpenBLAS conflicts
+            os.environ['FLAGS_use_mkldnn'] = '0'  # Disable MKL-DNN
+            
+            from paddleocr import PaddleOCR
+            
+            # Try memory-safe configurations
+            configs_to_try = [
+                # Config 1: Most memory-safe configuration
+                {
+                    'use_angle_cls': False,  # Disable angle to save memory
+                    'lang': 'ch',
+                    'rec_batch_num': 1,  # Process one at a time
+                    'max_text_length': 100,  # Limit text length
+                    'drop_score': 0.5,  # Higher threshold to reduce detections
+                    'cpu_threads': 1,  # Single thread to avoid conflicts
+                },
+                # Config 2: Minimal memory footprint
+                {
+                    'lang': 'ch',
+                    'rec_batch_num': 1,
+                    'cpu_threads': 1,
+                },
+                # Config 3: Absolute minimal
+                {
+                    'lang': 'ch'
+                },
+                # Config 4: Empty config
+                {}
+            ]
+            
+            for i, config in enumerate(configs_to_try):
+                try:
+                    self._log(f"   Trying configuration {i+1}/{len(configs_to_try)}: {config}")
+                    
+                    # Force garbage collection before loading
+                    import gc
+                    gc.collect()
+                    
+                    self.model = PaddleOCR(**config)
+                    self.is_loaded = True
+                    self.current_config = config
+                    self._log(f"✅ PaddleOCR loaded successfully with config: {config}")
+                    return True
+                except Exception as e:
+                    error_str = str(e)
+                    self._log(f"   Config {i+1} failed: {error_str}", "debug")
+                    
+                    # Clean up on failure
+                    if hasattr(self, 'model'):
+                        del self.model
+                    gc.collect()
+                    continue
+            
+            self._log(f"❌ PaddleOCR failed to load with any configuration", "error")
+            return False
+            
+        except Exception as e:
+            self._log(f"❌ Failed to load paddleocr: {str(e)}", "error")
+            import traceback
+            self._log(traceback.format_exc(), "debug")
+            return False
+    
+    def detect_text(self, image: np.ndarray, **kwargs) -> List[OCRResult]:
+        """Detect text with memory safety measures"""
+        results = []
+        
+        try:
+            if not self.is_loaded:
+                if not self.load_model():
+                    return results
+            
+            import cv2
+            import numpy as np
+            import gc
+            
+            # Memory safety: Ensure image isn't too large
+            h, w = image.shape[:2] if len(image.shape) >= 2 else (0, 0)
+            
+            # Limit image size to prevent memory issues
+            MAX_DIMENSION = 1500
+            if h > MAX_DIMENSION or w > MAX_DIMENSION:
+                scale = min(MAX_DIMENSION/h, MAX_DIMENSION/w)
+                new_h, new_w = int(h*scale), int(w*scale)
+                self._log(f"⚠️ Resizing large image from {w}x{h} to {new_w}x{new_h} for memory safety", "warning")
+                image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)
+                scale_factor = 1/scale
+            else:
+                scale_factor = 1.0
+            
+            # Ensure correct format
+            if len(image.shape) == 2:  # Grayscale
+                image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+            elif len(image.shape) == 4:  # Batch
+                image = image[0]
+            
+            # Ensure uint8 type
+            if image.dtype != np.uint8:
+                if image.max() <= 1.0:
+                    image = (image * 255).astype(np.uint8)
+                else:
+                    image = image.astype(np.uint8)
+            
+            # Make a copy to avoid memory corruption
+            image_copy = image.copy()
+            
+            # Force garbage collection before OCR
+            gc.collect()
+            
+            # Process with timeout protection
+            import signal
+            import threading
+            
+            ocr_results = None
+            ocr_error = None
+            
+            def run_ocr():
+                nonlocal ocr_results, ocr_error
+                try:
+                    ocr_results = self.model.ocr(image_copy)
+                except Exception as e:
+                    ocr_error = e
+            
+            # Run OCR in a separate thread with timeout
+            ocr_thread = threading.Thread(target=run_ocr)
+            ocr_thread.daemon = True
+            ocr_thread.start()
+            ocr_thread.join(timeout=30)  # 30 second timeout
+            
+            if ocr_thread.is_alive():
+                self._log("❌ PaddleOCR timeout - taking too long", "error")
+                return results
+            
+            if ocr_error:
+                raise ocr_error
+            
+            # Parse results
+            results = self._parse_ocr_results(ocr_results)
+            
+            # Scale coordinates back if image was resized
+            if scale_factor != 1.0 and results:
+                for r in results:
+                    x, y, width, height = r.bbox
+                    r.bbox = (int(x*scale_factor), int(y*scale_factor), 
+                            int(width*scale_factor), int(height*scale_factor))
+                    r.vertices = [(int(v[0]*scale_factor), int(v[1]*scale_factor)) 
+                                for v in r.vertices]
+            
+            if results:
+                self._log(f"✅ Detected {len(results)} text regions", "info")
+            else:
+                self._log("No text regions found", "debug")
+            
+            # Clean up
+            del image_copy
+            gc.collect()
+            
+        except Exception as e:
+            error_msg = str(e) if str(e) else type(e).__name__
+            
+            if "memory" in error_msg.lower() or "0x" in error_msg:
+                self._log("❌ Memory access violation in PaddleOCR", "error")
+                self._log("   This is a known Windows issue with PaddleOCR", "info")
+                self._log("   Please switch to EasyOCR or manga-ocr instead", "warning")
+            elif "trace_order.size()" in error_msg:
+                self._log("❌ PaddleOCR internal error", "error")
+                self._log("   Please switch to EasyOCR or manga-ocr", "warning")
+            else:
+                self._log(f"❌ Error in paddleocr detection: {error_msg}", "error")
+            
+            import traceback
+            self._log(traceback.format_exc(), "debug")
+        
+        return results
+    
+    def _parse_ocr_results(self, ocr_results) -> List[OCRResult]:
+        """Parse OCR results safely"""
+        results = []
+        
+        if isinstance(ocr_results, bool) and ocr_results == False:
+            return results
+        
+        if ocr_results is None or not isinstance(ocr_results, list):
+            return results
+        
+        if len(ocr_results) == 0:
+            return results
+        
+        # Handle batch format
+        if isinstance(ocr_results[0], list) and len(ocr_results[0]) > 0:
+            first_item = ocr_results[0][0]
+            if isinstance(first_item, list) and len(first_item) > 0:
+                if isinstance(first_item[0], (list, tuple)) and len(first_item[0]) == 2:
+                    ocr_results = ocr_results[0]
+        
+        # Parse detections
+        for detection in ocr_results:
+            if not detection or isinstance(detection, bool):
+                continue
+            
+            if not isinstance(detection, (list, tuple)) or len(detection) < 2:
+                continue
+            
+            try:
+                bbox_points = detection[0]
+                text_data = detection[1]
+                
+                if not isinstance(bbox_points, (list, tuple)) or len(bbox_points) != 4:
+                    continue
+                
+                if not isinstance(text_data, (tuple, list)) or len(text_data) < 2:
+                    continue
+                
+                text = str(text_data[0]).strip()
+                confidence = float(text_data[1])
+                
+                if not text or confidence < 0.3:
+                    continue
+                
+                xs = [float(p[0]) for p in bbox_points]
+                ys = [float(p[1]) for p in bbox_points]
+                x_min, x_max = min(xs), max(xs)
+                y_min, y_max = min(ys), max(ys)
+                
+                if (x_max - x_min) < 5 or (y_max - y_min) < 5:
+                    continue
+                
+                results.append(OCRResult(
+                    text=text,
+                    bbox=(int(x_min), int(y_min), int(x_max - x_min), int(y_max - y_min)),
+                    confidence=confidence,
+                    vertices=[(int(p[0]), int(p[1])) for p in bbox_points]
+                ))
+                
+            except Exception:
+                continue
+        
+        return results
+
+class DocTROCRProvider(OCRProvider):
+    """DocTR OCR provider"""
+    
+    def check_installation(self) -> bool:
+        """Check if doctr is installed"""
+        try:
+            from doctr.models import ocr_predictor
+            self.is_installed = True
+            return True
+        except ImportError:
+            return False
+    
+    def install(self, progress_callback=None) -> bool:
+        """Install doctr"""
+        pass
+    
+    def load_model(self, **kwargs) -> bool:
+        """Load doctr model"""
+        try:
+            if not self.is_installed and not self.check_installation():
+                self._log("❌ doctr not installed", "error")
+                return False
+            
+            self._log("🔥 Loading DocTR model...")
+            from doctr.models import ocr_predictor
+            
+            # Load pretrained model
+            self.model = ocr_predictor(pretrained=True)
+            self.is_loaded = True
+            
+            self._log("✅ DocTR model loaded successfully")
+            return True
+            
+        except Exception as e:
+            self._log(f"❌ Failed to load doctr: {str(e)}", "error")
+            return False
+    
+    def detect_text(self, image: np.ndarray, **kwargs) -> List[OCRResult]:
+        """Detect text using doctr"""
+        results = []
+        
+        try:
+            if not self.is_loaded:
+                if not self.load_model():
+                    return results
+            
+            from doctr.io import DocumentFile
+            
+            # DocTR expects document format
+            # Convert numpy array to PIL and save temporarily
+            import tempfile
+            import cv2
+            
+            with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp:
+                cv2.imwrite(tmp.name, image)
+                doc = DocumentFile.from_images(tmp.name)
+            
+            # Run OCR
+            result = self.model(doc)
+            
+            # Parse results
+            h, w = image.shape[:2]
+            for page in result.pages:
+                for block in page.blocks:
+                    for line in block.lines:
+                        for word in line.words:
+                            # Handle different geometry formats
+                            geometry = word.geometry
+                            
+                            if len(geometry) == 4:
+                                # Standard format: (x1, y1, x2, y2)
+                                x1, y1, x2, y2 = geometry
+                            elif len(geometry) == 2:
+                                # Alternative format: ((x1, y1), (x2, y2))
+                                (x1, y1), (x2, y2) = geometry
+                            else:
+                                self._log(f"Unexpected geometry format: {geometry}", "warning")
+                                continue
+                            
+                            # Convert relative coordinates to absolute
+                            x1, x2 = int(x1 * w), int(x2 * w)
+                            y1, y2 = int(y1 * h), int(y2 * h)
+                            
+                            results.append(OCRResult(
+                                text=word.value,
+                                bbox=(x1, y1, x2 - x1, y2 - y1),
+                                confidence=word.confidence,
+                                vertices=[(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
+                            ))
+            
+            # Clean up temp file
+            try:
+                os.unlink(tmp.name)
+            except:
+                pass
+            
+            self._log(f"DocTR detected {len(results)} text regions")
+            
+        except Exception as e:
+            self._log(f"Error in doctr detection: {str(e)}", "error")
+            import traceback
+            self._log(traceback.format_exc(), "error")
+        
+        return results
+
+
+class RapidOCRProvider(OCRProvider):
+    """RapidOCR provider for fast local OCR"""
+    
+    def check_installation(self) -> bool:
+        """Check if rapidocr is installed"""
+        try:
+            import rapidocr_onnxruntime
+            self.is_installed = True
+            return True
+        except ImportError:
+            return False
+    
+    def install(self, progress_callback=None) -> bool:
+        """Install rapidocr (requires manual pip install)"""
+        # RapidOCR requires manual installation
+        if progress_callback:
+            progress_callback("RapidOCR requires manual pip installation")
+        self._log("Run: pip install rapidocr-onnxruntime", "info")
+        return False  # Always return False since we can't auto-install
+    
+    def load_model(self, **kwargs) -> bool:
+        """Load RapidOCR model"""
+        try:
+            if not self.is_installed and not self.check_installation():
+                self._log("RapidOCR not installed", "error")
+                return False
+            
+            self._log("Loading RapidOCR...")
+            from rapidocr_onnxruntime import RapidOCR
+            
+            self.model = RapidOCR()
+            self.is_loaded = True
+            
+            self._log("RapidOCR model loaded successfully")
+            return True
+            
+        except Exception as e:
+            self._log(f"Failed to load RapidOCR: {str(e)}", "error")
+            return False
+    
+    def detect_text(self, image: np.ndarray, **kwargs) -> List[OCRResult]:
+        """Detect text using RapidOCR"""
+        if not self.is_loaded:
+            self._log("RapidOCR model not loaded", "error")
+            return []
+        
+        results = []
+        
+        try:
+            # Convert numpy array to PIL Image for RapidOCR
+            if len(image.shape) == 3:
+                # BGR to RGB
+                image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            else:
+                image_rgb = image
+            
+            # RapidOCR expects PIL Image or numpy array
+            ocr_results, _ = self.model(image_rgb)
+            
+            if ocr_results:
+                for result in ocr_results:
+                    # RapidOCR returns [bbox, text, confidence]
+                    bbox_points = result[0]  # 4 corner points
+                    text = result[1]
+                    confidence = float(result[2])
+                    
+                    if not text or not text.strip():
+                        continue
+                    
+                    # Convert 4-point bbox to x,y,w,h format
+                    xs = [point[0] for point in bbox_points]
+                    ys = [point[1] for point in bbox_points]
+                    x_min, x_max = min(xs), max(xs)
+                    y_min, y_max = min(ys), max(ys)
+                    
+                    results.append(OCRResult(
+                        text=text.strip(),
+                        bbox=(int(x_min), int(y_min), int(x_max - x_min), int(y_max - y_min)),
+                        confidence=confidence,
+                        vertices=[(int(p[0]), int(p[1])) for p in bbox_points]
+                    ))
+            
+            self._log(f"Detected {len(results)} text regions")
+            
+        except Exception as e:
+            self._log(f"Error in RapidOCR detection: {str(e)}", "error")
+        
+        return results
+
+class OCRManager:
+    """Manager for multiple OCR providers"""
+    
+    def __init__(self, log_callback=None):
+        self.log_callback = log_callback
+        self.providers = {
+            'custom-api': CustomAPIProvider(log_callback) ,
+            'manga-ocr': MangaOCRProvider(log_callback),
+            'easyocr': EasyOCRProvider(log_callback),
+            'paddleocr': PaddleOCRProvider(log_callback),
+            'doctr': DocTROCRProvider(log_callback),
+            'rapidocr': RapidOCRProvider(log_callback),
+            'Qwen2-VL': Qwen2VL(log_callback)
+        }
+        self.current_provider = None
+        self.stop_flag = None
+        
+    def get_provider(self, name: str) -> Optional[OCRProvider]:
+        """Get OCR provider by name"""
+        return self.providers.get(name)
+    
+    def set_current_provider(self, name: str):
+        """Set current active provider"""
+        if name in self.providers:
+            self.current_provider = name
+            return True
+        return False
+    
+    def check_provider_status(self, name: str) -> Dict[str, bool]:
+        """Check installation and loading status of provider"""
+        provider = self.providers.get(name)
+        if not provider:
+            return {'installed': False, 'loaded': False}
+        
+        result = {
+            'installed': provider.check_installation(),
+            'loaded': provider.is_loaded
+        }
+        if self.log_callback:
+            self.log_callback(f"DEBUG: check_provider_status({name}) returning loaded={result['loaded']}", "debug")
+        return result
+    
+    def install_provider(self, name: str, progress_callback=None) -> bool:
+        """Install a provider"""
+        provider = self.providers.get(name)
+        if not provider:
+            return False
+        
+        return provider.install(progress_callback)
+    
+    def load_provider(self, name: str, **kwargs) -> bool:
+        """Load a provider's model with optional parameters"""
+        provider = self.providers.get(name)
+        if not provider:
+            return False
+        
+        return provider.load_model(**kwargs)  # <-- Passes model_size and any other kwargs
+    
+    def shutdown(self):
+        """Release models/processors/tokenizers for all providers and clear caches."""
+        try:
+            import gc
+            for name, provider in list(self.providers.items()):
+                try:
+                    if hasattr(provider, 'model'):
+                        provider.model = None
+                    if hasattr(provider, 'processor'):
+                        provider.processor = None
+                    if hasattr(provider, 'tokenizer'):
+                        provider.tokenizer = None
+                    if hasattr(provider, 'reader'):
+                        provider.reader = None
+                    if hasattr(provider, 'is_loaded'):
+                        provider.is_loaded = False
+                except Exception:
+                    pass
+            gc.collect()
+            try:
+                import torch
+                torch.cuda.empty_cache()
+            except Exception:
+                pass
+        except Exception:
+            pass
+
+    def detect_text(self, image: np.ndarray, provider_name: str = None, **kwargs) -> List[OCRResult]:
+        """Detect text using specified or current provider"""
+        provider_name = provider_name or self.current_provider
+        if not provider_name:
+            return []
+        
+        provider = self.providers.get(provider_name)
+        if not provider:
+            return []
+        
+        return provider.detect_text(image, **kwargs)
+    
+    def set_stop_flag(self, stop_flag):
+        """Set stop flag for all providers"""
+        self.stop_flag = stop_flag
+        for provider in self.providers.values():
+            if hasattr(provider, 'set_stop_flag'):
+                provider.set_stop_flag(stop_flag)
+    
+    def reset_stop_flags(self):
+        """Reset stop flags for all providers"""
+        for provider in self.providers.values():
+            if hasattr(provider, 'reset_stop_flags'):
+                provider.reset_stop_flags()