Loading modules/ocr/ocr_llm_api.py +226 −208 Original line number Diff line number Diff line Loading @@ -12,128 +12,128 @@ import numpy as np from .base import register_OCR, OCRBase, TextBlock @register_OCR('llm_ocr') @register_OCR("llm_ocr") class LLM_OCR(OCRBase): lang_map = { 'Auto Detect': None, # OpenAI doesn't explicitly have "Auto Detect" 'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Assamese': 'as', 'Azerbaijani': 'az', 'Bangla': 'bn', 'Basque': 'eu', 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Breton': 'br', 'Bulgarian': 'bg', 'Burmese': 'my', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Cherokee': 'chr', 'Chinese (Simplified)': 'zh-CN', # Updated to OpenAI format 'Chinese (Traditional)': 'zh-TW', # Updated to OpenAI format 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', 'Esperanto': 'eo', 'Estonian': 'et', 'Faroese': 'fo', 'Filipino': 'fil', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', 'Hebrew': 'he', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', 'Interlingua': 'ia', 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jv', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Nepali': 'ne', 'Norwegian': 'no', 'Occitan': 'oc', 'Oriya': 'or', 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese': 'pt', 'Punjabi': 'pa', 'Quechua': 'qu', 'Romanian': 'ro', 'Russian': 'ru', 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian (Cyrillic)': 'sr-Cyrl', 'Serbian (Latin)': 'sr-Latn', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala': 'si', 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', 'Tagalog': 'tl', 'Tajik': 'tg', 'Tamil': 'ta', 'Tatar': 'tt', 'Telugu': 'te', 'Thai': 'th', 'Tibetan': 'bo', 'Tigrinya': 'ti', 'Tongan': 'to', 'Turkish': 'tr', 'Ukrainian': 'uk', 'Urdu': 'ur', 'Uyghur': 'ug', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', 'Zulu': 'zu', "Auto Detect": None, "Afrikaans": "af", "Albanian": "sq", "Amharic": "am", "Arabic": "ar", "Armenian": "hy", "Assamese": "as", "Azerbaijani": "az", "Bangla": "bn", "Basque": "eu", "Belarusian": "be", "Bengali": "bn", "Bosnian": "bs", "Breton": "br", "Bulgarian": "bg", "Burmese": "my", "Catalan": "ca", "Cebuano": "ceb", "Cherokee": "chr", "Chinese (Simplified)": "zh-CN", "Chinese (Traditional)": "zh-TW", "Corsican": "co", "Croatian": "hr", "Czech": "cs", "Danish": "da", "Dutch": "nl", "English": "en", "Esperanto": "eo", "Estonian": "et", "Faroese": "fo", "Filipino": "fil", "Finnish": "fi", "French": "fr", "Frisian": "fy", "Galician": "gl", "Georgian": "ka", "German": "de", "Greek": "el", "Gujarati": "gu", "Haitian Creole": "ht", "Hausa": "ha", "Hawaiian": "haw", "Hebrew": "he", "Hindi": "hi", "Hmong": "hmn", "Hungarian": "hu", "Icelandic": "is", "Igbo": "ig", "Indonesian": "id", "Interlingua": "ia", "Irish": "ga", "Italian": "it", "Japanese": "ja", "Javanese": "jv", "Kannada": "kn", "Kazakh": "kk", "Khmer": "km", "Korean": "ko", "Kurdish": "ku", "Kyrgyz": "ky", "Lao": "lo", "Latin": "la", "Latvian": "lv", "Lithuanian": "lt", "Luxembourgish": "lb", "Macedonian": "mk", "Malagasy": "mg", "Malay": "ms", "Malayalam": "ml", "Maltese": "mt", "Maori": "mi", "Marathi": "mr", "Mongolian": "mn", "Nepali": "ne", "Norwegian": "no", "Occitan": "oc", "Oriya": "or", "Pashto": "ps", "Persian": "fa", "Polish": "pl", "Portuguese": "pt", "Punjabi": "pa", "Quechua": "qu", "Romanian": "ro", "Russian": "ru", "Samoan": "sm", "Scots Gaelic": "gd", "Serbian (Cyrillic)": "sr-Cyrl", "Serbian (Latin)": "sr-Latn", "Shona": "sn", "Sindhi": "sd", "Sinhala": "si", "Slovak": "sk", "Slovenian": "sl", "Somali": "so", "Spanish": "es", "Sundanese": "su", "Swahili": "sw", "Swedish": "sv", "Tagalog": "tl", "Tajik": "tg", "Tamil": "ta", "Tatar": "tt", "Telugu": "te", "Thai": "th", "Tibetan": "bo", "Tigrinya": "ti", "Tongan": "to", "Turkish": "tr", "Ukrainian": "uk", "Urdu": "ur", "Uyghur": "ug", "Uzbek": "uz", "Vietnamese": "vi", "Welsh": "cy", "Xhosa": "xh", "Yiddish": "yi", "Yoruba": "yo", "Zulu": "zu", } popular_models = [ Loading @@ -142,62 +142,56 @@ class LLM_OCR(OCRBase): "OAI: gpt-3.5-turbo", "GGL: gemini-1.5-pro-latest", "GGL: gemini-2.0-flash-exp", "GGL: gemini-2.0-flash" "GGL: gemini-2.0-flash", ] params = { 'provider': { 'type': 'selector', 'options': ['OpenAI', 'Google'], 'value': 'OpenAI', 'description': 'Select the LLM provider.' "provider": { "type": "selector", "options": ["OpenAI", "Google"], "value": "OpenAI", "description": "Select the LLM provider.", }, 'api_key': { 'value': '', 'description': 'Your API key.' "api_key": {"value": "", "description": "Your API key."}, "endpoint": { "value": "", # Default to empty, allowing provider to dictate "description": "Base URL for the API. Leave empty to use provider default.", }, 'endpoint': { 'value': '', # Default to empty, allowing provider to dictate 'description': 'Base URL for the API. Leave empty to use provider default.' "model": { "type": "selector", "options": popular_models, "value": "", # Default to empty, allowing provider to dictate "description": "Select the model to use. Leave empty to use provider default. (Provider prefix indicates the provider).", }, 'model': { 'type': 'selector', 'options': popular_models, 'value': '', # Default to empty, allowing provider to dictate 'description': 'Select the model to use. Leave empty to use provider default. (Provider prefix indicates the provider).' "override_model": { "value": "", "description": "Specify a custom model name to override the selected model.", }, 'override_model': { 'value': '', 'description': 'Specify a custom model name to override the selected model.' "language": { "type": "selector", "options": list(lang_map.keys()), "value": "Auto Detect", "description": "Language for OCR.", }, 'language': { 'type': 'selector', 'options': list(lang_map.keys()), 'value': 'Auto Detect', 'description': 'Language for OCR.' "prompt": { "value": "Recognize the text in this image.", "description": "Default prompt for OCR.", }, 'prompt': { 'value': 'Recognize the text in this image.', 'description': 'Default prompt for OCR.' "system_prompt": { "type": "editor", "value": "", "description": "Optional system prompt to guide the model's behavior.", }, 'system_prompt': { 'type': 'editor', 'value': '', 'description': 'Optional system prompt to guide the model\'s behavior.' "proxy": { "value": "", "description": "Proxy address (e.g., http(s)://user:password@host:port or socks4/5://user:password@host:port)", }, 'proxy': { 'value': '', 'description': 'Proxy address (e.g., http(s)://user:password@host:port or socks4/5://user:password@host:port)' "delay": {"value": 0.0, "description": "Delay in seconds between requests."}, "requests_per_minute": { "value": 0, "description": "Maximum number of requests per minute (0 for no limit).", }, 'delay': { 'value': 0.0, 'description': 'Delay in seconds between requests.' }, 'requests_per_minute': { 'value': 0, 'description': 'Maximum number of requests per minute (0 for no limit).' }, 'description': 'OCR using various LLMs compatible with the OpenAI API.' "description": "OCR using various LLMs compatible with the OpenAI API.", } def __init__(self, **params) -> None: Loading Loading @@ -225,66 +219,64 @@ class LLM_OCR(OCRBase): endpoint = self.endpoint if not endpoint: # If endpoint is empty, use provider default provider = self.provider if provider == 'OpenAI': endpoint = 'https://api.openai.com/v1' elif provider == 'Google': endpoint = 'https://generativelanguage.googleapis.com/v1beta/openai' if provider == "OpenAI": endpoint = "https://api.openai.com/v1" elif provider == "Google": endpoint = "https://generativelanguage.googleapis.com/v1beta/openai" else: endpoint = 'https://api.openai.com/v1' # Default endpoint = "https://api.openai.com/v1" # Default self.client = OpenAI( api_key=self.api_key, base_url=endpoint, http_client=transport api_key=self.api_key, base_url=endpoint, http_client=transport ) @property def provider(self): return self.get_param_value('provider') return self.get_param_value("provider") @property def request_delay(self): try: return float(self.get_param_value('delay')) return float(self.get_param_value("delay")) except (ValueError, TypeError): return 1.0 @property def api_key(self): return self.get_param_value('api_key') return self.get_param_value("api_key") @property def endpoint(self): return self.get_param_value('endpoint') return self.get_param_value("endpoint") @property def model(self): return self.get_param_value('model') return self.get_param_value("model") @property def override_model(self): return self.get_param_value('override_model') return self.get_param_value("override_model") @property def language(self): lang_name = self.get_param_value('language') lang_name = self.get_param_value("language") return self.lang_map.get(lang_name) @property def prompt(self): return self.get_param_value('prompt') return self.get_param_value("prompt") @property def system_prompt(self): return self.get_param_value('system_prompt') return self.get_param_value("system_prompt") @property def proxy(self): return self.get_param_value('proxy') return self.get_param_value("proxy") @property def requests_per_minute(self): return int(self.get_param_value('requests_per_minute')) return int(self.get_param_value("requests_per_minute")) def _respect_delay(self): current_time = time.time() Loading @@ -299,7 +291,9 @@ class LLM_OCR(OCRBase): wait_time = 62 - (current_time - self.minute_start_time) if wait_time > 0: if self.debug_mode: self.logger.info(f'Reached request limit. Waiting {wait_time:.2f} seconds.') self.logger.info( f"Reached request limit. Waiting {wait_time:.2f} seconds." ) time.sleep(wait_time) # Reset the counter and start time after waiting, just in case. self.request_count_minute = 0 Loading @@ -308,12 +302,14 @@ class LLM_OCR(OCRBase): # Handle delay parameter time_since_last_request = current_time - self.last_request_time if self.debug_mode: self.logger.info(f'Time since last request: {time_since_last_request} seconds') self.logger.info( f"Time since last request: {time_since_last_request} seconds" ) if time_since_last_request < self.request_delay: sleep_time = self.request_delay - time_since_last_request if self.debug_mode: self.logger.info(f'Waiting {sleep_time} seconds before next request') self.logger.info(f"Waiting {sleep_time} seconds before next request") time.sleep(sleep_time) self.last_request_time = time.time() Loading @@ -325,7 +321,7 @@ class LLM_OCR(OCRBase): Performs OCR on a base64 encoded image. """ if self.debug_mode: self.logger.debug(f'Starting OCR on image') self.logger.debug(f"Starting OCR on image") self._respect_delay() try: Loading Loading @@ -358,16 +354,16 @@ class LLM_OCR(OCRBase): if not model_name: # If model is also empty, determine from provider provider = self.provider # You might want to set default models for each provider here if provider == 'OpenAI': if provider == "OpenAI": model_name = "gpt-4-vision-preview" elif provider == 'Google': elif provider == "Google": model_name = "gemini-1.5-pro-latest" else: model_name = "gpt-4-vision-preview" # Default # Extract model name without provider prefix if it exists if ': ' in model_name: model_name = model_name.split(': ', 1)[1] if ": " in model_name: model_name = model_name.split(": ", 1)[1] # Log the model being used if self.debug_mode: Loading @@ -381,54 +377,76 @@ class LLM_OCR(OCRBase): if response.choices: full_text = response.choices[0].message.content if full_text is None: # Добавлена проверка на None if self.debug_mode: self.logger.warning("OCR response content is None.") return "" # Возвращаем пустую строку в случае None if self.debug_mode: self.logger.debug(f'OCR result: {full_text}') self.logger.debug(f"OCR result: {full_text}") return full_text else: if self.debug_mode: self.logger.warning('No text found in OCR response') return '' self.logger.warning("No text found in OCR response") return "" except Exception as e: self.logger.error(f"OCR error: {e}") return '' return "" def _ocr_blk_list(self, img: np.ndarray, blk_list: List[TextBlock], *args, **kwargs): def _ocr_blk_list( self, img: np.ndarray, blk_list: List[TextBlock], *args, **kwargs ): """ Processes a list of text blocks in an image. """ im_h, im_w = img.shape[:2] if self.debug_mode: self.logger.debug(f'Image dimensions: {im_h}x{im_w}') self.logger.debug(f"Image dimensions: {im_h}x{im_w}") for blk in blk_list: x1, y1, x2, y2 = blk.xyxy if self.debug_mode: self.logger.debug(f'Processing block: ({x1}, {y1}, {x2}, {y2})') if y2 <= im_h and x2 <= im_w and x1 >= 0 and y1 >= 0 and x1 < x2 and y1 < y2: self.logger.debug(f"Processing block: ({x1}, {y1}, {x2}, {y2})") if ( y2 <= im_h and x2 <= im_w and x1 >= 0 and y1 >= 0 and x1 < x2 and y1 < y2 ): cropped_img = img[y1:y2, x1:x2] # Encode the cropped image to base64 _, buffer = cv2.imencode('.jpg', cropped_img) img_base64 = base64.b64encode(buffer).decode('utf-8') _, buffer = cv2.imencode(".jpg", cropped_img) img_base64 = base64.b64encode(buffer).decode("utf-8") if self.debug_mode: self.logger.debug(f'Cropped image dimensions: {cropped_img.shape}') blk.text = self.ocr(img_base64, prompt_override=kwargs.get('prompt', "")) self.logger.debug(f"Cropped image dimensions: {cropped_img.shape}") blk.text = self.ocr( img_base64, prompt_override=kwargs.get("prompt", "") ) else: if self.debug_mode: self.logger.warning('Invalid text block coordinates') blk.text = '' self.logger.warning("Invalid text block coordinates") blk.text = "" def ocr_img(self, img: np.ndarray, prompt: str = "") -> str: """ Performs OCR on the entire image. """ # Encode the entire image to base64 _, buffer = cv2.imencode('.jpg', img) img_base64 = base64.b64encode(buffer).decode('utf-8') _, buffer = cv2.imencode(".jpg", img) img_base64 = base64.b64encode(buffer).decode("utf-8") return self.ocr(img_base64, prompt_override=prompt) def updateParam(self, param_key: str, param_content): super().updateParam(param_key, param_content) if param_key in ['api_key', 'endpoint', 'proxy', 'provider', 'model', 'override_model']: if param_key in [ "api_key", "endpoint", "proxy", "provider", "model", "override_model", ]: self._initialize_client() Loading
modules/ocr/ocr_llm_api.py +226 −208 Original line number Diff line number Diff line Loading @@ -12,128 +12,128 @@ import numpy as np from .base import register_OCR, OCRBase, TextBlock @register_OCR('llm_ocr') @register_OCR("llm_ocr") class LLM_OCR(OCRBase): lang_map = { 'Auto Detect': None, # OpenAI doesn't explicitly have "Auto Detect" 'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Assamese': 'as', 'Azerbaijani': 'az', 'Bangla': 'bn', 'Basque': 'eu', 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Breton': 'br', 'Bulgarian': 'bg', 'Burmese': 'my', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Cherokee': 'chr', 'Chinese (Simplified)': 'zh-CN', # Updated to OpenAI format 'Chinese (Traditional)': 'zh-TW', # Updated to OpenAI format 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', 'Esperanto': 'eo', 'Estonian': 'et', 'Faroese': 'fo', 'Filipino': 'fil', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', 'Hebrew': 'he', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', 'Interlingua': 'ia', 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jv', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Nepali': 'ne', 'Norwegian': 'no', 'Occitan': 'oc', 'Oriya': 'or', 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese': 'pt', 'Punjabi': 'pa', 'Quechua': 'qu', 'Romanian': 'ro', 'Russian': 'ru', 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian (Cyrillic)': 'sr-Cyrl', 'Serbian (Latin)': 'sr-Latn', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala': 'si', 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', 'Tagalog': 'tl', 'Tajik': 'tg', 'Tamil': 'ta', 'Tatar': 'tt', 'Telugu': 'te', 'Thai': 'th', 'Tibetan': 'bo', 'Tigrinya': 'ti', 'Tongan': 'to', 'Turkish': 'tr', 'Ukrainian': 'uk', 'Urdu': 'ur', 'Uyghur': 'ug', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', 'Zulu': 'zu', "Auto Detect": None, "Afrikaans": "af", "Albanian": "sq", "Amharic": "am", "Arabic": "ar", "Armenian": "hy", "Assamese": "as", "Azerbaijani": "az", "Bangla": "bn", "Basque": "eu", "Belarusian": "be", "Bengali": "bn", "Bosnian": "bs", "Breton": "br", "Bulgarian": "bg", "Burmese": "my", "Catalan": "ca", "Cebuano": "ceb", "Cherokee": "chr", "Chinese (Simplified)": "zh-CN", "Chinese (Traditional)": "zh-TW", "Corsican": "co", "Croatian": "hr", "Czech": "cs", "Danish": "da", "Dutch": "nl", "English": "en", "Esperanto": "eo", "Estonian": "et", "Faroese": "fo", "Filipino": "fil", "Finnish": "fi", "French": "fr", "Frisian": "fy", "Galician": "gl", "Georgian": "ka", "German": "de", "Greek": "el", "Gujarati": "gu", "Haitian Creole": "ht", "Hausa": "ha", "Hawaiian": "haw", "Hebrew": "he", "Hindi": "hi", "Hmong": "hmn", "Hungarian": "hu", "Icelandic": "is", "Igbo": "ig", "Indonesian": "id", "Interlingua": "ia", "Irish": "ga", "Italian": "it", "Japanese": "ja", "Javanese": "jv", "Kannada": "kn", "Kazakh": "kk", "Khmer": "km", "Korean": "ko", "Kurdish": "ku", "Kyrgyz": "ky", "Lao": "lo", "Latin": "la", "Latvian": "lv", "Lithuanian": "lt", "Luxembourgish": "lb", "Macedonian": "mk", "Malagasy": "mg", "Malay": "ms", "Malayalam": "ml", "Maltese": "mt", "Maori": "mi", "Marathi": "mr", "Mongolian": "mn", "Nepali": "ne", "Norwegian": "no", "Occitan": "oc", "Oriya": "or", "Pashto": "ps", "Persian": "fa", "Polish": "pl", "Portuguese": "pt", "Punjabi": "pa", "Quechua": "qu", "Romanian": "ro", "Russian": "ru", "Samoan": "sm", "Scots Gaelic": "gd", "Serbian (Cyrillic)": "sr-Cyrl", "Serbian (Latin)": "sr-Latn", "Shona": "sn", "Sindhi": "sd", "Sinhala": "si", "Slovak": "sk", "Slovenian": "sl", "Somali": "so", "Spanish": "es", "Sundanese": "su", "Swahili": "sw", "Swedish": "sv", "Tagalog": "tl", "Tajik": "tg", "Tamil": "ta", "Tatar": "tt", "Telugu": "te", "Thai": "th", "Tibetan": "bo", "Tigrinya": "ti", "Tongan": "to", "Turkish": "tr", "Ukrainian": "uk", "Urdu": "ur", "Uyghur": "ug", "Uzbek": "uz", "Vietnamese": "vi", "Welsh": "cy", "Xhosa": "xh", "Yiddish": "yi", "Yoruba": "yo", "Zulu": "zu", } popular_models = [ Loading @@ -142,62 +142,56 @@ class LLM_OCR(OCRBase): "OAI: gpt-3.5-turbo", "GGL: gemini-1.5-pro-latest", "GGL: gemini-2.0-flash-exp", "GGL: gemini-2.0-flash" "GGL: gemini-2.0-flash", ] params = { 'provider': { 'type': 'selector', 'options': ['OpenAI', 'Google'], 'value': 'OpenAI', 'description': 'Select the LLM provider.' "provider": { "type": "selector", "options": ["OpenAI", "Google"], "value": "OpenAI", "description": "Select the LLM provider.", }, 'api_key': { 'value': '', 'description': 'Your API key.' "api_key": {"value": "", "description": "Your API key."}, "endpoint": { "value": "", # Default to empty, allowing provider to dictate "description": "Base URL for the API. Leave empty to use provider default.", }, 'endpoint': { 'value': '', # Default to empty, allowing provider to dictate 'description': 'Base URL for the API. Leave empty to use provider default.' "model": { "type": "selector", "options": popular_models, "value": "", # Default to empty, allowing provider to dictate "description": "Select the model to use. Leave empty to use provider default. (Provider prefix indicates the provider).", }, 'model': { 'type': 'selector', 'options': popular_models, 'value': '', # Default to empty, allowing provider to dictate 'description': 'Select the model to use. Leave empty to use provider default. (Provider prefix indicates the provider).' "override_model": { "value": "", "description": "Specify a custom model name to override the selected model.", }, 'override_model': { 'value': '', 'description': 'Specify a custom model name to override the selected model.' "language": { "type": "selector", "options": list(lang_map.keys()), "value": "Auto Detect", "description": "Language for OCR.", }, 'language': { 'type': 'selector', 'options': list(lang_map.keys()), 'value': 'Auto Detect', 'description': 'Language for OCR.' "prompt": { "value": "Recognize the text in this image.", "description": "Default prompt for OCR.", }, 'prompt': { 'value': 'Recognize the text in this image.', 'description': 'Default prompt for OCR.' "system_prompt": { "type": "editor", "value": "", "description": "Optional system prompt to guide the model's behavior.", }, 'system_prompt': { 'type': 'editor', 'value': '', 'description': 'Optional system prompt to guide the model\'s behavior.' "proxy": { "value": "", "description": "Proxy address (e.g., http(s)://user:password@host:port or socks4/5://user:password@host:port)", }, 'proxy': { 'value': '', 'description': 'Proxy address (e.g., http(s)://user:password@host:port or socks4/5://user:password@host:port)' "delay": {"value": 0.0, "description": "Delay in seconds between requests."}, "requests_per_minute": { "value": 0, "description": "Maximum number of requests per minute (0 for no limit).", }, 'delay': { 'value': 0.0, 'description': 'Delay in seconds between requests.' }, 'requests_per_minute': { 'value': 0, 'description': 'Maximum number of requests per minute (0 for no limit).' }, 'description': 'OCR using various LLMs compatible with the OpenAI API.' "description": "OCR using various LLMs compatible with the OpenAI API.", } def __init__(self, **params) -> None: Loading Loading @@ -225,66 +219,64 @@ class LLM_OCR(OCRBase): endpoint = self.endpoint if not endpoint: # If endpoint is empty, use provider default provider = self.provider if provider == 'OpenAI': endpoint = 'https://api.openai.com/v1' elif provider == 'Google': endpoint = 'https://generativelanguage.googleapis.com/v1beta/openai' if provider == "OpenAI": endpoint = "https://api.openai.com/v1" elif provider == "Google": endpoint = "https://generativelanguage.googleapis.com/v1beta/openai" else: endpoint = 'https://api.openai.com/v1' # Default endpoint = "https://api.openai.com/v1" # Default self.client = OpenAI( api_key=self.api_key, base_url=endpoint, http_client=transport api_key=self.api_key, base_url=endpoint, http_client=transport ) @property def provider(self): return self.get_param_value('provider') return self.get_param_value("provider") @property def request_delay(self): try: return float(self.get_param_value('delay')) return float(self.get_param_value("delay")) except (ValueError, TypeError): return 1.0 @property def api_key(self): return self.get_param_value('api_key') return self.get_param_value("api_key") @property def endpoint(self): return self.get_param_value('endpoint') return self.get_param_value("endpoint") @property def model(self): return self.get_param_value('model') return self.get_param_value("model") @property def override_model(self): return self.get_param_value('override_model') return self.get_param_value("override_model") @property def language(self): lang_name = self.get_param_value('language') lang_name = self.get_param_value("language") return self.lang_map.get(lang_name) @property def prompt(self): return self.get_param_value('prompt') return self.get_param_value("prompt") @property def system_prompt(self): return self.get_param_value('system_prompt') return self.get_param_value("system_prompt") @property def proxy(self): return self.get_param_value('proxy') return self.get_param_value("proxy") @property def requests_per_minute(self): return int(self.get_param_value('requests_per_minute')) return int(self.get_param_value("requests_per_minute")) def _respect_delay(self): current_time = time.time() Loading @@ -299,7 +291,9 @@ class LLM_OCR(OCRBase): wait_time = 62 - (current_time - self.minute_start_time) if wait_time > 0: if self.debug_mode: self.logger.info(f'Reached request limit. Waiting {wait_time:.2f} seconds.') self.logger.info( f"Reached request limit. Waiting {wait_time:.2f} seconds." ) time.sleep(wait_time) # Reset the counter and start time after waiting, just in case. self.request_count_minute = 0 Loading @@ -308,12 +302,14 @@ class LLM_OCR(OCRBase): # Handle delay parameter time_since_last_request = current_time - self.last_request_time if self.debug_mode: self.logger.info(f'Time since last request: {time_since_last_request} seconds') self.logger.info( f"Time since last request: {time_since_last_request} seconds" ) if time_since_last_request < self.request_delay: sleep_time = self.request_delay - time_since_last_request if self.debug_mode: self.logger.info(f'Waiting {sleep_time} seconds before next request') self.logger.info(f"Waiting {sleep_time} seconds before next request") time.sleep(sleep_time) self.last_request_time = time.time() Loading @@ -325,7 +321,7 @@ class LLM_OCR(OCRBase): Performs OCR on a base64 encoded image. """ if self.debug_mode: self.logger.debug(f'Starting OCR on image') self.logger.debug(f"Starting OCR on image") self._respect_delay() try: Loading Loading @@ -358,16 +354,16 @@ class LLM_OCR(OCRBase): if not model_name: # If model is also empty, determine from provider provider = self.provider # You might want to set default models for each provider here if provider == 'OpenAI': if provider == "OpenAI": model_name = "gpt-4-vision-preview" elif provider == 'Google': elif provider == "Google": model_name = "gemini-1.5-pro-latest" else: model_name = "gpt-4-vision-preview" # Default # Extract model name without provider prefix if it exists if ': ' in model_name: model_name = model_name.split(': ', 1)[1] if ": " in model_name: model_name = model_name.split(": ", 1)[1] # Log the model being used if self.debug_mode: Loading @@ -381,54 +377,76 @@ class LLM_OCR(OCRBase): if response.choices: full_text = response.choices[0].message.content if full_text is None: # Добавлена проверка на None if self.debug_mode: self.logger.warning("OCR response content is None.") return "" # Возвращаем пустую строку в случае None if self.debug_mode: self.logger.debug(f'OCR result: {full_text}') self.logger.debug(f"OCR result: {full_text}") return full_text else: if self.debug_mode: self.logger.warning('No text found in OCR response') return '' self.logger.warning("No text found in OCR response") return "" except Exception as e: self.logger.error(f"OCR error: {e}") return '' return "" def _ocr_blk_list(self, img: np.ndarray, blk_list: List[TextBlock], *args, **kwargs): def _ocr_blk_list( self, img: np.ndarray, blk_list: List[TextBlock], *args, **kwargs ): """ Processes a list of text blocks in an image. """ im_h, im_w = img.shape[:2] if self.debug_mode: self.logger.debug(f'Image dimensions: {im_h}x{im_w}') self.logger.debug(f"Image dimensions: {im_h}x{im_w}") for blk in blk_list: x1, y1, x2, y2 = blk.xyxy if self.debug_mode: self.logger.debug(f'Processing block: ({x1}, {y1}, {x2}, {y2})') if y2 <= im_h and x2 <= im_w and x1 >= 0 and y1 >= 0 and x1 < x2 and y1 < y2: self.logger.debug(f"Processing block: ({x1}, {y1}, {x2}, {y2})") if ( y2 <= im_h and x2 <= im_w and x1 >= 0 and y1 >= 0 and x1 < x2 and y1 < y2 ): cropped_img = img[y1:y2, x1:x2] # Encode the cropped image to base64 _, buffer = cv2.imencode('.jpg', cropped_img) img_base64 = base64.b64encode(buffer).decode('utf-8') _, buffer = cv2.imencode(".jpg", cropped_img) img_base64 = base64.b64encode(buffer).decode("utf-8") if self.debug_mode: self.logger.debug(f'Cropped image dimensions: {cropped_img.shape}') blk.text = self.ocr(img_base64, prompt_override=kwargs.get('prompt', "")) self.logger.debug(f"Cropped image dimensions: {cropped_img.shape}") blk.text = self.ocr( img_base64, prompt_override=kwargs.get("prompt", "") ) else: if self.debug_mode: self.logger.warning('Invalid text block coordinates') blk.text = '' self.logger.warning("Invalid text block coordinates") blk.text = "" def ocr_img(self, img: np.ndarray, prompt: str = "") -> str: """ Performs OCR on the entire image. """ # Encode the entire image to base64 _, buffer = cv2.imencode('.jpg', img) img_base64 = base64.b64encode(buffer).decode('utf-8') _, buffer = cv2.imencode(".jpg", img) img_base64 = base64.b64encode(buffer).decode("utf-8") return self.ocr(img_base64, prompt_override=prompt) def updateParam(self, param_key: str, param_content): super().updateParam(param_key, param_content) if param_key in ['api_key', 'endpoint', 'proxy', 'provider', 'model', 'override_model']: if param_key in [ "api_key", "endpoint", "proxy", "provider", "model", "override_model", ]: self._initialize_client()
