import openai import os, sys, json, inspect, glob, tiktoken, shutil, yaml import openpyxl from openpyxl import Workbook, load_workbook import google.generativeai as palm from langchain.chat_models import AzureChatOpenAI from google.oauth2 import service_account currentdir = os.path.dirname(os.path.abspath( inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.append(parentdir) parentdir = os.path.dirname(parentdir) sys.path.append(parentdir) from general_utils import get_cfg_from_full_path, num_tokens_from_string from embeddings_db import VoucherVisionEmbedding from OCR_google_cloud_vision import detect_text, overlay_boxes_on_image from LLM_chatGPT_3_5 import OCR_to_dict, OCR_to_dict_16k from LLM_PaLM import OCR_to_dict_PaLM # from LLM_Falcon import OCR_to_dict_Falcon from prompts import PROMPT_UMICH_skeleton_all_asia, PROMPT_OCR_Organized, PROMPT_UMICH_skeleton_all_asia_GPT4, PROMPT_OCR_Organized_GPT4, PROMPT_JSON from prompt_catalog import PromptCatalog ''' * For the prefix_removal, the image names have 'MICH-V-' prior to the barcode, so that is used for matching but removed for output. * There is also code active to replace the LLM-predicted "Catalog Number" with the correct number since it is known. The LLMs to usually assign the barcode to the correct field, but it's not needed since it is already known. - Look for ####################### Catalog Number pre-defined ''' ''' Prior to StructuredOutputParser: response = openai.ChatCompletion.create( model=MODEL, temperature = 0, messages=[ {"role": "system", "content": "You are a helpful assistant acting as a transcription expert and your job is to transcribe herbarium specimen labels based on OCR data and reformat it to meet Darwin Core Archive Standards into a Python dictionary based on certain rules."}, {"role": "user", "content": prompt}, ], max_tokens=2048, ) # print the model's response return response.choices[0].message['content'] ''' class VoucherVision(): def __init__(self, cfg, logger, dir_home, path_custom_prompts, Project, Dirs): self.cfg = cfg self.logger = logger self.dir_home = dir_home self.path_custom_prompts = path_custom_prompts self.Project = Project self.Dirs = Dirs self.headers = None self.prompt_version = None self.client = None self.set_API_keys() self.setup() def setup(self): self.logger.name = f'[Transcription]' self.logger.info(f'Setting up OCR and LLM') self.db_name = self.cfg['leafmachine']['project']['embeddings_database_name'] self.path_domain_knowledge = self.cfg['leafmachine']['project']['path_to_domain_knowledge_xlsx'] self.build_new_db = self.cfg['leafmachine']['project']['build_new_embeddings_database'] self.continue_run_from_partial_xlsx = self.cfg['leafmachine']['project']['continue_run_from_partial_xlsx'] self.prefix_removal = self.cfg['leafmachine']['project']['prefix_removal'] self.suffix_removal = self.cfg['leafmachine']['project']['suffix_removal'] self.catalog_numerical_only = self.cfg['leafmachine']['project']['catalog_numerical_only'] self.prompt_version0 = self.cfg['leafmachine']['project']['prompt_version'] self.use_domain_knowledge = self.cfg['leafmachine']['project']['use_domain_knowledge'] self.catalog_name_options = ["Catalog Number", "catalog_number"] self.utility_headers = ["tokens_in", "tokens_out", "path_to_crop","path_to_original","path_to_content","path_to_helper",] self.map_prompt_versions() self.map_dir_labels() self.map_API_options() self.init_embeddings() self.init_transcription_xlsx() '''Logging''' self.logger.info(f'Transcribing dataset --- {self.dir_labels}') self.logger.info(f'Saving transcription batch to --- {self.path_transcription}') self.logger.info(f'Saving individual transcription files to --- {self.Dirs.transcription_ind}') self.logger.info(f'Starting transcription...') self.logger.info(f' LLM MODEL --> {self.version_name}') self.logger.info(f' Using Azure API --> {self.is_azure}') self.logger.info(f' Model name passed to API --> {self.model_name}') self.logger.info(f' API access token is found in PRIVATE_DATA.yaml --> {self.has_key}') def map_API_options(self): self.chat_version = self.cfg['leafmachine']['LLM_version'] version_mapping = { 'GPT 4': ('OpenAI GPT 4', False, 'GPT_4', self.has_key_openai), 'GPT 3.5': ('OpenAI GPT 3.5', False, 'GPT_3_5', self.has_key_openai), 'Azure GPT 3.5': ('(Azure) OpenAI GPT 3.5', True, 'Azure_GPT_3_5', self.has_key_azure_openai), 'Azure GPT 4': ('(Azure) OpenAI GPT 4', True, 'Azure_GPT_4', self.has_key_azure_openai), 'PaLM 2': ('Google PaLM 2', None, None, self.has_key_palm2) } if self.chat_version not in version_mapping: supported_LLMs = ", ".join(version_mapping.keys()) raise Exception(f"Unsupported LLM: {self.chat_version}. Requires one of: {supported_LLMs}") self.version_name, self.is_azure, self.model_name, self.has_key = version_mapping[self.chat_version] def map_prompt_versions(self): self.prompt_version_map = { "Version 1": "prompt_v1_verbose", "Version 1 No Domain Knowledge": "prompt_v1_verbose_noDomainKnowledge", "Version 2": "prompt_v2_json_rules", "Version 1 PaLM 2": 'prompt_v1_palm2', "Version 1 PaLM 2 No Domain Knowledge": 'prompt_v1_palm2_noDomainKnowledge', "Version 2 PaLM 2": 'prompt_v2_palm2', } self.prompt_version = self.prompt_version_map.get(self.prompt_version0, self.path_custom_prompts) self.is_predefined_prompt = self.is_in_prompt_version_map(self.prompt_version) def is_in_prompt_version_map(self, value): return value in self.prompt_version_map.values() def init_embeddings(self): if self.use_domain_knowledge: self.logger.info(f'*** USING DOMAIN KNOWLEDGE ***') self.logger.info(f'*** Initializing vector embeddings database ***') self.initialize_embeddings() else: self.Voucher_Vision_Embedding = None def map_dir_labels(self): if self.cfg['leafmachine']['use_RGB_label_images']: self.dir_labels = os.path.join(self.Dirs.save_per_annotation_class,'label') else: self.dir_labels = self.Dirs.save_original # Use glob to get all image paths in the directory self.img_paths = glob.glob(os.path.join(self.dir_labels, "*")) def load_rules_config(self): with open(self.path_custom_prompts, 'r') as stream: try: return yaml.safe_load(stream) except yaml.YAMLError as exc: print(exc) return None def generate_xlsx_headers(self): # Extract headers from the 'Dictionary' keys in the JSON template rules xlsx_headers = list(self.rules_config_json['rules']["Dictionary"].keys()) xlsx_headers = xlsx_headers + self.utility_headers return xlsx_headers def init_transcription_xlsx(self): self.HEADERS_v1_n22 = ["Catalog Number","Genus","Species","subspecies","variety","forma","Country","State","County","Locality Name","Min Elevation","Max Elevation","Elevation Units","Verbatim Coordinates","Datum","Cultivated","Habitat","Collectors","Collector Number","Verbatim Date","Date","End Date"] self.HEADERS_v2_n26 = ["catalog_number","genus","species","subspecies","variety","forma","country","state","county","locality_name","min_elevation","max_elevation","elevation_units","verbatim_coordinates","decimal_coordinates","datum","cultivated","habitat","plant_description","collectors","collector_number","determined_by","multiple_names","verbatim_date","date","end_date"] self.HEADERS_v1_n22 = self.HEADERS_v1_n22 + self.utility_headers self.HEADERS_v2_n26 = self.HEADERS_v2_n26 + self.utility_headers # Initialize output file self.path_transcription = os.path.join(self.Dirs.transcription,"transcribed.xlsx") if self.prompt_version in ['prompt_v2_json_rules','prompt_v2_palm2']: self.headers = self.HEADERS_v2_n26 self.headers_used = 'HEADERS_v2_n26' elif self.prompt_version in ['prompt_v1_verbose', 'prompt_v1_verbose_noDomainKnowledge','prompt_v1_palm2', 'prompt_v1_palm2_noDomainKnowledge']: self.headers = self.HEADERS_v1_n22 self.headers_used = 'HEADERS_v1_n22' else: if not self.is_predefined_prompt: # Load the rules configuration self.rules_config_json = self.load_rules_config() # Generate the headers from the configuration self.headers = self.generate_xlsx_headers() # Set the headers used to the dynamically generated headers self.headers_used = 'CUSTOM' else: # If it's a predefined prompt, raise an exception as we don't have further instructions raise ValueError("Predefined prompt is not handled in this context.") self.create_or_load_excel_with_headers(os.path.join(self.Dirs.transcription,"transcribed.xlsx"), self.headers) def pick_model(self, vendor, nt): if vendor == 'GPT_3_5': if nt > 6000: return "gpt-3.5-turbo-16k-0613", True else: return "gpt-3.5-turbo", False if vendor == 'GPT_4': return "gpt-4", False if vendor == 'Azure_GPT_3_5': return "gpt-35-turbo", False if vendor == 'Azure_GPT_4': return "gpt-4", False def create_or_load_excel_with_headers(self, file_path, headers, show_head=False): output_dir_names = ['Archival_Components', 'Config_File', 'Cropped_Images', 'Logs', 'Original_Images', 'Transcription'] self.completed_specimens = [] # Check if the file exists and it's not None if self.continue_run_from_partial_xlsx is not None and os.path.isfile(self.continue_run_from_partial_xlsx): workbook = load_workbook(filename=self.continue_run_from_partial_xlsx) sheet = workbook.active show_head=True # Identify the 'path_to_crop' column try: path_to_crop_col = headers.index('path_to_crop') + 1 path_to_original_col = headers.index('path_to_original') + 1 path_to_content_col = headers.index('path_to_content') + 1 path_to_helper_col = headers.index('path_to_helper') + 1 # self.completed_specimens = list(sheet.iter_cols(min_col=path_to_crop_col, max_col=path_to_crop_col, values_only=True, min_row=2)) except ValueError: print("'path_to_crop' not found in the header row.") path_to_crop = list(sheet.iter_cols(min_col=path_to_crop_col, max_col=path_to_crop_col, values_only=True, min_row=2)) path_to_original = list(sheet.iter_cols(min_col=path_to_original_col, max_col=path_to_original_col, values_only=True, min_row=2)) path_to_content = list(sheet.iter_cols(min_col=path_to_content_col, max_col=path_to_content_col, values_only=True, min_row=2)) path_to_helper = list(sheet.iter_cols(min_col=path_to_helper_col, max_col=path_to_helper_col, values_only=True, min_row=2)) others = [path_to_crop_col, path_to_original_col, path_to_content_col, path_to_helper_col] jsons = [path_to_content_col, path_to_helper_col] for cell in path_to_crop[0]: old_path = cell new_path = file_path for dir_name in output_dir_names: if dir_name in old_path: old_path_parts = old_path.split(dir_name) new_path_parts = new_path.split('Transcription') updated_path = new_path_parts[0] + dir_name + old_path_parts[1] self.completed_specimens.append(os.path.basename(updated_path)) print(f"{len(self.completed_specimens)} images are already completed") ### Copy the JSON files over for colu in jsons: cell = next(sheet.iter_rows(min_row=2, min_col=colu, max_col=colu))[0] old_path = cell.value new_path = file_path old_path_parts = old_path.split('Transcription') new_path_parts = new_path.split('Transcription') updated_path = new_path_parts[0] + 'Transcription' + old_path_parts[1] # Copy files old_dir = os.path.dirname(old_path) new_dir = os.path.dirname(updated_path) # Check if old_dir exists and it's a directory if os.path.exists(old_dir) and os.path.isdir(old_dir): # Check if new_dir exists. If not, create it. if not os.path.exists(new_dir): os.makedirs(new_dir) # Iterate through all files in old_dir and copy each to new_dir for filename in os.listdir(old_dir): shutil.copy2(os.path.join(old_dir, filename), new_dir) # copy2 preserves metadata ### Update the file names for colu in others: for row in sheet.iter_rows(min_row=2, min_col=colu, max_col=colu): for cell in row: old_path = cell.value new_path = file_path for dir_name in output_dir_names: if dir_name in old_path: old_path_parts = old_path.split(dir_name) new_path_parts = new_path.split('Transcription') updated_path = new_path_parts[0] + dir_name + old_path_parts[1] cell.value = updated_path show_head=True else: # Create a new workbook and select the active worksheet workbook = Workbook() sheet = workbook.active # Write headers in the first row for i, header in enumerate(headers, start=1): sheet.cell(row=1, column=i, value=header) self.completed_specimens = [] # Save the workbook workbook.save(file_path) if show_head: print("continue_run_from_partial_xlsx:") for i, row in enumerate(sheet.iter_rows(values_only=True)): print(row) if i == 3: # print the first 5 rows (0-indexed) print("\n") break def add_data_to_excel_from_response(self, path_transcription, response, filename_without_extension, path_to_crop, path_to_content, path_to_helper, nt_in, nt_out): wb = openpyxl.load_workbook(path_transcription) sheet = wb.active # find the next empty row next_row = sheet.max_row + 1 if isinstance(response, str): try: response = json.loads(response) except json.JSONDecodeError: print(f"Failed to parse response: {response}") return # iterate over headers in the first row for i, header in enumerate(sheet[1], start=1): # check if header value is in response keys if (header.value in response) and (header.value not in self.catalog_name_options): ####################### Catalog Number pre-defined # check if the response value is a dictionary if isinstance(response[header.value], dict): # if it is a dictionary, extract the 'value' field cell_value = response[header.value].get('value', '') else: # if it's not a dictionary, use it directly cell_value = response[header.value] try: # write the value to the cell sheet.cell(row=next_row, column=i, value=cell_value) except: sheet.cell(row=next_row, column=i, value=cell_value[0]) elif header.value in self.catalog_name_options: # if self.prefix_removal: # filename_without_extension = filename_without_extension.replace(self.prefix_removal, "") # if self.suffix_removal: # filename_without_extension = filename_without_extension.replace(self.suffix_removal, "") # if self.catalog_numerical_only: # filename_without_extension = self.remove_non_numbers(filename_without_extension) sheet.cell(row=next_row, column=i, value=filename_without_extension) elif header.value == "path_to_crop": sheet.cell(row=next_row, column=i, value=path_to_crop) elif header.value == "path_to_original": if self.cfg['leafmachine']['use_RGB_label_images']: fname = os.path.basename(path_to_crop) base = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(path_to_crop)))) path_to_original = os.path.join(base, 'Original_Images', fname) sheet.cell(row=next_row, column=i, value=path_to_original) else: fname = os.path.basename(path_to_crop) base = os.path.dirname(os.path.dirname(path_to_crop)) path_to_original = os.path.join(base, 'Original_Images', fname) sheet.cell(row=next_row, column=i, value=path_to_original) elif header.value == "path_to_content": sheet.cell(row=next_row, column=i, value=path_to_content) elif header.value == "path_to_helper": sheet.cell(row=next_row, column=i, value=path_to_helper) elif header.value == "tokens_in": sheet.cell(row=next_row, column=i, value=nt_in) elif header.value == "tokens_out": sheet.cell(row=next_row, column=i, value=nt_out) # save the workbook wb.save(path_transcription) def set_API_keys(self): # Access secrets directly from the environment openai_api_key = os.getenv('OPENAI_API_KEY') google_application_credentials = os.getenv('GOOGLE_APPLICATION_CREDENTIALS') palm_api_key = os.getenv('PALM') self.has_key_openai = openai_api_key is not None self.has_key_google_OCR = google_application_credentials is not None self.has_key_palm2 = palm_api_key is not None if self.has_key_google_OCR: # Get the credentials JSON from the environment variable google_credentials_json = os.getenv('GOOGLE_APPLICATION_CREDENTIALS') # Convert the JSON string into a Python dictionary google_credentials_dict = json.loads(google_credentials_json) # Create a credentials object google_credentials = service_account.Credentials.from_service_account_info(google_credentials_dict) # Now, use this `google_credentials` object to authenticate your Google Cloud services # For example, if you are using the Google Vision API, it would look like this: from google.cloud import vision self.client = vision.ImageAnnotatorClient(credentials=google_credentials) if self.has_key_palm2: os.environ['PALM'] = palm_api_key palm.configure(api_key=palm_api_key) if self.has_key_openai: openai.api_key = openai_api_key os.environ["OPENAI_API_KEY"] = openai_api_key def initialize_embeddings(self): '''Loading embedding search __init__(self, db_name, path_domain_knowledge, logger, build_new_db=False, model_name="hkunlp/instructor-xl", device="cuda")''' self.Voucher_Vision_Embedding = VoucherVisionEmbedding(self.db_name, self.path_domain_knowledge, logger=self.logger, build_new_db=self.build_new_db) def clean_catalog_number(self, data, filename_without_extension): #Cleans up the catalog number in data if it's a dict def modify_catalog_key(catalog_key, filename_without_extension, data): # Helper function to apply modifications on catalog number if catalog_key not in data: new_data = {catalog_key: None} data = {**new_data, **data} if self.prefix_removal: filename_without_extension = filename_without_extension.replace(self.prefix_removal, "") if self.suffix_removal: filename_without_extension = filename_without_extension.replace(self.suffix_removal, "") if self.catalog_numerical_only: filename_without_extension = self.remove_non_numbers(data[catalog_key]) data[catalog_key] = filename_without_extension return data if isinstance(data, dict): if self.headers_used == 'HEADERS_v1_n22': return modify_catalog_key("Catalog Number", filename_without_extension, data) elif self.headers_used in ['HEADERS_v2_n26', 'CUSTOM']: return modify_catalog_key("catalog_number", filename_without_extension, data) else: raise ValueError("Invalid headers used.") else: raise TypeError("Data is not of type dict.") def write_json_to_file(self, filepath, data): '''Writes dictionary data to a JSON file.''' with open(filepath, 'w') as txt_file: if isinstance(data, dict): data = json.dumps(data, indent=4) txt_file.write(data) def create_null_json(self): return {} def remove_non_numbers(self, s): return ''.join([char for char in s if char.isdigit()]) def create_null_row(self, filename_without_extension, path_to_crop, path_to_content, path_to_helper): json_dict = {header: '' for header in self.headers} for header, value in json_dict.items(): if header in self.catalog_name_options: if self.prefix_removal: json_dict[header] = filename_without_extension.replace(self.prefix_removal, "") if self.suffix_removal: json_dict[header] = filename_without_extension.replace(self.suffix_removal, "") if self.catalog_numerical_only: json_dict[header] = self.remove_non_numbers(json_dict[header]) elif header == "path_to_crop": json_dict[header] = path_to_crop elif header == "path_to_original": fname = os.path.basename(path_to_crop) base = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(path_to_crop)))) path_to_original = os.path.join(base, 'Original_Images', fname) json_dict[header] = path_to_original elif header == "path_to_content": json_dict[header] = path_to_content elif header == "path_to_helper": json_dict[header] = path_to_helper return json_dict def setup_GPT(self, prompt_version, gpt): Catalog = PromptCatalog() self.logger.info(f'Length of OCR raw -- {len(self.OCR)}') # if prompt_version == 'prompt_v1_verbose': if self.is_predefined_prompt: if self.use_domain_knowledge: # Find a similar example from the domain knowledge domain_knowledge_example = self.Voucher_Vision_Embedding.query_db(self.OCR, 1) similarity= self.Voucher_Vision_Embedding.get_similarity() if prompt_version == 'prompt_v1_verbose': prompt, n_fields, xlsx_headers = Catalog.prompt_v1_verbose(OCR=self.OCR,domain_knowledge_example=domain_knowledge_example,similarity=similarity) else: if prompt_version == 'prompt_v1_verbose_noDomainKnowledge': prompt, n_fields, xlsx_headers = Catalog.prompt_v1_verbose_noDomainKnowledge(OCR=self.OCR) elif prompt_version == 'prompt_v2_json_rules': prompt, n_fields, xlsx_headers = Catalog.prompt_v2_json_rules(OCR=self.OCR) else: prompt, n_fields, xlsx_headers = Catalog.prompt_v2_custom(self.path_custom_prompts, OCR=self.OCR) nt = num_tokens_from_string(prompt, "cl100k_base") self.logger.info(f'Prompt token length --- {nt}') MODEL, use_long_form = self.pick_model(gpt, nt) self.logger.info(f'Waiting for {gpt} API call --- Using {MODEL}') return MODEL, prompt, use_long_form, n_fields, xlsx_headers, nt # def setup_GPT(self, opt, gpt): # if opt == 'dict': # # Find a similar example from the domain knowledge # domain_knowledge_example = self.Voucher_Vision_Embedding.query_db(self.OCR, 1) # similarity= self.Voucher_Vision_Embedding.get_similarity() # self.logger.info(f'Length of OCR raw -- {len(self.OCR)}') # # prompt = PROMPT_UMICH_skeleton_all_asia_GPT4(self.OCR, domain_knowledge_example, similarity) # prompt, n_fields, xlsx_headers = # nt = num_tokens_from_string(prompt, "cl100k_base") # self.logger.info(f'Prompt token length --- {nt}') # MODEL, use_long_form = self.pick_model(gpt, nt) # ### Direct GPT ### # self.logger.info(f'Waiting for {MODEL} API call --- Using chatGPT --- Content') # return MODEL, prompt, use_long_form # elif opt == 'helper': # prompt = PROMPT_OCR_Organized_GPT4(self.OCR) # nt = num_tokens_from_string(prompt, "cl100k_base") # MODEL, use_long_form = self.pick_model(gpt, nt) # self.logger.info(f'Length of OCR raw -- {len(self.OCR)}') # self.logger.info(f'Prompt token length --- {nt}') # self.logger.info(f'Waiting for {MODEL} API call --- Using chatGPT --- Helper') # return MODEL, prompt, use_long_form def use_chatGPT(self, is_azure, progress_report, gpt): total_tokens_in = 0 total_tokens_out = 0 final_JSON_response = None if progress_report is not None: progress_report.set_n_batches(len(self.img_paths)) for i, path_to_crop in enumerate(self.img_paths): if progress_report is not None: progress_report.update_batch(f"Working on image {i+1} of {len(self.img_paths)}") if os.path.basename(path_to_crop) in self.completed_specimens: self.logger.info(f'[Skipping] specimen {os.path.basename(path_to_crop)} already processed') else: filename_without_extension, txt_file_path, txt_file_path_OCR, txt_file_path_OCR_bounds, jpg_file_path_OCR_helper = self.generate_paths(path_to_crop, i) # Use Google Vision API to get OCR # self.OCR = detect_text(path_to_crop) self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- Starting OCR') self.OCR, self.bounds, self.text_to_box_mapping = detect_text(path_to_crop, self.client) self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- Finished OCR') if len(self.OCR) > 0: self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- Creating OCR Overlay Image') self.overlay_image = overlay_boxes_on_image(path_to_crop, self.bounds) self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- Saved OCR Overlay Image') self.write_json_to_file(txt_file_path_OCR, {"OCR":self.OCR}) self.write_json_to_file(txt_file_path_OCR_bounds, {"OCR_Bounds":self.bounds}) self.overlay_image.save(jpg_file_path_OCR_helper) # Setup Dict MODEL, prompt, use_long_form, n_fields, xlsx_headers, nt_in = self.setup_GPT(self.prompt_version, gpt) if is_azure: self.llm.deployment_name = MODEL else: self.llm = None # Send OCR to chatGPT and return formatted dictonary if use_long_form: response_candidate = OCR_to_dict_16k(is_azure, self.logger, MODEL, prompt, self.llm, self.prompt_version) nt_out = num_tokens_from_string(response_candidate, "cl100k_base") else: response_candidate = OCR_to_dict(is_azure, self.logger, MODEL, prompt, self.llm, self.prompt_version) nt_out = num_tokens_from_string(response_candidate, "cl100k_base") else: response_candidate = None nt_out = 0 total_tokens_in += nt_in total_tokens_out += nt_out final_JSON_response0 = self.save_json_and_xlsx(response_candidate, filename_without_extension, path_to_crop, txt_file_path, jpg_file_path_OCR_helper, nt_in, nt_out) if response_candidate is not None: final_JSON_response = final_JSON_response0 self.logger.info(f'Formatted JSON\n{final_JSON_response}') self.logger.info(f'Finished {MODEL} API calls\n') if progress_report is not None: progress_report.reset_batch(f"Batch Complete") try: final_JSON_response = json.loads(final_JSON_response.strip('```').replace('json\n', '', 1).replace('json', '', 1)) except: pass return final_JSON_response, total_tokens_in, total_tokens_out def use_PaLM(self, progress_report): total_tokens_in = 0 total_tokens_out = 0 final_JSON_response = None if progress_report is not None: progress_report.set_n_batches(len(self.img_paths)) for i, path_to_crop in enumerate(self.img_paths): if progress_report is not None: progress_report.update_batch(f"Working on image {i+1} of {len(self.img_paths)}") if os.path.basename(path_to_crop) in self.completed_specimens: self.logger.info(f'[Skipping] specimen {os.path.basename(path_to_crop)} already processed') else: filename_without_extension, txt_file_path, txt_file_path_OCR, txt_file_path_OCR_bounds, jpg_file_path_OCR_helper = self.generate_paths(path_to_crop, i) # Use Google Vision API to get OCR self.OCR, self.bounds, self.text_to_box_mapping = detect_text(path_to_crop, self.client) if len(self.OCR) > 0: self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- Starting OCR') self.OCR = self.OCR.replace("\'", "Minutes").replace('\"', "Seconds") self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- Finished OCR') self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- Creating OCR Overlay Image') self.overlay_image = overlay_boxes_on_image(path_to_crop, self.bounds) self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- Saved OCR Overlay Image') self.write_json_to_file(txt_file_path_OCR, {"OCR":self.OCR}) self.write_json_to_file(txt_file_path_OCR_bounds, {"OCR_Bounds":self.bounds}) self.overlay_image.save(jpg_file_path_OCR_helper) # Send OCR to chatGPT and return formatted dictonary response_candidate, nt_in = OCR_to_dict_PaLM(self.logger, self.OCR, self.prompt_version, self.Voucher_Vision_Embedding) nt_out = num_tokens_from_string(response_candidate, "cl100k_base") else: response_candidate = None nt_out = 0 total_tokens_in += nt_in total_tokens_out += nt_out final_JSON_response0 = self.save_json_and_xlsx(response_candidate, filename_without_extension, path_to_crop, txt_file_path, jpg_file_path_OCR_helper, nt_in, nt_out) if response_candidate is not None: final_JSON_response = final_JSON_response0 self.logger.info(f'Formatted JSON\n{final_JSON_response}') self.logger.info(f'Finished PaLM 2 API calls\n') if progress_report is not None: progress_report.reset_batch(f"Batch Complete") return final_JSON_response, total_tokens_in, total_tokens_out ''' def use_falcon(self, progress_report): for i, path_to_crop in enumerate(self.img_paths): progress_report.update_batch(f"Working on image {i+1} of {len(self.img_paths)}") if os.path.basename(path_to_crop) in self.completed_specimens: self.logger.info(f'[Skipping] specimen {os.path.basename(path_to_crop)} already processed') else: filename_without_extension = os.path.splitext(os.path.basename(path_to_crop))[0] txt_file_path = os.path.join(self.Dirs.transcription_ind, filename_without_extension + '.json') txt_file_path_helper = os.path.join(self.Dirs.transcription_ind_helper, filename_without_extension + '.json') self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- {filename_without_extension}') # Use Google Vision API to get OCR self.OCR, self.bounds, self.text_to_box_mapping = detect_text(path_to_crop) if len(self.OCR) > 0: self.OCR = self.OCR.replace("\'", "Minutes").replace('\"', "Seconds") # Send OCR to Falcon and return formatted dictionary response = OCR_to_dict_Falcon(self.logger, self.OCR, self.Voucher_Vision_Embedding) # response_helper = OCR_to_helper_Falcon(self.logger, OCR) # Assuming you have a similar helper function for Falcon response_helper = None self.logger.info(f'Finished Falcon API calls\n') else: response = None if (response is not None) and (response_helper is not None): # Save transcriptions to json files self.write_json_to_file(txt_file_path, response) # self.write_json_to_file(txt_file_path_helper, response_helper) # add to the xlsx file self.add_data_to_excel_from_response(self.path_transcription, response, filename_without_extension, path_to_crop, txt_file_path, txt_file_path_helper) progress_report.reset_batch() ''' def generate_paths(self, path_to_crop, i): filename_without_extension = os.path.splitext(os.path.basename(path_to_crop))[0] txt_file_path = os.path.join(self.Dirs.transcription_ind, filename_without_extension + '.json') txt_file_path_OCR = os.path.join(self.Dirs.transcription_ind_OCR, filename_without_extension + '.json') txt_file_path_OCR_bounds = os.path.join(self.Dirs.transcription_ind_OCR_bounds, filename_without_extension + '.json') jpg_file_path_OCR_helper = os.path.join(self.Dirs.transcription_ind_OCR_helper, filename_without_extension + '.jpg') self.logger.info(f'Working on {i+1}/{len(self.img_paths)} --- {filename_without_extension}') return filename_without_extension, txt_file_path, txt_file_path_OCR, txt_file_path_OCR_bounds, jpg_file_path_OCR_helper def save_json_and_xlsx(self, response, filename_without_extension, path_to_crop, txt_file_path, jpg_file_path_OCR_helper, nt_in, nt_out): if response is None: response = self.create_null_json() self.write_json_to_file(txt_file_path, response) # Then add the null info to the spreadsheet response_null = self.create_null_row(filename_without_extension, path_to_crop, txt_file_path, jpg_file_path_OCR_helper) self.add_data_to_excel_from_response(self.path_transcription, response_null, filename_without_extension, path_to_crop, txt_file_path, jpg_file_path_OCR_helper, nt_in=0, nt_out=0) ### Set completed JSON else: response = self.clean_catalog_number(response, filename_without_extension) self.write_json_to_file(txt_file_path, response) # add to the xlsx file self.add_data_to_excel_from_response(self.path_transcription, response, filename_without_extension, path_to_crop, txt_file_path, jpg_file_path_OCR_helper, nt_in, nt_out) return response def process_specimen_batch(self, progress_report): try: if self.has_key: if self.model_name: final_json_response, total_tokens_in, total_tokens_out = self.use_chatGPT(self.is_azure, progress_report, self.model_name) else: final_json_response, total_tokens_in, total_tokens_out = self.use_PaLM(progress_report) return final_json_response, total_tokens_in, total_tokens_out else: self.logger.info(f'No API key found for {self.version_name}') raise Exception(f"No API key found for {self.version_name}") except: if progress_report is not None: progress_report.reset_batch(f"Batch Failed") self.logger.error("LLM call failed. Ending batch. process_specimen_batch()") for handler in self.logger.handlers[:]: handler.close() self.logger.removeHandler(handler) raise def process_specimen_batch_OCR_test(self, path_to_crop): for img_filename in os.listdir(path_to_crop): img_path = os.path.join(path_to_crop, img_filename) self.OCR, self.bounds, self.text_to_box_mapping = detect_text(img_path, self.client) def space_saver(cfg, Dirs, logger): dir_out = cfg['leafmachine']['project']['dir_output'] run_name = Dirs.run_name path_project = os.path.join(dir_out, run_name) if cfg['leafmachine']['project']['delete_temps_keep_VVE']: logger.name = '[DELETE TEMP FILES]' logger.info("Deleting temporary files. Keeping files required for VoucherVisionEditor.") delete_dirs = ['Archival_Components', 'Config_File'] for d in delete_dirs: path_delete = os.path.join(path_project, d) if os.path.exists(path_delete): shutil.rmtree(path_delete) elif cfg['leafmachine']['project']['delete_all_temps']: logger.name = '[DELETE TEMP FILES]' logger.info("Deleting ALL temporary files!") delete_dirs = ['Archival_Components', 'Config_File', 'Original_Images', 'Cropped_Images'] for d in delete_dirs: path_delete = os.path.join(path_project, d) if os.path.exists(path_delete): shutil.rmtree(path_delete) # Delete the transctiption folder, but keep the xlsx transcription_path = os.path.join(path_project, 'Transcription') if os.path.exists(transcription_path): for item in os.listdir(transcription_path): item_path = os.path.join(transcription_path, item) if os.path.isdir(item_path): # if the item is a directory if os.path.exists(item_path): shutil.rmtree(item_path) # delete the directory