File size: 47,435 Bytes
87c3140
806953a
87c3140
 
e91ac58
 
 
5590fea
524a99c
e91ac58
 
 
 
 
 
 
 
 
 
 
524a99c
e91ac58
87c3140
 
 
 
 
 
 
 
 
e91ac58
87c3140
 
e91ac58
87c3140
 
 
 
 
 
 
 
e91ac58
 
524a99c
 
 
 
 
e91ac58
 
87c3140
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e91ac58
 
b8abf64
 
 
 
524a99c
9d06861
b8abf64
 
 
524a99c
b8abf64
e91ac58
b8abf64
 
 
e91ac58
b8abf64
 
 
87c3140
e91ac58
87c3140
 
 
 
e91ac58
87c3140
e91ac58
87c3140
 
 
 
 
 
 
 
 
 
 
b8abf64
e91ac58
 
 
 
524a99c
 
e91ac58
 
 
 
 
b8abf64
87c3140
e91ac58
cac5f9c
e91ac58
 
 
b8abf64
87c3140
e91ac58
 
 
 
 
 
87c3140
b8abf64
87c3140
 
 
 
 
 
 
b8abf64
87c3140
 
 
 
 
 
 
 
 
 
 
 
 
b8abf64
87c3140
 
 
 
 
 
 
 
b8abf64
87c3140
 
e91ac58
 
87c3140
 
 
b8abf64
87c3140
 
 
 
e91ac58
 
 
 
 
 
 
 
87c3140
e91ac58
 
87c3140
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
524a99c
e91ac58
 
87c3140
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e91ac58
 
9d06861
 
524a99c
 
e91ac58
 
b8abf64
e91ac58
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b8abf64
e91ac58
 
9d06861
 
 
 
 
 
87c3140
 
e91ac58
87c3140
e91ac58
 
 
cac5f9c
e91ac58
 
 
9d06861
4d5e173
3a1d033
 
 
4d5e173
7a93196
4d5e173
 
 
 
 
5590fea
4d5e173
87c3140
e91ac58
4d5e173
 
 
 
 
 
e91ac58
4d5e173
 
 
e91ac58
4d5e173
 
 
fdfdfc3
 
 
 
 
 
b8abf64
fdfdfc3
 
 
 
 
 
 
 
 
e91ac58
 
4d5e173
 
 
 
 
 
 
 
 
 
e91ac58
4d5e173
 
7a93196
 
4d5e173
7a93196
 
4d5e173
 
 
 
 
 
e91ac58
 
4d5e173
 
 
 
 
 
 
 
 
 
 
 
 
 
e91ac58
4d5e173
 
e91ac58
 
7a93196
b8abf64
7a93196
 
e91ac58
4d5e173
 
 
 
 
 
 
 
7a93196
e91ac58
 
4d5e173
 
 
 
 
7a93196
 
e91ac58
93fd830
4d5e173
 
 
 
 
7a93196
4d5e173
87c3140
4d5e173
87c3140
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e91ac58
87c3140
 
 
 
 
 
 
 
 
 
9f288f7
87c3140
 
e91ac58
 
 
87c3140
e91ac58
87c3140
 
 
e91ac58
87c3140
 
 
e91ac58
87c3140
 
 
 
 
 
 
 
 
 
e91ac58
 
 
 
 
 
 
 
 
 
 
 
 
 
87c3140
e91ac58
87c3140
e91ac58
 
 
524a99c
e91ac58
 
87c3140
e91ac58
524a99c
e91ac58
 
87c3140
e91ac58
 
 
87c3140
e91ac58
 
87c3140
e91ac58
 
 
87c3140
e91ac58
 
87c3140
e91ac58
 
87c3140
e91ac58
 
 
 
87c3140
e91ac58
 
 
 
 
 
87c3140
e91ac58
 
 
87c3140
e91ac58
 
 
 
 
 
 
9d06861
 
 
87c3140
e91ac58
 
 
87c3140
e91ac58
 
87c3140
e91ac58
 
 
87c3140
e91ac58
 
87c3140
9d06861
e91ac58
524a99c
e91ac58
87c3140
e91ac58
 
 
 
 
 
 
 
 
87c3140
e91ac58
 
87c3140
e91ac58
9d06861
e91ac58
 
9d06861
e91ac58
 
9d06861
e91ac58
 
 
 
9d06861
e91ac58
9d06861
e91ac58
9d06861
87c3140
e91ac58
 
 
 
 
 
 
87c3140
9d06861
87c3140
e91ac58
87c3140
e91ac58
 
 
 
 
 
87c3140
e91ac58
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
87c3140
e91ac58
 
 
 
 
 
 
 
 
 
 
87c3140
e91ac58
 
 
87c3140
e91ac58
 
87c3140
 
 
 
e91ac58
87c3140
e91ac58
87c3140
 
e91ac58
 
 
 
 
 
87c3140
e91ac58
 
 
 
 
 
9d06861
 
e91ac58
 
524a99c
e91ac58
 
524a99c
e91ac58
87c3140
e91ac58
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
87c3140
 
 
 
 
 
 
9d06861
 
87c3140
 
 
9d06861
87c3140
e91ac58
524a99c
87c3140
e91ac58
 
 
87c3140
 
 
 
524a99c
87c3140
 
 
 
 
 
524a99c
87c3140
 
e91ac58
 
 
 
 
 
87c3140
e91ac58
 
 
 
 
 
 
 
 
87c3140
 
e91ac58
87c3140
 
e91ac58
 
 
 
 
 
 
806953a
 
 
 
87c3140
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
import openai
import os, json, glob, shutil, yaml, torch, logging
import openpyxl
from openpyxl import Workbook, load_workbook
import vertexai
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from langchain_openai import AzureChatOpenAI
from google.oauth2 import service_account
from transformers import AutoTokenizer, AutoModel

from vouchervision.LLM_OpenAI import OpenAIHandler
from vouchervision.LLM_GooglePalm2 import GooglePalm2Handler
from vouchervision.LLM_GoogleGemini import GoogleGeminiHandler
from vouchervision.LLM_MistralAI import MistralHandler
from vouchervision.LLM_local_cpu_MistralAI import LocalCPUMistralHandler
from vouchervision.LLM_local_MistralAI import LocalMistralHandler 
from vouchervision.utils_LLM import remove_colons_and_double_apostrophes
from vouchervision.prompt_catalog import PromptCatalog
from vouchervision.model_maps import ModelMaps
from vouchervision.general_utils import get_cfg_from_full_path
from vouchervision.OCR_google_cloud_vision import OCREngine 

'''
* 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
'''


    
class VoucherVision():

    def __init__(self, cfg, logger, dir_home, path_custom_prompts, Project, Dirs, is_hf):
        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.is_hf = is_hf

        self.trOCR_model_version = "microsoft/trocr-large-handwritten"
        # self.trOCR_model_version = "microsoft/trocr-base-handwritten"
        # self.trOCR_model_version = "dh-unibe/trocr-medieval-escriptmask" # NOPE
        # self.trOCR_model_version = "dh-unibe/trocr-kurrent" # NOPE
        # self.trOCR_model_version = "DunnBC22/trocr-base-handwritten-OCR-handwriting_recognition_v2" # NOPE
        self.trOCR_processor = None
        self.trOCR_model = 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", "catalogNumber"]

        self.geo_headers = ["GEO_override_OCR", "GEO_method", "GEO_formatted_full_string", "GEO_decimal_lat",
                       "GEO_decimal_long","GEO_city", "GEO_county", "GEO_state",
                       "GEO_state_code", "GEO_country", "GEO_country_code", "GEO_continent",]
        
        self.usage_headers = ["current_time", "inference_time_s", "tool_time_s","max_cpu", "max_ram_gb", "n_gpus", "max_gpu_load", "max_gpu_vram_gb","total_gpu_vram_gb","capability_score",]
        
        self.wfo_headers = ["WFO_override_OCR", "WFO_exact_match","WFO_exact_match_name","WFO_best_match","WFO_candidate_names","WFO_placement"]
        self.wfo_headers_no_lists = ["WFO_override_OCR", "WFO_exact_match","WFO_exact_match_name","WFO_best_match","WFO_placement"]
        
        self.utility_headers = ["filename"] + self.wfo_headers + self.geo_headers + self.usage_headers + ["run_name", "prompt", "LLM", "tokens_in", "tokens_out", "path_to_crop","path_to_original","path_to_content","path_to_helper",]
                                # "WFO_override_OCR", "WFO_exact_match","WFO_exact_match_name","WFO_best_match","WFO_candidate_names","WFO_placement",
                                
                                # "GEO_override_OCR", "GEO_method", "GEO_formatted_full_string", "GEO_decimal_lat",
                                # "GEO_decimal_long","GEO_city", "GEO_county", "GEO_state",
                                # "GEO_state_code", "GEO_country", "GEO_country_code", "GEO_continent",
                                
                                # "tokens_in", "tokens_out", "path_to_crop","path_to_original","path_to_content","path_to_helper",]
        
        # WFO_candidate_names is separate, bc it may be type --> list

        self.do_create_OCR_helper_image = self.cfg['leafmachine']['do_create_OCR_helper_image']

        self.map_prompt_versions()
        self.map_dir_labels()
        self.map_API_options()
        # self.init_embeddings()
        self.init_transcription_xlsx()
        self.init_trOCR_model()

        '''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 init_trOCR_model(self):
        lgr = logging.getLogger('transformers')
        lgr.setLevel(logging.ERROR)
        
        self.trOCR_processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") # usually just the "microsoft/trocr-base-handwritten"
        self.trOCR_model = VisionEncoderDecoderModel.from_pretrained(self.trOCR_model_version) # This matches the model
        
        # Check for GPU availability
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.trOCR_model.to(self.device)


    def map_API_options(self):
        self.chat_version = self.cfg['leafmachine']['LLM_version']

        # Get the required values from ModelMaps
        self.model_name = ModelMaps.get_version_mapping_cost(self.chat_version)
        self.is_azure = ModelMaps.get_version_mapping_is_azure(self.chat_version)
        self.has_key = ModelMaps.get_version_has_key(self.chat_version, self.has_key_openai, self.has_key_azure_openai, self.has_key_google_application_credentials, self.has_key_mistral)

        # Check if the version is supported
        if self.model_name is None:
            supported_LLMs = ", ".join(ModelMaps.get_models_gui_list())
            raise Exception(f"Unsupported LLM: {self.chat_version}. Requires one of: {supported_LLMs}")

        self.version_name = self.chat_version


    def map_prompt_versions(self):
        self.prompt_version_map = {
            "Version 1": "prompt_v1_verbose",
        }
        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 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 = list(self.rules_config_json['rules'].keys())
        xlsx_headers = xlsx_headers + self.utility_headers
        return xlsx_headers


    def init_transcription_xlsx(self):
        # Initialize output file
        self.path_transcription = os.path.join(self.Dirs.transcription,"transcribed.xlsx")
        
        # 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 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, Dirs, path_transcription, response, WFO_record, GEO_record, usage_report, MODEL_NAME_FORMATTED, 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)
            elif header.value == "filename":
                sheet.cell(row=next_row, column=i, value=filename_without_extension)
            elif header.value == "prompt":
                sheet.cell(row=next_row, column=i, value=os.path.basename(self.path_custom_prompts))
            elif header.value == "run_name":
                sheet.cell(row=next_row, column=i, value=Dirs.run_name)

            # "WFO_exact_match","WFO_exact_match_name","WFO_best_match","WFO_candidate_names","WFO_placement"
            elif header.value in self.wfo_headers_no_lists:
                sheet.cell(row=next_row, column=i, value=WFO_record.get(header.value, ''))
            # elif header.value == "WFO_exact_match":
            #     sheet.cell(row=next_row, column=i, value= WFO_record.get("WFO_exact_match",''))
            # elif header.value == "WFO_exact_match_name":
            #     sheet.cell(row=next_row, column=i, value= WFO_record.get("WFO_exact_match_name",''))
            # elif header.value == "WFO_best_match":
            #     sheet.cell(row=next_row, column=i, value= WFO_record.get("WFO_best_match",''))
            # elif header.value == "WFO_placement":
            #     sheet.cell(row=next_row, column=i, value= WFO_record.get("WFO_placement",''))
            elif header.value == "WFO_candidate_names":
                candidate_names = WFO_record.get("WFO_candidate_names", '')
                # Check if candidate_names is a list and convert to a string if it is
                if isinstance(candidate_names, list):
                    candidate_names_str = '|'.join(candidate_names)
                else:
                    candidate_names_str = candidate_names
                sheet.cell(row=next_row, column=i, value=candidate_names_str)
            
            # "GEO_method", "GEO_formatted_full_string", "GEO_decimal_lat", "GEO_decimal_long",
            # "GEO_city", "GEO_county", "GEO_state", "GEO_state_code", "GEO_country", "GEO_country_code", "GEO_continent"
            elif header.value in self.geo_headers:
                sheet.cell(row=next_row, column=i, value=GEO_record.get(header.value, ''))

            elif header.value in self.usage_headers:
                sheet.cell(row=next_row, column=i, value=usage_report.get(header.value, ''))

            elif header.value == "LLM":
                sheet.cell(row=next_row, column=i, value=MODEL_NAME_FORMATTED)

        # save the workbook
        wb.save(path_transcription)
    

    def has_API_key(self, val):
        if val != '':
            return True
        else:
            return False
        

    def get_google_credentials(self): # Also used for google drive
        if self.is_hf:
            creds_json_str = os.getenv('GOOGLE_APPLICATION_CREDENTIALS')
            credentials = service_account.Credentials.from_service_account_info(json.loads(creds_json_str))
            return credentials
        else:
            with open(self.cfg_private['google']['GOOGLE_APPLICATION_CREDENTIALS'], 'r') as file:
                data = json.load(file)
                creds_json_str = json.dumps(data)
                credentials = service_account.Credentials.from_service_account_info(json.loads(creds_json_str))
                os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = creds_json_str
                return credentials
        

    def set_API_keys(self):
        if self.is_hf:
            self.dir_home = os.path.dirname(os.path.dirname(__file__))
            self.path_cfg_private = None
            self.cfg_private = None

            k_openai = os.getenv('OPENAI_API_KEY')
            k_openai_azure = os.getenv('AZURE_API_VERSION')

            k_google_project_id = os.getenv('GOOGLE_PROJECT_ID')
            k_google_location = os.getenv('GOOGLE_LOCATION')
            k_google_application_credentials = os.getenv('GOOGLE_APPLICATION_CREDENTIALS')

            k_mistral = os.getenv('MISTRAL_API_KEY')
            k_here = os.getenv('HERE_API_KEY')
            k_opencage = os.getenv('open_cage_geocode')
        else:
            self.dir_home = os.path.dirname(os.path.dirname(__file__))
            self.path_cfg_private = os.path.join(self.dir_home, 'PRIVATE_DATA.yaml')
            self.cfg_private = get_cfg_from_full_path(self.path_cfg_private)

            k_openai = self.cfg_private['openai']['OPENAI_API_KEY']
            k_openai_azure = self.cfg_private['openai_azure']['OPENAI_API_KEY_AZURE']

            k_google_project_id = self.cfg_private['google']['GOOGLE_PROJECT_ID']
            k_google_location = self.cfg_private['google']['GOOGLE_LOCATION']
            k_google_application_credentials = self.cfg_private['google']['GOOGLE_APPLICATION_CREDENTIALS']
            
            k_mistral = self.cfg_private['mistral']['MISTRAL_API_KEY']
            k_here = self.cfg_private['here']['API_KEY']
            k_opencage = self.cfg_private['open_cage_geocode']['API_KEY']
            


        self.has_key_openai = self.has_API_key(k_openai)
        self.has_key_azure_openai = self.has_API_key(k_openai_azure)
        
        self.has_key_google_project_id = self.has_API_key(k_google_project_id)
        self.has_key_google_location = self.has_API_key(k_google_location)
        self.has_key_google_application_credentials = self.has_API_key(k_google_application_credentials)

        self.has_key_mistral = self.has_API_key(k_mistral)
        self.has_key_here = self.has_API_key(k_here)
        self.has_key_open_cage_geocode = self.has_API_key(k_opencage)

        
        ### Google - OCR, Palm2, Gemini
        if self.has_key_google_application_credentials and self.has_key_google_project_id and self.has_key_google_location:
            if self.is_hf:
                vertexai.init(project=os.getenv('GOOGLE_PROJECT_ID'), location=os.getenv('GOOGLE_LOCATION'), credentials=self.get_google_credentials())
            else:
                vertexai.init(project=k_google_project_id, location=k_google_location, credentials=self.get_google_credentials())

        ### OpenAI
        if self.has_key_openai:
            if self.is_hf:
                openai.api_key = os.getenv('OPENAI_API_KEY')
            else:
                openai.api_key = self.cfg_private['openai']['OPENAI_API_KEY']
                os.environ["OPENAI_API_KEY"] = self.cfg_private['openai']['OPENAI_API_KEY']


        ### OpenAI - Azure
        if self.has_key_azure_openai:
            if self.is_hf:
                # Initialize the Azure OpenAI client
                self.llm = AzureChatOpenAI(
                    deployment_name = 'gpt-35-turbo',#'gpt-35-turbo',
                    openai_api_version = os.getenv('AZURE_API_VERSION'),
                    openai_api_key = os.getenv('AZURE_API_KEY'),
                    azure_endpoint = os.getenv('AZURE_API_BASE'),
                    openai_organization = os.getenv('AZURE_ORGANIZATION'),
                )
                self.has_key_azure_openai = True
                
            else:
                # Initialize the Azure OpenAI client
                self.llm = AzureChatOpenAI(
                    deployment_name = 'gpt-35-turbo',#'gpt-35-turbo',
                    openai_api_version = self.cfg_private['openai_azure']['OPENAI_API_VERSION'],
                    openai_api_key = self.cfg_private['openai_azure']['OPENAI_API_KEY_AZURE'],
                    azure_endpoint = self.cfg_private['openai_azure']['OPENAI_API_BASE'],
                    openai_organization = self.cfg_private['openai_azure']['OPENAI_ORGANIZATION'],
                )
                self.has_key_azure_openai = True
                

        ### Mistral
        if self.has_key_mistral:
            if self.is_hf:
                pass # Already set
            else:
                os.environ['MISTRAL_API_KEY'] = self.cfg_private['mistral']['MISTRAL_API_KEY']


        ### HERE
        if self.has_key_here:
            if self.is_hf:
                pass # Already set
            else:
                os.environ['HERE_APP_ID'] = self.cfg_private['here']['APP_ID']
                os.environ['HERE_API_KEY'] = self.cfg_private['here']['API_KEY']


        ### HERE
        if self.has_key_open_cage_geocode:
            if self.is_hf:
                pass # Already set
            else:
                os.environ['OPENCAGE_API_KEY'] = self.cfg_private['open_cage_geocode']['API_KEY']
                

        
    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("filename", 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, sort_keys=False)
            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 == "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
            elif header == "filename":
                json_dict[header] = filename_without_extension

            # "WFO_exact_match","WFO_exact_match_name","WFO_best_match","WFO_candidate_names","WFO_placement"
            elif header == "WFO_exact_match":
                json_dict[header] =''
            elif header == "WFO_exact_match_name":
                json_dict[header] = ''
            elif header == "WFO_best_match":
                json_dict[header] = ''
            elif header == "WFO_candidate_names":
                json_dict[header] = ''
            elif header == "WFO_placement":
                json_dict[header] = ''
        return json_dict
    

    ##################################################################################################################################
    ##################################################     OCR      ##################################################################
    ##################################################################################################################################
    def perform_OCR_and_save_results(self, image_index, json_report, jpg_file_path_OCR_helper, txt_file_path_OCR, txt_file_path_OCR_bounds):
        self.logger.info(f'Working on {image_index + 1}/{len(self.img_paths)} --- Starting OCR')
        # self.OCR - None

        ### Process_image() runs the OCR for text, handwriting, trOCR AND creates the overlay image
        ocr_google = OCREngine(self.logger, json_report, self.dir_home, self.is_hf, self.path_to_crop, self.cfg, self.trOCR_model_version, self.trOCR_model, self.trOCR_processor, self.device)  
        ocr_google.process_image(self.do_create_OCR_helper_image, self.logger)
        self.OCR = ocr_google.OCR

        self.write_json_to_file(txt_file_path_OCR, ocr_google.OCR_JSON_to_file)
        
        self.logger.info(f'Working on {image_index + 1}/{len(self.img_paths)} --- Finished OCR')

        if len(self.OCR) > 0:
            ocr_google.overlay_image.save(jpg_file_path_OCR_helper)

            OCR_bounds = {}
            if ocr_google.hand_text_to_box_mapping is not None:
                OCR_bounds['OCR_bounds_handwritten'] = ocr_google.hand_text_to_box_mapping

            if ocr_google.normal_text_to_box_mapping is not None:
                OCR_bounds['OCR_bounds_printed'] = ocr_google.normal_text_to_box_mapping

            if ocr_google.trOCR_text_to_box_mapping is not None:
                OCR_bounds['OCR_bounds_trOCR'] = ocr_google.trOCR_text_to_box_mapping

            self.write_json_to_file(txt_file_path_OCR_bounds, OCR_bounds)
            self.logger.info(f'Working on {image_index + 1}/{len(self.img_paths)} --- Saved OCR Overlay Image')
        else:
            pass ########################################################################################################################### fix logic for no OCR

    ##################################################################################################################################
    #######################################################  LLM Switchboard  ########################################################
    ##################################################################################################################################
    def send_to_LLM(self, is_azure, progress_report, json_report, model_name):
        self.n_failed_LLM_calls = 0
        self.n_failed_OCR = 0

        final_JSON_response = None
        final_WFO_record = None
        final_GEO_record = None

        self.initialize_token_counters()
        self.update_progress_report_initial(progress_report)

        MODEL_NAME_FORMATTED = ModelMaps.get_API_name(model_name)
        name_parts = model_name.split("_")
        
        self.setup_JSON_dict_structure()

        Copy_Prompt = PromptCatalog()
        Copy_Prompt.copy_prompt_template_to_new_dir(self.Dirs.transcription_prompt, self.path_custom_prompts)
        
        json_report.set_text(text_main=f'Loading {MODEL_NAME_FORMATTED}')
        json_report.set_JSON({}, {}, {})
        llm_model = self.initialize_llm_model(self.logger, MODEL_NAME_FORMATTED, self.JSON_dict_structure, name_parts, is_azure, self.llm)

        for i, path_to_crop in enumerate(self.img_paths):
            self.update_progress_report_batch(progress_report, i)

            if self.should_skip_specimen(path_to_crop):
                self.log_skipping_specimen(path_to_crop)
                continue

            paths = self.generate_paths(path_to_crop, i)
            self.path_to_crop = path_to_crop

            filename_without_extension, txt_file_path, txt_file_path_OCR, txt_file_path_OCR_bounds, jpg_file_path_OCR_helper, json_file_path_wiki, txt_file_path_ind_prompt = paths
            json_report.set_text(text_main='Starting OCR')
            self.perform_OCR_and_save_results(i, json_report, jpg_file_path_OCR_helper, txt_file_path_OCR, txt_file_path_OCR_bounds)
            json_report.set_text(text_main='Finished OCR')

            if not self.OCR:
                self.n_failed_OCR += 1
                response_candidate = None
                nt_in = 0
                nt_out = 0
            else:
                ### Format prompt
                prompt = self.setup_prompt()
                prompt = remove_colons_and_double_apostrophes(prompt)

                ### Send prompt to chosen LLM
                self.logger.info(f'Waiting for {model_name} API call --- Using {MODEL_NAME_FORMATTED}')

                if 'PALM2' in name_parts:
                    response_candidate, nt_in, nt_out, WFO_record, GEO_record, usage_report = llm_model.call_llm_api_GooglePalm2(prompt, json_report, paths)
                
                elif 'GEMINI' in name_parts:
                    response_candidate, nt_in, nt_out, WFO_record, GEO_record, usage_report = llm_model.call_llm_api_GoogleGemini(prompt, json_report, paths)
                
                elif 'MISTRAL' in name_parts and ('LOCAL' not in name_parts):
                    response_candidate, nt_in, nt_out, WFO_record, GEO_record, usage_report = llm_model.call_llm_api_MistralAI(prompt, json_report, paths)
                
                elif 'LOCAL' in name_parts: 
                    if 'MISTRAL' in name_parts or 'MIXTRAL' in name_parts:
                        if 'CPU' in name_parts:     
                            response_candidate, nt_in, nt_out, WFO_record, GEO_record, usage_report = llm_model.call_llm_local_cpu_MistralAI(prompt, json_report, paths) 
                        else:
                            response_candidate, nt_in, nt_out, WFO_record, GEO_record, usage_report = llm_model.call_llm_local_MistralAI(prompt, json_report, paths) 
                else:
                    response_candidate, nt_in, nt_out, WFO_record, GEO_record, usage_report = llm_model.call_llm_api_OpenAI(prompt, json_report, paths)

            self.n_failed_LLM_calls += 1 if response_candidate is None else 0
                
            ### Estimate n tokens returned
            self.logger.info(f'Prompt tokens IN --- {nt_in}')
            self.logger.info(f'Prompt tokens OUT --- {nt_out}')
                
            self.update_token_counters(nt_in, nt_out)

            final_JSON_response, final_WFO_record, final_GEO_record = self.update_final_response(response_candidate, WFO_record, GEO_record, usage_report, MODEL_NAME_FORMATTED, paths, path_to_crop, nt_in, nt_out)

            self.log_completion_info(final_JSON_response)

            json_report.set_JSON(final_JSON_response, final_WFO_record, final_GEO_record)

        self.update_progress_report_final(progress_report)
        final_JSON_response = self.parse_final_json_response(final_JSON_response)
        return final_JSON_response, final_WFO_record, final_GEO_record, self.total_tokens_in, self.total_tokens_out
    

    ##################################################################################################################################
    ################################################## LLM Helper Funcs ##############################################################
    ##################################################################################################################################
    def initialize_llm_model(self, logger, model_name, JSON_dict_structure, name_parts, is_azure=None, llm_object=None):
        if 'LOCAL'in name_parts:
            if ('MIXTRAL' in name_parts) or ('MISTRAL' in name_parts):
                if 'CPU' in name_parts:
                    return LocalCPUMistralHandler(logger, model_name, JSON_dict_structure)
                else:
                    return LocalMistralHandler(logger, model_name, JSON_dict_structure)
        else:
            if 'PALM2' in name_parts:
                return GooglePalm2Handler(logger, model_name, JSON_dict_structure)
            elif 'GEMINI' in name_parts:
                return GoogleGeminiHandler(logger, model_name, JSON_dict_structure)
            elif 'MISTRAL' in name_parts and ('LOCAL' not in name_parts):
                return MistralHandler(logger, model_name, JSON_dict_structure)
            else:
                return OpenAIHandler(logger, model_name, JSON_dict_structure, is_azure, llm_object)

    def setup_prompt(self):
        Catalog = PromptCatalog()
        prompt, _ = Catalog.prompt_SLTP(self.path_custom_prompts, OCR=self.OCR)
        return prompt
    
    def setup_JSON_dict_structure(self):
        Catalog = PromptCatalog()
        _, self.JSON_dict_structure = Catalog.prompt_SLTP(self.path_custom_prompts, OCR='Text')
    

    def initialize_token_counters(self):
        self.total_tokens_in = 0
        self.total_tokens_out = 0


    def update_progress_report_initial(self, progress_report):
        if progress_report is not None:
            progress_report.set_n_batches(len(self.img_paths))


    def update_progress_report_batch(self, progress_report, batch_index):
        if progress_report is not None:
            progress_report.update_batch(f"Working on image {batch_index + 1} of {len(self.img_paths)}")


    def should_skip_specimen(self, path_to_crop):
        return os.path.basename(path_to_crop) in self.completed_specimens


    def log_skipping_specimen(self, path_to_crop):
        self.logger.info(f'[Skipping] specimen {os.path.basename(path_to_crop)} already processed')

    
    def update_token_counters(self, nt_in, nt_out):
        self.total_tokens_in += nt_in
        self.total_tokens_out += nt_out


    def update_final_response(self, response_candidate, WFO_record, GEO_record, usage_report, MODEL_NAME_FORMATTED, paths, path_to_crop, nt_in, nt_out):
        filename_without_extension, txt_file_path, txt_file_path_OCR, txt_file_path_OCR_bounds, jpg_file_path_OCR_helper, json_file_path_wiki, txt_file_path_ind_prompt = paths
        # Saving the JSON and XLSX files with the response and updating the final JSON response
        if response_candidate is not None:
            final_JSON_response_updated = self.save_json_and_xlsx(self.Dirs, response_candidate, WFO_record, GEO_record, usage_report, MODEL_NAME_FORMATTED, filename_without_extension, path_to_crop, txt_file_path, jpg_file_path_OCR_helper, nt_in, nt_out)
            return final_JSON_response_updated, WFO_record, GEO_record
        else:
            final_JSON_response_updated = self.save_json_and_xlsx(self.Dirs, response_candidate, WFO_record, GEO_record, usage_report, MODEL_NAME_FORMATTED, filename_without_extension, path_to_crop, txt_file_path, jpg_file_path_OCR_helper, nt_in, nt_out)
            return final_JSON_response_updated, WFO_record, GEO_record


    def log_completion_info(self, final_JSON_response):
        self.logger.info(f'Formatted JSON\n{final_JSON_response}')
        self.logger.info(f'Finished API calls\n')


    def update_progress_report_final(self, progress_report):
        if progress_report is not None:
            progress_report.reset_batch("Batch Complete")


    def parse_final_json_response(self, final_JSON_response):
        try:
            return json.loads(final_JSON_response.strip('```').replace('json\n', '', 1).replace('json', '', 1))
        except:
            return final_JSON_response
    
    

    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')
        json_file_path_wiki = os.path.join(self.Dirs.transcription_ind_wiki, filename_without_extension + '.json')
        txt_file_path_ind_prompt = os.path.join(self.Dirs.transcription_ind_prompt, filename_without_extension + '.txt')

        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, json_file_path_wiki, txt_file_path_ind_prompt


    def save_json_and_xlsx(self, Dirs, response, WFO_record, GEO_record, usage_report, MODEL_NAME_FORMATTED, filename_without_extension, path_to_crop, txt_file_path, jpg_file_path_OCR_helper, nt_in, nt_out):
        if response is None:
            response = self.JSON_dict_structure
            # Insert 'filename' as the first key
            response = {'filename': filename_without_extension, **{k: v for k, v in response.items() if k != 'filename'}}
            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(Dirs, self.path_transcription, response_null, WFO_record, GEO_record, usage_report, MODEL_NAME_FORMATTED, 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(Dirs, self.path_transcription, response, WFO_record, GEO_record, usage_report, MODEL_NAME_FORMATTED, 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, json_report, is_real_run=False):
        if not self.has_key:
            self.logger.error(f'No API key found for {self.version_name}')
            raise Exception(f"No API key found for {self.version_name}")

        try:
            if is_real_run:
                progress_report.update_overall(f"Transcribing Labels")

            final_json_response, final_WFO_record, final_GEO_record, total_tokens_in, total_tokens_out = self.send_to_LLM(self.is_azure, progress_report, json_report, self.model_name)
            
            return final_json_response, final_WFO_record, final_GEO_record, total_tokens_in, total_tokens_out

        except Exception as e:
            self.logger.error(f"LLM call failed in process_specimen_batch: {e}")
            if progress_report is not None:
                progress_report.reset_batch(f"Batch Failed")
            self.close_logger_handlers()
            raise


    def close_logger_handlers(self):
        for handler in self.logger.handlers[:]:
            handler.close()
            self.logger.removeHandler(handler)


    # 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)



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