File size: 18,303 Bytes
2d5ffb9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import re
import streamlit as st
from modelcards import CardData, ModelCard
from markdownTagExtract import tag_checker,listToString,to_markdown
#from specific_extraction import extract_it


# from persist import persist
#global bytes_data


################################################################
#### Markdown parser logic #################################
################################################################

def file_upload():
    bytes_data = st.session_state.markdown_upload
    return bytes_data


# Sets up the basics
model_card_md = file_upload()  # this is where the new model card will be read in from
model_card_md = model_card_md#.decode("utf-8")
# Does metadata appear in any other format than this?
metadata_re = re.compile("^---(.*?)---", re.DOTALL)
header_re = re.compile("^\s*# (.*)", re.MULTILINE)
subheader_re = re.compile("^\s*## (.*)", re.MULTILINE)
subsubheader_re = re.compile("^\s*### (.*)", re.MULTILINE)
subsubsubheader_re = re.compile("^\s*#### (.*)", re.MULTILINE)
# We could be a lot more flexible on this re.
# We require keys to be bold-faced here.
# We don't have to require bold, as long as it's key:value
# **License:**
# Bold terms use ** or __
# Allows the mixing of ** and __ for bold but eh whatev
key_value_re = re.compile("^\s*([*_]{2}[^*_]+[*_]{2})([^\n]*)", re.MULTILINE)
# Hyphens or stars mark list items.
# Unordered list
list_item_re = re.compile("^\s*[-*+]\s+.*", re.MULTILINE)
# This is the ordered list
enum_re = re.compile("^\s*[0-9].*", re.MULTILINE)
table_re = re.compile("^\s*\|.*", re.MULTILINE)
text_item_re = re.compile("^\s*[A-Za-z(](.*)", re.MULTILINE)
# text_item_re = re.compile("^\s*#\s*.*", re.MULTILINE)
# Allows the mixing of -* and *- for italics but eh whatev
italicized_text_item_re = re.compile(
    "^[_*][^_*\s].*\n?.*[^_*][_*]$", flags=re.MULTILINE
)
tag_re = re.compile("^\s*<.*", re.MULTILINE)
image_re = re.compile("!\[.*\]\(.*\)", re.MULTILINE)


subheader_re_dict = {}
subheader_re_dict[header_re] = subheader_re
subheader_re_dict[subheader_re] = subsubheader_re
subheader_re_dict[subsubheader_re] = subsubsubheader_re


def get_metadata(section_text):
    return list(metadata_re.finditer(section_text))


def find_images(section_text):
    return list(image_re.finditer(section_text))


def find_tags(section_text):
    return list(tag_re.finditer(section_text))


def find_tables(section_text):
    return list(table_re.finditer(section_text))


def find_enums(section_text):
    return list(enum_re.finditer(section_text))


# Extracts the stuff from the .md file
def find_key_values(section_text):
    return list(key_value_re.finditer(section_text))


def find_lists(section_text):
    # Find lists: Those lines starting with either '-' or '*'
    return list(list_item_re.finditer(section_text))


def find_texts(section_text):
    # Find texts: Free writing within a section
    basic_text = list(text_item_re.finditer(section_text))
    ital_text = list(italicized_text_item_re.finditer(section_text))
    free_text = basic_text + ital_text
    return free_text


def find_headers(full_text):
    headers = list(header_re.finditer(full_text))
    subheaders = list(subheader_re.finditer(full_text))
    subsubheaders = list(subsubheader_re.finditer(full_text))
    subsubsubheaders = list(subsubsubheader_re.finditer(full_text))
    return (headers, subheaders, subsubheaders, subsubsubheaders)


metadata_list = get_metadata(model_card_md)
if metadata_list != []:
    metadata_end = metadata_list[-1].span()[-1]
    print("Metadata extracted")
    # Metadata processing can happen here.
    # For now I'm just ignoring it.
    model_card_md = model_card_md[metadata_end:]
else:
    print("No metadata found")

# Matches of all header types
headers_list = find_headers(model_card_md)
print("Headers extracted")
# This type of header (one #)
headers = headers_list[0]
## This type of header (two ##)
subheaders = headers_list[1]
### This type of header
subsubheaders = headers_list[2]
#### This type of header
subsubsubheaders = headers_list[3]

# Matches of bulleted lists
lists_list = find_lists(model_card_md)
print("Bulleted lists extracted")

enums_list = find_enums(model_card_md)
print("Enumerated lists extracted")

key_value_list = find_key_values(model_card_md)
print("Key values extracted")

tables_list = find_tables(model_card_md)
print("Tables extracted")

tags_list = find_tags(model_card_md)
print("Markup tags extracted")

images_list = find_images(model_card_md)
print("Images extracted")

# Matches of free text within a section
texts_list = find_texts(model_card_md)
print("Free text extracted")


# List items have the attribute: value;
# This provides for special handling of those strings,
# allowing us to check if it's a list item in order to split/print ok.
LIST_ITEM = "List item"
KEY_VALUE = "Key: Value"
FREE_TEXT = "Free text"
ENUM_LIST_ITEM = "Enum item"
TABLE_ITEM = "Table item"
TAG_ITEM = "Markup tag"
IMAGE_ITEM = "Image"


def create_span_dict(match_list, match_type):
    """
    Creates a dictionary made out of all the spans.
    This is useful for knowing which types to fill out with what in the app.
    Also useful for checking if there are spans in the .md file that we've missed.
    """
    span_dict = {}
    for match in match_list:
        if len(match.group().strip()) > 0:
            span_dict[(match.span())] = (match.group(), match_type)
    return span_dict


metadata_span_dict = create_span_dict(metadata_list, "Metadata")
# Makes a little dict for each span type
header_span_dict = create_span_dict(headers, "# Header")
subheader_span_dict = create_span_dict(subheaders, "## Subheader")
subsubheader_span_dict = create_span_dict(subsubheaders, "### Subsubheader")
subsubsubheader_span_dict = create_span_dict(subsubsubheaders, "#### Subsubsubheader")
key_value_span_dict = create_span_dict(key_value_list, KEY_VALUE)
lists_span_dict = create_span_dict(lists_list, LIST_ITEM)
enums_span_dict = create_span_dict(enums_list, ENUM_LIST_ITEM)
tables_span_dict = create_span_dict(tables_list, TABLE_ITEM)
tags_span_dict = create_span_dict(tags_list, TAG_ITEM)
images_span_dict = create_span_dict(images_list, IMAGE_ITEM)
texts_span_dict = create_span_dict(texts_list, FREE_TEXT)

# We don't have to have these organized by type necessarily.
# Doing it here for clarity.
all_spans_dict = {}
all_spans_dict["headers"] = header_span_dict
all_spans_dict["subheaders"] = subheader_span_dict
all_spans_dict["subsubheaders"] = subsubheader_span_dict
all_spans_dict["subsubsubheaders"] = subsubsubheader_span_dict
all_spans_dict[LIST_ITEM] = lists_span_dict
all_spans_dict[KEY_VALUE] = key_value_span_dict
all_spans_dict[TABLE_ITEM] = tables_span_dict
all_spans_dict[ENUM_LIST_ITEM] = enums_span_dict
all_spans_dict[TAG_ITEM] = tags_span_dict
all_spans_dict[IMAGE_ITEM] = images_span_dict
all_spans_dict[FREE_TEXT] = texts_span_dict


def get_sorted_spans(spans_dict):
    merged_spans = {}
    for span_dict in spans_dict.values():
        merged_spans.update(span_dict)
    sorted_spans = sorted(merged_spans)
    return sorted_spans, merged_spans


sorted_spans, merged_spans = get_sorted_spans(all_spans_dict)

# Sanity/Parse check. Have we captured all spans in the .md file?
if sorted_spans[0][0] != 0:
    print("FYI, our spans don't start at the start of the file.")
    print("We did not catch this start:")
    print(model_card_md[: sorted_spans[0][0]])

for idx in range(len(sorted_spans) - 1):
    last_span_end = sorted_spans[idx][1]
    new_span_start = sorted_spans[idx + 1][0]
    if new_span_start > last_span_end + 1:
        start_nonparse = sorted_spans[idx]
        end_nonparse = sorted_spans[idx + 1]
        text = model_card_md[start_nonparse[1] : end_nonparse[0]]
        if text.strip():
            print("Found an unparsed span in the file:")
            print(start_nonparse)
            print(" ---> ")
            print(end_nonparse)
            print(text)

# print(header_span_dict)
def section_map_to_help_text(text_retrieved):

    presit_states = {
        "## Model Details": "Give an overview of your model, the relevant research paper, who trained it, etc.",
        "## How to Get Started with the Model": "Give an overview of how to get started with the model",
        "## Limitations and Biases": "Provide an overview of the possible Limitations and Risks that may be associated with this model",
        "## Uses": "Detail the potential uses, intended use and out-of-scope uses for this model",
        "## Training": "Provide an overview of the Training Data and Training Procedure for this model",
        "## Evaluation Results": "Detail the Evaluation Results for this model",
        "## Environmental Impact": "Provide an estimate for the carbon emissions: Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here.",
        "## Citation Information": "How to best cite the model authors",
        "## Glossary": "If relevant, include terms and calculations in this section that can help readers understand the model or model card.",
        "## More Information": "Any additional information",
        "## Model Card Authors": "This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc.",
        "Model Card Contact": "Mediums to use, in order to contact the model creators",
        "##  Technical Specifications": " Additional technical information",
        '## Model Examination': " Examining the model",
    }

    for key in presit_states:
        if key == text_retrieved:
            return presit_states(key)


def section_map_to_persist(text_retrieved):

    presit_states = {
        "Model_details_text": "## Model Details",
        "Model_how_to": "## How to Get Started with the Model",
        "Model_Limits_n_Risks": "## Limitations and Biases",
        "Model_uses": "## Uses",
        "Model_training": "## Training",
        "Model_Eval": "## Evaluation Results",
        "Model_carbon": "## Environmental Impact",
        "Model_cite": "## Citation Information",
        "Glossary": "## Glossary",
        "More_info": "## More Information",
        "Model_card_authors": "## Model Card Authors",
        "Model_card_contact": "## Model Card Contact",
        "Technical_specs": "## Technical specifications",
        "Model_examin": "## Model Examination",
    }

    for key in presit_states:
        if presit_states[key] == text_retrieved:
            return key


def main():
    # st.write('here')
    print(extract_it("Model_details_text"))


def extract_headers():
    headers = {}
    subheaders = {}
    subsubheaders = {}
    subsubsubheaders = {}
    previous = (None, None, None, None)

    for s in sorted_spans:
        if merged_spans[s][1] == "# Header":
            headers[s] = (sorted_spans.index(s), previous[0])
            previous = (sorted_spans.index(s), previous[1], previous[2], previous[3])
        if merged_spans[s][1] == "## Subheader":
            subheaders[s] = (sorted_spans.index(s), previous[1])
            previous = (previous[0], sorted_spans.index(s), previous[2], previous[3])
        if merged_spans[s][1] == "### Subsubheader":
            subsubheaders[s] = (sorted_spans.index(s), previous[2])
            previous = (previous[0], previous[1], sorted_spans.index(s), previous[3])
        if merged_spans[s][1] == "#### Subsubsubheader":
            subsubsubheaders[s] = (sorted_spans.index(s), previous[3])
            previous = (previous[0], previous[1], previous[2], sorted_spans.index(s))

    return headers, subheaders, subsubheaders, subsubsubheaders


def stringify():
    headers, subheaders, subsubheaders, subsubsubheaders = extract_headers()
    headers_strings = {}
    subheaders_strings = {}
    subsubheaders_strings = {}
    subsubsubheaders_strings = {}

    first = None
    for i in headers:
        if headers[i][1] == None:
            continue
        sub_spans = sorted_spans[headers[i][1] : headers[i][0]]
        lines = []
        for x in sub_spans:
            lines.append(merged_spans[x][0])
        try:
            name = lines[0]
        except:
            name = "Model Details"
        lines = "".join(lines)
        # print(merged_spans[i][0] + "-------------------")
        # print(lines)
        headers_strings[
            name.replace("\n# ", "")
            .replace("    ", "")
            .replace("  ", "")
            .replace("\n", "")
            .replace("{{", "")
            .replace("}}", "")
        ] = lines
        first = i

    first = None
    for i in subheaders:
        if subheaders[i][1] == None:
            continue
        sub_spans = sorted_spans[subheaders[i][1] : subheaders[i][0]]
        lines = []
        for x in sub_spans:
            if merged_spans[x][1] == "## Subheader" and first == None:
                break
            elif merged_spans[x][1] == "# Header":
                break
            else:
                lines.append(merged_spans[x][0])
        try:
            name = lines[0]
        except:
            name = "Model Details"
        lines = "".join(lines)
        # print(merged_spans[i][0] + "-------------------")
        # print(lines)
        subheaders_strings[
            name.replace("\n# ", "").replace("    ", "").replace("  ", "")
        ] = lines
        first = i

    first = None
    for i in subsubheaders:
        if subsubheaders[i][1] == None:
            continue
        sub_spans = sorted_spans[subsubheaders[i][1] : subsubheaders[i][0]]
        lines = []
        for x in sub_spans:
            if merged_spans[x][1] == "## Subheader" or (
                merged_spans[x][1] == "### Subsubheader" and first == None
            ):
                break
            else:
                lines.append(merged_spans[x][0])
        lines = "".join(lines)

        subsubheaders_strings[
            merged_spans[i][0].replace("\n", "").replace("### ", "").replace("    ", "")
        ] = lines
        first = i

    for i in subsubsubheaders:
        if subsubsubheaders[i][1] == None:
            continue
        sub_spans = sorted_spans[subsubsubheaders[i][1] : subsubsubheaders[i][0]]
        lines = []
        for x in sub_spans:
            if (
                merged_spans[x][1] == "## Subheader"
                or merged_spans[x][1] == "### Subsubheader"
            ):
                break
            else:
                lines.append(merged_spans[x][0])
        lines = "".join(lines)

        subsubsubheaders_strings[
            merged_spans[i][0].replace("#### ", "").replace("**", "").replace("\n", "")
        ] = lines

    return (
        headers_strings,
        subheaders_strings,
        subsubheaders_strings,
        subsubsubheaders_strings,
    )


def extract_it(text_to_retrieve):
    print("Span\t\tType\t\tText")
    print("-------------------------------------")
    found_subheader = False
    current_subheader = " "
    page_state = " "
    help_text = " "
    #st.write("in cs- body here")

    (
        headers_strings,
        subheaders_strings,
        subsubheaders_strings,
        subsubsubheaders_strings,
    ) = stringify()

    h_keys = list(headers_strings.keys())
    sh_keys = list(subheaders_strings.keys())
    ssh_keys = list(subsubheaders_strings.keys())
    sssh_keys = list(subsubsubheaders_strings.keys())

    needed = [
        "model details",
        "howto",
        "limitations",
        "uses",
        "training",
        "evaluation",
        "environmental",
        "citation",
        "glossary",
        "more information",
        "authors",
        "contact",
    ]  # not sure what keyword should be used for citation, howto, and contact
    # info_strings = {
    #     "details": "## Model Details",
    #     "howto": "## How to Get Started with the Model",
    #     "limitations": "## Limitations and Biases",
    #     "uses": "## Uses",
    #     "training": "## Training",
    #     "evaluation": "## Evaluation Results",
    #     "environmental": "## Environmental Impact",
    #     "citation": "## Citation Information",
    #     "glossary": "## Glossary",
    #     "more information": "## More Information",
    #     "authors": "## Model Card Authors",
    #     "contact": "## Model Card Contact",
    # }
    info_strings = {
        "model details": "",
        "howto": "",
        "limitations": "",
        "uses": "",
        "training": "",
        "evaluation": "",
        "environmental": "",
        "citation": "",
        "glossary": "",
        "more information": "",
        "authors": "",
        "contact": "",
    }

    for x in needed:
        for l in h_keys:
            if x in l.lower():
                info_strings[x] = info_strings[x] + headers_strings[l]
        for i in sh_keys:
            if x in i.lower():
                info_strings[x] = info_strings[x] + subheaders_strings[i]
        for z in ssh_keys:
            try:
                if x in z.lower():
                    info_strings[x] = info_strings[x] + subsubheaders_strings[z]
            except:
                continue
        for y in sssh_keys:
            try:
                if x in y.lower():
                    info_strings[x] = info_strings[x] + subsubsubheaders_strings[y]
            except:
                continue

    extracted_info = {
        "Model_details_text": info_strings["model details"],
        "Model_how_to": info_strings["howto"],
        "Model_Limits_n_Risks": info_strings["limitations"],
        "Model_uses": info_strings["uses"],
        "Model_training": info_strings["training"],
        "Model_Eval": info_strings["evaluation"],
        "Model_carbon": info_strings["environmental"],
        "Model_cite": info_strings["citation"],
        "Glossary": info_strings["glossary"],
        "More_info": info_strings["more information"],
        "Model_card_authors": info_strings["authors"],
        "Model_card_contact": info_strings["contact"],
        "Technical_specs": "## Technical specifications",
        "Model_examin": "## Model Examination",
    }

    #text_to_retrieve = "Model_details_text"

    new_t = extracted_info[text_to_retrieve] + " "

    return(new_t)


if __name__ == "__main__":

    main()