File size: 27,661 Bytes
0b39ee8
2ac59dc
707efdb
5a5a36e
 
a5a780f
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c3c6a41
5a5a36e
 
b9cb207
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
448407e
 
 
 
a5a780f
5a5a36e
 
 
 
d57a7e2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c39027b
d57a7e2
 
 
 
 
 
 
 
 
 
c39027b
d57a7e2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c39027b
d57a7e2
 
 
5a5a36e
 
 
 
 
a5a780f
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
503029a
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b9cb207
5a5a36e
 
 
 
b9cb207
 
5a5a36e
d57a7e2
 
 
5a5a36e
d57a7e2
 
 
 
 
d981095
 
111e33c
 
 
 
 
d981095
b9cb207
228e920
b9cb207
 
 
 
 
 
5a5a36e
b9cb207
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b9cb207
 
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b9cb207
5a5a36e
 
 
 
 
 
c3c6a41
 
b9cb207
 
 
5a5a36e
 
448407e
 
a5a780f
448407e
a5a780f
448407e
a5a780f
 
 
 
 
 
 
 
 
448407e
a5a780f
 
 
 
 
 
 
 
 
 
b7d7354
a5a780f
fe37605
 
b7d7354
fe37605
a5a780f
 
 
 
 
 
448407e
 
a5a780f
448407e
 
a5a780f
448407e
 
 
 
 
 
 
 
 
 
 
 
 
5a5a36e
 
 
 
c3c6a41
5a5a36e
 
 
 
b9cb207
228e920
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b9cb207
 
 
 
 
 
 
 
 
5a5a36e
 
b9cb207
c3c6a41
 
5a5a36e
 
 
 
 
 
 
 
d57a7e2
5a5a36e
 
 
 
d981095
5a5a36e
 
 
 
 
1aec9d3
d293706
 
5a5a36e
111e33c
046adc3
35b980c
b9cb207
a5a780f
 
18df839
fe37605
18df839
fe37605
e2975a8
75e3b48
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
448407e
 
 
a5a780f
 
 
fe37605
448407e
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b9cb207
c3c6a41
5a5a36e
 
 
 
b9cb207
 
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d57a7e2
 
 
 
 
 
 
 
 
 
 
448407e
d57a7e2
448407e
 
 
b9cb207
5a5a36e
 
 
 
 
 
 
b9cb207
c3c6a41
5a5a36e
 
 
 
b9cb207
 
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2aa7f7b
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
2aa7f7b
5a5a36e
 
 
653f44e
5a5a36e
 
 
 
 
 
2aa7f7b
 
 
 
 
 
 
 
49e6356
2aa7f7b
 
 
 
5a5a36e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
228e920
5a5a36e
3df3fbb
b5fea0f
 
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
import os
os.system("pip install gradio==4.31.5 pydantic==2.7.1 gradio_modal==0.0.3")

import gradio as gr
import pandas as pd
import re
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from gradio_space_ci import enable_space_ci

from src.display.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    FAQ_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    NUMERIC_INTERVALS,
    NUMERIC_MODELSIZE,
    TYPES,
    AutoEvalColumn,
    GroupDtype,
    ModelType,
    fields,
    WeightType,
    Precision,
    ComputeDtype,
    WeightDtype,
    QuantType
)
from src.envs import API, EVAL_REQUESTS_PATH, DYNAMIC_INFO_REPO, DYNAMIC_INFO_FILE_PATH, DYNAMIC_INFO_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO, REPO, GIT_REQUESTS_PATH, GIT_STATUS_PATH, GIT_RESULTS_PATH
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from src.scripts.update_all_request_files import update_dynamic_files
from src.tools.collections import update_collections
from src.tools.plots import (
    create_metric_plot_obj,
    create_plot_df,
    create_scores_df,
)
from gradio_modal import Modal
import plotly.graph_objects as go

selected_indices = []
selected_values = {}

# Start ephemeral Spaces on PRs (see config in README.md)
#enable_space_ci()

precision_to_dtype = {
    "2bit": ["int2"],
    "3bit": ["int3"],
    "4bit": ["int4", "nf4", "fp4"],
    "?": ["?"]
}

current_weightDtype = ["All", "int2", "int3", "int4", "nf4", "fp4", "?"]

# Global variable to store the selected dtypes
selected_dtypes = ["All"]
init_select = False

def quant_update_Weight_Dtype(selected_precisions):
    global current_weightDtype
    if 'βœ– None' in selected_precisions:
        if not any(dtype in ['float16', 'bfloat16', 'float32'] for dtype in current_weightDtype):
            current_weightDtype += ['float16', 'bfloat16', 'float32']
    else:
        if any(dtype in ['float16', 'bfloat16', 'float32'] for dtype in current_weightDtype):
            current_weightDtype = [dtype for dtype in current_weightDtype if dtype not in ['float16', 'bfloat16', 'float32']]
    return gr.Dropdown(choices=current_weightDtype, value="All")


def update_Weight_Dtype(selected_precisions):
    global selected_dtypes
    global current_weightDtype
    global init_select
    init_select = True

    if not selected_precisions:  # If no precision is selected, return "All"
        selected_dtypes = ["All"]
        return gr.Dropdown(choices=["All"], value="All")

    selected_dtypes_set = set()
    for precision in selected_precisions:
        if precision in precision_to_dtype:
            selected_dtypes_set.update(precision_to_dtype[precision])

    
    # Convert set to sorted list to maintain order
    selected_dtypes = sorted(selected_dtypes_set)
    if any(dtype in ['float16', 'bfloat16', 'float32'] for dtype in current_weightDtype) and not any(dtype in ['float16', 'bfloat16', 'float32'] for dtype in selected_dtypes):
        selected_dtypes += ['float16', 'bfloat16', 'float32']
    # Add "All" to the beginning of the list for display purposes
    display_choices = ["All"] + selected_dtypes


    current_weightDtype = display_choices
    return gr.Dropdown(choices=display_choices, value="All")



def restart_space():
    API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)


def init_space(full_init: bool = True):
    
    if full_init:
        try:
            branch = REPO.active_branch.name
            REPO.remotes.origin.pull(branch)
        except Exception as e:
            print(str(e))
            restart_space()

        try:
            print(DYNAMIC_INFO_PATH)
            snapshot_download(
                repo_id=DYNAMIC_INFO_REPO, local_dir=DYNAMIC_INFO_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
            )
        except Exception:
            restart_space()

    raw_data, original_df = get_leaderboard_df(
        results_path=GIT_RESULTS_PATH, 
        requests_path=GIT_STATUS_PATH, 
        dynamic_path=DYNAMIC_INFO_FILE_PATH, 
        cols=COLS, 
        benchmark_cols=BENCHMARK_COLS
    )
    # update_collections(original_df.copy())
    leaderboard_df = original_df.copy()

    plot_df = create_plot_df(create_scores_df(raw_data))

    (
        finished_eval_queue_df,
        running_eval_queue_df,
        pending_eval_queue_df,
    ) = get_evaluation_queue_df(GIT_STATUS_PATH, EVAL_COLS)

    return leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df

leaderboard_df, original_df, plot_df, finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = init_space()

def str_to_bool(value):
    if str(value).lower() == "true":
        return True
    elif str(value).lower() == "false":
        return False
    else:
        return False

# Searching and filtering
def update_table(
    hidden_df: pd.DataFrame,
    columns: list,
    type_query: list,
    precision_query: str,
    size_query: list,
    params_query: list,
    hide_models: list,
    query: str,
    compute_dtype: str,
    weight_dtype: str,
    double_quant: str,
    group_dtype: str
):
    global init_select
    global current_weightDtype


    if selected_dtypes == ['All']:
        weight_dtype = current_weightDtype
    elif weight_dtype == ['All'] or weight_dtype == 'All' or init_select:
        weight_dtype = selected_dtypes
        init_select = False
    else:
        weight_dtype = [weight_dtype]

    if compute_dtype == 'All':
        compute_dtype = ['bfloat16', 'float16', 'int8', 'float32']
    else:
        compute_dtype = [compute_dtype]   
        
    if group_dtype == 'All':
        group_dtype = [-1, 1024, 256, 128, 64, 32]
    else:
        try:
            group_dtype = [int(group_dtype)]
        except ValueError:
            group_dtype = [-1]

    double_quant = [str_to_bool(double_quant)]
    filtered_df = filter_models(df=hidden_df, type_query=type_query, size_query=size_query, precision_query=precision_query, hide_models=hide_models, compute_dtype=compute_dtype, weight_dtype=weight_dtype, double_quant=double_quant, group_dtype=group_dtype, params_query=params_query)
    filtered_df = filter_queries(query, filtered_df)
    df = select_columns(filtered_df, columns)
    return df


def load_query(request: gr.Request):  # triggered only once at startup => read query parameter if it exists
    query = request.query_params.get("query") or ""
    return query, query # return one for the "search_bar", one for a hidden component that triggers a reload only if value has changed


def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
    return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]


def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
    always_here_cols = [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
    dummy_col = [AutoEvalColumn.dummy.name]
    # We use COLS to maintain sorting
    filtered_df = df[
        always_here_cols + [c for c in COLS if c in df.columns and c in columns] + dummy_col
    ]
    return filtered_df


def filter_queries(query: str, filtered_df: pd.DataFrame):
    """Added by Abishek"""
    final_df = []
    if query != "":
        queries = [q.strip() for q in query.split(";")]
        for _q in queries:
            _q = _q.strip()
            if _q != "":
                temp_filtered_df = search_table(filtered_df, _q)
                if len(temp_filtered_df) > 0:
                    final_df.append(temp_filtered_df)
        if len(final_df) > 0:
            filtered_df = pd.concat(final_df)
            filtered_df = filtered_df.drop_duplicates(
                subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
            )

    return filtered_df


def filter_models(
    df: pd.DataFrame, type_query: list, size_query: list, params_query:list, precision_query: list, hide_models: list, compute_dtype: list, weight_dtype: list, double_quant: list, group_dtype: list,
 ) -> pd.DataFrame:
    # Show all models
    if "Private or deleted" in hide_models:
        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
    else:
        filtered_df = df

    if "Contains a merge/moerge" in hide_models:
        filtered_df = filtered_df[filtered_df[AutoEvalColumn.merged.name] == False]

    if "MoE" in hide_models:
        filtered_df = filtered_df[filtered_df[AutoEvalColumn.moe.name] == False]

    if "Flagged" in hide_models:
        filtered_df = filtered_df[filtered_df[AutoEvalColumn.flagged.name] == False]

    type_emoji = [t[0] for t in type_query]
    filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
    
    filtered_df = filtered_df.loc[df[AutoEvalColumn.weight_dtype.name].isin(weight_dtype)]
    filtered_df = filtered_df.loc[df[AutoEvalColumn.compute_dtype.name].isin(compute_dtype)]
    filtered_df = filtered_df.loc[df[AutoEvalColumn.double_quant.name].isin(double_quant)]
    filtered_df = filtered_df.loc[df[AutoEvalColumn.group_size.name].isin(group_dtype)]

    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
    filtered_df = filtered_df.loc[mask]

    numeric_interval_params = pd.IntervalIndex(sorted([NUMERIC_MODELSIZE[s] for s in params_query]))
    params_column_params = pd.to_numeric(df[AutoEvalColumn.model_size.name], errors="coerce")
    mask_params = params_column_params.apply(lambda x: any(numeric_interval_params.contains(x)))
    filtered_df = filtered_df.loc[mask_params]

    return filtered_df

def select(df, data: gr.SelectData):
    global selected_indices
    global selected_values
    
    selected_index = data.index[0]
    if selected_index in selected_indices:
        selected_indices.remove(selected_index)
        
        value = df.iloc[selected_index].iloc[1]
        pattern = r'<a[^>]+>([^<]+)</a>'
        match = re.search(pattern, value)
        if match:
            text_content = match.group(1)
            if text_content in selected_values:
                del selected_values[text_content]
    else:
        selected_indices.append(selected_index)

        value = df.iloc[selected_index].iloc[1]
        pattern = r'<a[^>]+>([^<]+)</a>'
        match = re.search(pattern, value)
        if match:
            text_content = match.group(1)
            selected_values[text_content] = value

    print('selected_values', selected_values, selected_indices)
    return gr.CheckboxGroup(list(selected_values.keys()), value=list(selected_values.keys()))

def init_comparison_data():
    global selected_values
    return gr.CheckboxGroup(list(selected_values.keys()), value=list(selected_values.keys())) 

def generate_spider_chart(df, selected_keys):
    global selected_values
    print('generate_spider_chart', selected_values, selected_keys)
    current_selected_values = [selected_values[key] for key in selected_keys if key in selected_values]
    selected_rows = df[df.iloc[:, 1].isin(current_selected_values)]


    fig = go.Figure()
    for _, row in selected_rows.iterrows():
        fig.add_trace(go.Scatterpolar(
            r=[row['Average ⬆️'], row['ARC-c'], row['ARC-e'], row['Boolq'], row['HellaSwag'], row['Lambada'], row['MMLU'], row['Openbookqa'], row['Piqa'], row['Truthfulqa'], row['Winogrande']],
            theta=['Average ⬆️', 'ARC-c', 'ARC-e', 'Boolq', 'HellaSwag', 'Lambada', 'MMLU', 'Openbookqa', 'Piqa', 'Truthfulqa', 'Winogrande'],
            fill='toself',
            name=str(row['Model'])  
        ))
    fig.update_layout(
        polar=dict(
            radialaxis=dict(
                visible=True,
            )),
        showlegend=True
    )
    
    return fig    

leaderboard_df = filter_models(
    df=leaderboard_df, 
    type_query=[t.to_str(" : ") for t in QuantType], 
    size_query=list(NUMERIC_INTERVALS.keys()), 
    params_query=list(NUMERIC_MODELSIZE.keys()),
    precision_query=[i.value.name for i in Precision],
    hide_models=["Private or deleted", "Contains a merge/moerge", "Flagged"], # Deleted, merges, flagged, MoEs,
    compute_dtype=[i.value.name for i in ComputeDtype],
    weight_dtype=[i.value.name for i in WeightDtype],
    double_quant=[True, False],
    group_dtype=[-1, 1024, 256, 128, 64, 32]
)

demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("πŸ… LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        search_bar = gr.Textbox(
                            placeholder=" πŸ” Search for your model (separate multiple queries with `;`) and press ENTER...",
                            show_label=False,
                            elem_id="search-bar",
                        )
                    with gr.Row():
                        shown_columns = gr.CheckboxGroup(
                            choices=[
                                c.name
                                for c in fields(AutoEvalColumn)
                                if not c.hidden and not c.never_hidden and not c.dummy
                            ],
                            value=[
                                c.name
                                for c in fields(AutoEvalColumn)
                                if c.displayed_by_default and not c.hidden and not c.never_hidden
                            ],
                            label="Select columns to show",
                            elem_id="column-select",
                            interactive=True,
                        )

                    with gr.Row():
                        filter_columns_parameters = gr.CheckboxGroup(
                        label="Model parameters (in billions of parameters)",
                        choices=list(NUMERIC_INTERVALS.keys()),
                        value=list(NUMERIC_INTERVALS.keys()),
                        interactive=True,
                        elem_id="filter-columns-size",
                    )
                    with gr.Row():
                        filter_columns_size = gr.CheckboxGroup(
                        label="Model sizes (GB, int4)",
                        choices=list(NUMERIC_MODELSIZE.keys()),
                        value=list(NUMERIC_MODELSIZE.keys()),
                        interactive=True,
                        elem_id="filter-columns-size",
                    )
                with gr.Column(min_width=320):
                    #with gr.Box(elem_id="box-filter"):
                    filter_columns_type = gr.CheckboxGroup(
                        label="Quantization types",
                        choices=[t.to_str() for t in QuantType],
                        value=[t.to_str() for t in QuantType if t != QuantType.QuantType_None],
                        interactive=True,
                        elem_id="filter-columns-type",  
                    )
                    filter_columns_precision = gr.CheckboxGroup(
                        label="Weight precision",
                        choices=[i.value.name for i in Precision],
                        value=[i.value.name for i in Precision],
                        interactive=True,
                        elem_id="filter-columns-precision",
                    )
                    with gr.Group() as config:
                        # gr.HTML("""<p style='padding-bottom: 0.5rem; color: #6b7280; '>Quantization config</p>""")
                        gr.HTML("""<p style='padding: 0.7rem; background: #fff; margin: 0; color: #6b7280;'>Quantization config</p>""")
                        with gr.Row():
                            filter_columns_computeDtype = gr.Dropdown(choices=[i.value.name for i in ComputeDtype], label="Compute Dtype", multiselect=False, value="All", interactive=True,)
                            filter_columns_weightDtype = gr.Dropdown(choices=[i.value.name for i in WeightDtype], label="Weight Dtype", multiselect=False, value="All", interactive=True,)
                            filter_columns_doubleQuant = gr.Dropdown(choices=["True", "False"], label="Double Quant", multiselect=False, value="False", interactive=True)
                            filter_columns_groupDtype = gr.Dropdown(choices=[i.value.name for i in GroupDtype], label="Group Size", multiselect=False, value="All", interactive=True,)

                    with gr.Row():
                        with gr.Column():
                            model_comparison = gr.CheckboxGroup(label="Accuracy Comparison on Selected Models", choices=list(selected_values.keys()), value=list(selected_values.keys()), interactive=True, elem_id="model_comparison")
                        with gr.Column():
                            spider_btn = gr.Button("Compare")                 
      
                    
            leaderboard_table = gr.components.Dataframe(
                value=leaderboard_df[
                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                    + shown_columns.value
                    + [AutoEvalColumn.dummy.name]
                ],
                headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                datatype=TYPES,
                elem_id="leaderboard-table",
                interactive=False,
                visible=True,
                #column_widths=["2%", "33%"] 
            )

            with Modal(visible=False) as modal:
                map = gr.Plot()
            
            leaderboard_table.select(select, leaderboard_table, model_comparison)
            spider_btn.click(generate_spider_chart, [leaderboard_table, model_comparison], map)
            spider_btn.click(lambda: Modal(visible=True), None, modal)
            demo.load(init_comparison_data, None, model_comparison)
            
            # Dummy leaderboard for handling the case when the user uses backspace key
            hidden_leaderboard_table_for_search = gr.components.Dataframe(
                value=original_df[COLS],
                headers=COLS,
                datatype=TYPES,
                visible=False,
            )

            hide_models = gr.Textbox(
                            placeholder="",
                            show_label=False,
                            elem_id="search-bar",
                            value="",
                            visible=False,

                        )
            
            search_bar.submit(
                update_table,
                [
                    hidden_leaderboard_table_for_search,
                    shown_columns,
                    filter_columns_type,
                    filter_columns_precision,
                    filter_columns_parameters,
                    filter_columns_size,
                    hide_models,
                    search_bar,
                    filter_columns_computeDtype,
                    filter_columns_weightDtype,
                    filter_columns_doubleQuant,
                    filter_columns_groupDtype
                ],
                leaderboard_table,
            )

            """
           
            # Define a hidden component that will trigger a reload only if a query parameter has been set
            hidden_search_bar = gr.Textbox(value="", visible=False)
            hidden_search_bar.change(
                update_table,
                [
                    hidden_leaderboard_table_for_search,
                    shown_columns,
                    filter_columns_type,
                    filter_columns_precision,
                    filter_columns_size,
                    hide_models,
                    search_bar,
                ],
                leaderboard_table,
            )
            # Check query parameter once at startup and update search bar + hidden component
            demo.load(load_query, inputs=[], outputs=[search_bar, hidden_search_bar])
            
            """
            filter_columns_precision.change(
                update_Weight_Dtype,
                [filter_columns_precision],
                [filter_columns_weightDtype]
            )

            filter_columns_type.change(
                quant_update_Weight_Dtype,
                [filter_columns_type],
                [filter_columns_weightDtype]
            )

            


    
            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, filter_columns_parameters, hide_models, filter_columns_computeDtype, filter_columns_weightDtype, filter_columns_doubleQuant, filter_columns_groupDtype]:
                selector.change(
                    update_table,
                    [
                        hidden_leaderboard_table_for_search,
                        shown_columns,
                        filter_columns_type,
                        filter_columns_precision,
                        filter_columns_parameters,
                        filter_columns_size,
                        hide_models,
                        search_bar,
                        filter_columns_computeDtype,
                        filter_columns_weightDtype,
                        filter_columns_doubleQuant,
                        filter_columns_groupDtype
                    ],
                    leaderboard_table,
                    queue=True,
                )


        with gr.TabItem("πŸ“ˆ Metrics through time", elem_id="llm-benchmark-tab-table", id=2):
            with gr.Row():
                with gr.Column():
                    chart = create_metric_plot_obj(
                        plot_df,
                        [AutoEvalColumn.average.name],
                        title="Average of Top Scores and Human Baseline Over Time (from last update)",
                    )
                    gr.Plot(value=chart, min_width=500) 
                with gr.Column():
                    chart = create_metric_plot_obj(
                        plot_df,
                        BENCHMARK_COLS,
                        title="Top Scores and Human Baseline Over Time (from last update)",
                    )
                    gr.Plot(value=chart, min_width=500) 
        with gr.TabItem("πŸ“ About", elem_id="llm-benchmark-tab-table", id=3):
            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        with gr.TabItem("❗FAQ", elem_id="llm-benchmark-tab-table", id=4):
            gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")

        with gr.TabItem("πŸš€ Submit ", elem_id="llm-benchmark-tab-table", id=5):
            with gr.Column():
                with gr.Row():
                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")

            with gr.Row():
                gr.Markdown("# βœ‰οΈβœ¨ Submit your model here!", elem_classes="markdown-text")

            with gr.Row():
                with gr.Column():
                    model_name_textbox = gr.Textbox(label="Model name")
                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
                    private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)

                with gr.Column():
                    """
                    precision = gr.Dropdown(
                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
                        label="Precision",
                        multiselect=False,
                        value="4bit",
                        interactive=True,
                    )
                    weight_type = gr.Dropdown(
                        choices=[i.value.name for i in WeightDtype],
                        label="Weights dtype",
                        multiselect=False,
                        value="int4",
                        interactive=True,
                    )
                    """
                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)",
                            visible=not IS_PUBLIC)
                    compute_type = gr.Dropdown(
                        choices=[i.value.name for i in ComputeDtype if i.value.name != "All"],
                        label="Compute dtype",
                        multiselect=False,
                        value="float16",
                        interactive=True,
                    )

            submit_button = gr.Button("Submit Eval")
            submission_result = gr.Markdown()
            submit_button.click(
                add_new_eval,
                [
                    model_name_textbox,
                    revision_name_textbox,
                    private,
                    compute_type,
                ],
                submission_result,
            )

            with gr.Column():
                with gr.Accordion(
                    f"βœ… Finished Evaluations ({len(finished_eval_queue_df)})",
                    open=False,
                ):
                    with gr.Row():
                        finished_eval_table = gr.components.Dataframe(
                            value=finished_eval_queue_df,
                            headers=EVAL_COLS,
                            datatype=EVAL_TYPES,
                            row_count=5,
                        )
                with gr.Accordion(
                    f"πŸ”„ Running Evaluation Queue ({len(running_eval_queue_df)})",
                    open=False,
                ):
                    with gr.Row():
                        running_eval_table = gr.components.Dataframe(
                            value=running_eval_queue_df,
                            headers=EVAL_COLS,
                            datatype=EVAL_TYPES,
                            row_count=5,
                        )

                with gr.Accordion(
                    f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
                    open=False,
                ):
                    with gr.Row():
                        pending_eval_table = gr.components.Dataframe(
                            value=pending_eval_queue_df,
                            headers=EVAL_COLS,
                            datatype=EVAL_TYPES,
                            row_count=5,
                        )

    with gr.Row():
        with gr.Accordion("πŸ“™ Citation", open=False):
            citation_button = gr.Textbox(
                value=CITATION_BUTTON_TEXT,
                label=CITATION_BUTTON_LABEL,
                lines=20,
                elem_id="citation-button",
                show_copy_button=True,
            )

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", hours=3) # restarted every 3h
scheduler.add_job(update_dynamic_files, "interval", hours=12) # launched every 2 hour
scheduler.start()

demo.queue(default_concurrency_limit=40).launch()
# demo.queue(concurrency_count=40).launch()