File size: 19,979 Bytes
1628a30
2269797
1628a30
 
657db0b
9e7becb
 
 
657db0b
a9c2212
9e7becb
 
5a8d02c
657db0b
 
b5bf2c0
5a8d02c
e65c78c
 
43d95a6
 
a5ced77
43d95a6
 
119b257
4996a19
64136bc
f2ee5d3
a5ced77
 
 
 
 
 
 
a9c2212
b5bf2c0
 
b5ec742
9e7becb
b5ec742
 
b5bf2c0
 
a9c2212
 
 
 
119b257
 
 
 
 
 
657db0b
 
 
 
b5ec742
 
a5c2f0e
657db0b
 
10cefed
fe421d1
f2ee5d3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5a8d02c
 
657db0b
 
a5c2f0e
 
 
 
 
 
 
 
 
 
 
 
b5bf2c0
a5c2f0e
 
 
657db0b
 
 
 
 
 
 
 
64136bc
657db0b
 
 
 
 
 
 
 
 
64136bc
2269797
75e3496
 
 
 
b5bf2c0
 
 
 
 
 
2269797
b5bf2c0
7dcda45
 
b5bf2c0
7dcda45
 
 
 
 
 
b5bf2c0
 
 
7dcda45
 
 
 
 
e739a24
b5bf2c0
fe421d1
9c726b4
 
 
 
 
fe421d1
 
 
b5bf2c0
e739a24
fd054e7
e739a24
fd054e7
 
2269797
e739a24
 
119b257
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9e7becb
657db0b
 
 
 
 
a5c2f0e
 
 
 
b5bf2c0
7dcda45
 
 
119b257
7dcda45
 
 
 
 
657db0b
a5c2f0e
 
64136bc
e2d9a99
560300f
c3813c7
9c726b4
 
 
 
 
 
abbebb7
dfa9cba
7dcda45
 
9c726b4
119b257
abbebb7
560300f
a5c2f0e
560300f
 
 
e2d9a99
a5c2f0e
e2d9a99
560300f
10cefed
2269797
c79877a
 
2269797
c79877a
2269797
c79877a
 
 
 
 
 
c3813c7
6a97ef9
560300f
e2d9a99
 
9e7becb
560300f
e2d9a99
9e7becb
 
 
 
 
 
 
119b257
 
 
b5ec742
 
 
 
 
 
 
 
 
 
 
 
9e7becb
 
 
 
 
 
119b257
9e7becb
fc9ec9d
560300f
a5c2f0e
 
 
9c726b4
 
 
 
 
 
abbebb7
dfa9cba
abbebb7
 
9c726b4
119b257
abbebb7
e2d9a99
a5c2f0e
560300f
64136bc
9e7becb
119b257
 
 
 
 
 
 
 
 
 
e65c78c
 
 
 
 
 
 
 
 
 
 
4b40490
dfa9cba
abbebb7
 
9c726b4
 
 
119b257
abbebb7
4b40490
657db0b
 
 
fe421d1
 
dfa9cba
 
fe421d1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
657db0b
9e7becb
 
119b257
9e7becb
 
 
9b9b3ce
657db0b
9c726b4
abbebb7
119b257
657db0b
fe421d1
 
657db0b
 
 
 
 
 
 
 
9e7becb
 
 
dfa9cba
9e7becb
 
 
119b257
657db0b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# These imports at the end because of torch/datamapplot issue in Zero GPU
# import spaces
import gradio as gr

import logging
import os

import datamapplot
import duckdb
import numpy as np
import requests

from dotenv import load_dotenv
from gradio_huggingfacehub_search import HuggingfaceHubSearch
from bertopic import BERTopic
from bertopic.representation import KeyBERTInspired
from bertopic.representation import TextGeneration

# Temporary disabling because of ZeroGPU does not support cuml
from cuml.manifold import UMAP
from cuml.cluster import HDBSCAN

# from umap import UMAP
# from hdbscan import HDBSCAN
from huggingface_hub import HfApi
from sklearn.feature_extraction.text import CountVectorizer
from sentence_transformers import SentenceTransformer
from prompts import REPRESENTATION_PROMPT
from torch import cuda, bfloat16
from transformers import (
    BitsAndBytesConfig,
    AutoTokenizer,
    AutoModelForCausalLM,
    pipeline,
)

"""
TODOs:
- Improve representation layer (Try with llamacpp or TextGeneration)
- Make it run on Zero GPU
- Try with more rows (Current: 50_000/10_000 -> Minimal Targett: 1_000_000/20_000)
- Export interactive plots and serve their HTML content (It doesn't work with gr.HTML)
"""

load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")
assert HF_TOKEN is not None, "You need to set HF_TOKEN in your environment variables"


EXPORTS_REPOSITORY = os.getenv("EXPORTS_REPOSITORY")
assert (
    EXPORTS_REPOSITORY is not None
), "You need to set EXPORTS_REPOSITORY in your environment variables"

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)

MAX_ROWS = 50_000
CHUNK_SIZE = 10_000


session = requests.Session()
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")

# Representation model
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=bfloat16,
)

model_id = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    quantization_config=bnb_config,
    device_map="auto",
)
model.eval()
generator = pipeline(
    model=model,
    tokenizer=tokenizer,
    task="text-generation",
    temperature=0.1,
    max_new_tokens=500,
    repetition_penalty=1.1,
)
representation_model = TextGeneration(generator, prompt=REPRESENTATION_PROMPT)
# End of representation model

vectorizer_model = CountVectorizer(stop_words="english")


def get_split_rows(dataset, config, split):
    config_size = session.get(
        f"https://datasets-server.huggingface.co/size?dataset={dataset}&config={config}",
        timeout=20,
    ).json()
    if "error" in config_size:
        raise Exception(f"Error fetching config size: {config_size['error']}")
    split_size = next(
        (s for s in config_size["size"]["splits"] if s["split"] == split),
        None,
    )
    if split_size is None:
        raise Exception(f"Error fetching split {split} in config {config}")
    return split_size["num_rows"]


def get_parquet_urls(dataset, config, split):
    parquet_files = session.get(
        f"https://datasets-server.huggingface.co/parquet?dataset={dataset}&config={config}&split={split}",
        timeout=20,
    ).json()
    if "error" in parquet_files:
        raise Exception(f"Error fetching parquet files: {parquet_files['error']}")
    parquet_urls = [file["url"] for file in parquet_files["parquet_files"]]
    logging.debug(f"Parquet files: {parquet_urls}")
    return ",".join(f"'{url}'" for url in parquet_urls)


def get_docs_from_parquet(parquet_urls, column, offset, limit):
    SQL_QUERY = f"SELECT {column} FROM read_parquet([{parquet_urls}]) LIMIT {limit} OFFSET {offset};"
    df = duckdb.sql(SQL_QUERY).to_df()
    logging.debug(f"Dataframe: {df.head(5)}")
    return df[column].tolist()


# @spaces.GPU
def calculate_embeddings(docs):
    return sentence_model.encode(docs, show_progress_bar=True, batch_size=32)


def calculate_n_neighbors_and_components(n_rows):
    n_neighbors = min(max(n_rows // 20, 15), 100)
    n_components = 10 if n_rows > 1000 else 5  # Higher components for larger datasets
    return n_neighbors, n_components


# @spaces.GPU
def fit_model(docs, embeddings, n_neighbors, n_components):
    umap_model = UMAP(
        n_neighbors=n_neighbors,
        n_components=n_components,
        min_dist=0.0,
        metric="cosine",
        random_state=42,
    )

    hdbscan_model = HDBSCAN(
        min_cluster_size=max(
            5, n_neighbors // 2
        ),  # Reducing min_cluster_size for fewer outliers
        metric="euclidean",
        cluster_selection_method="eom",
        prediction_data=True,
    )

    new_model = BERTopic(
        language="english",
        # Sub-models
        embedding_model=sentence_model,  # Step 1 - Extract embeddings
        umap_model=umap_model,  # Step 2 - UMAP model
        hdbscan_model=hdbscan_model,  # Step 3 - Cluster reduced embeddings
        vectorizer_model=vectorizer_model,  # Step 4 - Tokenize topics
        representation_model=representation_model,  # Step 5 - Label topics
        # Hyperparameters
        top_n_words=10,
        verbose=True,
        min_topic_size=n_neighbors,  # Coherent with n_neighbors?
    )
    logging.info("Fitting new model")
    new_model.fit(docs, embeddings)
    logging.info("End fitting new model")

    return new_model


def _push_to_hub(
    dataset_id,
    file_path,
):
    logging.info(f"Pushing file to hub: {dataset_id} on file {file_path}")

    file_name = file_path.split("/")[-1]
    api = HfApi(token=HF_TOKEN)
    try:
        logging.info(f"About to push {file_path} - {dataset_id}")
        api.upload_file(
            path_or_fileobj=file_path,
            path_in_repo=file_name,
            repo_id=EXPORTS_REPOSITORY,
            repo_type="dataset",
        )
    except Exception as e:
        logging.info("Failed to push file", e)
        raise


def generate_topics(dataset, config, split, column, nested_column, plot_type):
    logging.info(
        f"Generating topics for {dataset} with config {config} {split} {column} {nested_column}"
    )

    parquet_urls = get_parquet_urls(dataset, config, split)
    split_rows = get_split_rows(dataset, config, split)
    logging.info(f"Split rows: {split_rows}")

    limit = min(split_rows, MAX_ROWS)
    n_neighbors, n_components = calculate_n_neighbors_and_components(limit)

    reduce_umap_model = UMAP(
        n_neighbors=n_neighbors,
        n_components=2,  # For visualization, keeping it for 2D
        min_dist=0.0,
        metric="cosine",
        random_state=42,
    )

    offset = 0
    rows_processed = 0

    base_model = None
    all_docs = []
    reduced_embeddings_list = []
    topics_info, topic_plot = None, None
    full_processing = split_rows <= MAX_ROWS
    message = (
        f"⚙️ Processing full dataset: 0 of ({split_rows} rows)"
        if full_processing
        else f"⚙️ Processing partial dataset 0 of ({limit} rows)"
    )
    yield (
        gr.Accordion(open=False),
        gr.DataFrame(value=[], interactive=False, visible=True),
        gr.Plot(value=None, visible=True),
        gr.Label({message: rows_processed / limit}, visible=True),
        "",
    )
    while offset < limit:
        docs = get_docs_from_parquet(parquet_urls, column, offset, CHUNK_SIZE)
        if not docs:
            break

        logging.info(
            f"----> Processing chunk: {offset=} {CHUNK_SIZE=} with {len(docs)} docs"
        )

        embeddings = calculate_embeddings(docs)
        new_model = fit_model(docs, embeddings, n_neighbors, n_components)

        if base_model is None:
            base_model = new_model
        else:
            updated_model = BERTopic.merge_models([base_model, new_model])
            nr_new_topics = len(set(updated_model.topics_)) - len(
                set(base_model.topics_)
            )
            new_topics = list(updated_model.topic_labels_.values())[-nr_new_topics:]
            logging.info(f"The following topics are newly found: {new_topics}")
            base_model = updated_model

        reduced_embeddings = reduce_umap_model.fit_transform(embeddings)
        reduced_embeddings_list.append(reduced_embeddings)

        all_docs.extend(docs)
        reduced_embeddings_array = np.vstack(reduced_embeddings_list)

        topics_info = base_model.get_topic_info()
        all_topics, _ = base_model.transform(all_docs)
        all_topics = np.array(all_topics)

        topic_plot = (
            base_model.visualize_document_datamap(
                docs=all_docs,
                reduced_embeddings=reduced_embeddings_array,
                title=dataset,
                width=800,
                height=700,
                arrowprops={
                    "arrowstyle": "wedge,tail_width=0.5",
                    "connectionstyle": "arc3,rad=0.05",
                    "linewidth": 0,
                    "fc": "#33333377",
                },
                dynamic_label_size=False,
                # label_wrap_width=12,
                # label_over_points=True,
                # dynamic_label_size=True,
                # max_font_size=36,
                # min_font_size=4,
            )
            if plot_type == "DataMapPlot"
            else base_model.visualize_documents(
                docs=all_docs,
                reduced_embeddings=reduced_embeddings_array,
                custom_labels=True,
                title=dataset,
            )
        )

        rows_processed += len(docs)
        progress = min(rows_processed / limit, 1.0)
        logging.info(f"Progress: {progress} % - {rows_processed} of {limit}")
        message = (
            f"⚙️ Processing full dataset: {rows_processed} of {limit}"
            if full_processing
            else f"⚙️ Processing partial dataset: {rows_processed} of {limit} rows"
        )

        yield (
            gr.Accordion(open=False),
            topics_info,
            topic_plot,
            gr.Label({message: progress}, visible=True),
            "",
        )

        offset += CHUNK_SIZE

    logging.info("Finished processing all data")

    plot_png = f"{dataset.replace('/', '-')}-{plot_type.lower()}.png"
    if plot_type == "DataMapPlot":
        topic_plot.savefig(plot_png, format="png", dpi=300)
    else:
        topic_plot.write_image(plot_png)

    _push_to_hub(dataset, plot_png)
    plot_png_link = (
        f"https://huggingface.co/datasets/{EXPORTS_REPOSITORY}/blob/main/{plot_png}"
    )
    # interactive_plot = datamapplot.create_interactive_plot(
    #     reduced_embeddings_array,
    #     *cord19_label_layers,
    #     font_family="Cinzel",
    #     enable_search=True,
    #     inline_data=False,
    #     offline_data_prefix="cord-large-1",
    #     initial_zoom_fraction=0.4,
    # )
    # all_topics, _ = base_model.transform(all_topics)
    # logging.info(f"TAll opics: {all_topics[:5]}")
    yield (
        gr.Accordion(open=False),
        topics_info,
        topic_plot,
        gr.Label(
            {f"✅ Done: {rows_processed} rows have been processed": 1.0}, visible=True
        ),
        f"[![Download as PNG](https://img.shields.io/badge/Download_as-PNG-red)]({plot_png_link})",
    )
    cuda.empty_cache()


with gr.Blocks() as demo:
    gr.Markdown("# 💠 Dataset Topic Discovery 🔭")
    gr.Markdown("## Select dataset and text column")
    data_details_accordion = gr.Accordion("Data details", open=True)
    with data_details_accordion:
        with gr.Row():
            with gr.Column(scale=3):
                dataset_name = HuggingfaceHubSearch(
                    label="Hub Dataset ID",
                    placeholder="Search for dataset id on Huggingface",
                    search_type="dataset",
                )
            subset_dropdown = gr.Dropdown(label="Subset", visible=False)
            split_dropdown = gr.Dropdown(label="Split", visible=False)

        with gr.Accordion("Dataset preview", open=False):

            @gr.render(inputs=[dataset_name, subset_dropdown, split_dropdown])
            def embed(name, subset, split):
                html_code = f"""
                <iframe
                src="https://huggingface.co/datasets/{name}/embed/viewer/{subset}/{split}"
                frameborder="0"
                width="100%"
                height="600px"
                ></iframe>
                    """
                return gr.HTML(value=html_code)

        with gr.Row():
            text_column_dropdown = gr.Dropdown(label="Text column name")
            nested_text_column_dropdown = gr.Dropdown(
                label="Nested text column name", visible=False
            )
            plot_type_radio = gr.Radio(
                ["DataMapPlot", "Plotly"],
                value="DataMapPlot",
                label="Choose the plot type",
                interactive=True,
            )
        generate_button = gr.Button("Generate Topics", variant="primary")

    gr.Markdown("## Data map")
    full_topics_generation_label = gr.Label(visible=False, show_label=False)
    open_png_label = gr.Markdown()
    topics_plot = gr.Plot()
    with gr.Accordion("Topics Info", open=False):
        topics_df = gr.DataFrame(interactive=False, visible=True)
    generate_button.click(
        generate_topics,
        inputs=[
            dataset_name,
            subset_dropdown,
            split_dropdown,
            text_column_dropdown,
            nested_text_column_dropdown,
            plot_type_radio,
        ],
        outputs=[
            data_details_accordion,
            topics_df,
            topics_plot,
            full_topics_generation_label,
            open_png_label,
        ],
    )

    def _resolve_dataset_selection(
        dataset: str, default_subset: str, default_split: str, text_feature
    ):
        if "/" not in dataset.strip().strip("/"):
            return {
                subset_dropdown: gr.Dropdown(visible=False),
                split_dropdown: gr.Dropdown(visible=False),
                text_column_dropdown: gr.Dropdown(label="Text column name"),
                nested_text_column_dropdown: gr.Dropdown(visible=False),
            }
        info_resp = session.get(
            f"https://datasets-server.huggingface.co/info?dataset={dataset}", timeout=20
        ).json()
        if "error" in info_resp:
            return {
                subset_dropdown: gr.Dropdown(visible=False),
                split_dropdown: gr.Dropdown(visible=False),
                text_column_dropdown: gr.Dropdown(label="Text column name"),
                nested_text_column_dropdown: gr.Dropdown(visible=False),
            }
        subsets: list[str] = list(info_resp["dataset_info"])
        subset = default_subset if default_subset in subsets else subsets[0]
        splits: list[str] = list(info_resp["dataset_info"][subset]["splits"])
        split = default_split if default_split in splits else splits[0]
        features = info_resp["dataset_info"][subset]["features"]

        def _is_string_feature(feature):
            return isinstance(feature, dict) and feature.get("dtype") == "string"

        text_features = [
            feature_name
            for feature_name, feature in features.items()
            if _is_string_feature(feature)
        ]
        nested_features = [
            feature_name
            for feature_name, feature in features.items()
            if isinstance(feature, dict)
            and isinstance(next(iter(feature.values())), dict)
        ]
        nested_text_features = [
            feature_name
            for feature_name in nested_features
            if any(
                _is_string_feature(nested_feature)
                for nested_feature in features[feature_name].values()
            )
        ]
        if not text_feature:
            return {
                subset_dropdown: gr.Dropdown(
                    value=subset, choices=subsets, visible=len(subsets) > 1
                ),
                split_dropdown: gr.Dropdown(
                    value=split, choices=splits, visible=len(splits) > 1
                ),
                text_column_dropdown: gr.Dropdown(
                    choices=text_features + nested_text_features,
                    label="Text column name",
                ),
                nested_text_column_dropdown: gr.Dropdown(visible=False),
            }
        if text_feature in nested_text_features:
            nested_keys = [
                feature_name
                for feature_name, feature in features[text_feature].items()
                if _is_string_feature(feature)
            ]
            return {
                subset_dropdown: gr.Dropdown(
                    value=subset, choices=subsets, visible=len(subsets) > 1
                ),
                split_dropdown: gr.Dropdown(
                    value=split, choices=splits, visible=len(splits) > 1
                ),
                text_column_dropdown: gr.Dropdown(
                    choices=text_features + nested_text_features,
                    label="Text column name",
                ),
                nested_text_column_dropdown: gr.Dropdown(
                    value=nested_keys[0],
                    choices=nested_keys,
                    label="Nested text column name",
                    visible=True,
                ),
            }
        return {
            subset_dropdown: gr.Dropdown(
                value=subset, choices=subsets, visible=len(subsets) > 1
            ),
            split_dropdown: gr.Dropdown(
                value=split, choices=splits, visible=len(splits) > 1
            ),
            text_column_dropdown: gr.Dropdown(
                choices=text_features + nested_text_features, label="Text column name"
            ),
            nested_text_column_dropdown: gr.Dropdown(visible=False),
        }

    @dataset_name.change(
        inputs=[dataset_name],
        outputs=[
            subset_dropdown,
            split_dropdown,
            text_column_dropdown,
            nested_text_column_dropdown,
        ],
    )
    def show_input_from_subset_dropdown(dataset: str) -> dict:
        return _resolve_dataset_selection(
            dataset, default_subset="default", default_split="train", text_feature=None
        )

    @subset_dropdown.change(
        inputs=[dataset_name, subset_dropdown],
        outputs=[
            subset_dropdown,
            split_dropdown,
            text_column_dropdown,
            nested_text_column_dropdown,
        ],
    )
    def show_input_from_subset_dropdown(dataset: str, subset: str) -> dict:
        return _resolve_dataset_selection(
            dataset, default_subset=subset, default_split="train", text_feature=None
        )

    @split_dropdown.change(
        inputs=[dataset_name, subset_dropdown, split_dropdown],
        outputs=[
            subset_dropdown,
            split_dropdown,
            text_column_dropdown,
            nested_text_column_dropdown,
        ],
    )
    def show_input_from_split_dropdown(dataset: str, subset: str, split: str) -> dict:
        return _resolve_dataset_selection(
            dataset, default_subset=subset, default_split=split, text_feature=None
        )

    @text_column_dropdown.change(
        inputs=[dataset_name, subset_dropdown, split_dropdown, text_column_dropdown],
        outputs=[
            subset_dropdown,
            split_dropdown,
            text_column_dropdown,
            nested_text_column_dropdown,
        ],
    )
    def show_input_from_text_column_dropdown(
        dataset: str, subset: str, split: str, text_column
    ) -> dict:
        return _resolve_dataset_selection(
            dataset,
            default_subset=subset,
            default_split=split,
            text_feature=text_column,
        )


demo.launch()