File size: 20,666 Bytes
928f123
 
e518ed3
 
 
 
 
7e4123a
 
 
 
 
928f123
7e4123a
 
928f123
ca3e112
7e4123a
4bad4d8
e518ed3
4bad4d8
4572f9c
7e4123a
 
 
 
 
4572f9c
1be7f01
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
af10ea5
 
1be7f01
 
 
 
 
 
 
 
 
ca3e112
e08d6ea
7e4123a
 
 
 
 
 
 
 
 
 
 
 
 
 
ca3e112
7e4123a
 
 
ca3e112
7e4123a
125abbd
ba0d9a2
1c5a53b
046ea77
de51e8b
4572f9c
9ddd488
 
 
4572f9c
1c5a53b
64cc57f
1c5a53b
 
 
c53af21
1c5a53b
 
 
928f123
1c5a53b
 
 
928f123
 
 
 
 
 
 
 
046ea77
7e4123a
deee37e
b080e86
 
e08d6ea
 
 
b080e86
e08d6ea
66f4eb1
928f123
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
046ea77
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ca3e112
c3d4cb6
 
ba0d9a2
 
 
 
 
4572f9c
1c5a53b
4572f9c
923e7bc
a68da4c
1c5a53b
 
4572f9c
1c5a53b
4572f9c
923e7bc
a68da4c
1c5a53b
 
4572f9c
1c5a53b
4572f9c
923e7bc
a68da4c
1c5a53b
 
4572f9c
928f123
4572f9c
923e7bc
a68da4c
928f123
 
4572f9c
928f123
4572f9c
923e7bc
a68da4c
928f123
 
4572f9c
928f123
4572f9c
923e7bc
a68da4c
928f123
 
4572f9c
928f123
4572f9c
923e7bc
a68da4c
928f123
 
4572f9c
928f123
4572f9c
923e7bc
a68da4c
928f123
 
4572f9c
928f123
4572f9c
923e7bc
a68da4c
928f123
 
4572f9c
928f123
4572f9c
923e7bc
a68da4c
928f123
 
7e4123a
 
4572f9c
7e4123a
e08d6ea
4572f9c
 
 
 
 
 
 
 
 
 
 
923e7bc
a68da4c
4572f9c
 
7e4123a
 
4572f9c
7e4123a
e08d6ea
4572f9c
 
 
 
 
 
 
 
 
 
 
923e7bc
e08d6ea
4572f9c
 
7e4123a
 
a95339f
7e4123a
e08d6ea
a95339f
 
 
 
 
 
 
 
 
 
 
923e7bc
a68da4c
a95339f
 
7e4123a
ca3e112
7e4123a
e08d6ea
ca3e112
 
 
 
 
 
 
 
 
 
 
923e7bc
a68da4c
ca3e112
 
125abbd
 
928f123
 
 
 
16272b2
125abbd
 
 
ffff3d1
 
 
 
 
 
 
e08d6ea
 
ffff3d1
7e4123a
 
 
ffff3d1
7e4123a
923e7bc
a68da4c
7e4123a
 
 
923e7bc
a68da4c
7e4123a
 
 
 
 
 
923e7bc
a68da4c
7e4123a
 
 
923e7bc
a68da4c
7e4123a
 
 
928f123
 
e08d6ea
7e4123a
 
928f123
 
 
 
 
7a143c1
928f123
 
 
 
7e4123a
 
928f123
 
 
046ea77
928f123
8076230
ae265c6
8076230
 
e08d6ea
8076230
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
import gradio as gr

from init import (
    get_secrets, initialize_data, 
    update_dataframe, initialize_repos
)
from gen.openllm import GradioMistralChatPPManager
from gen.gemini_chat import GradioGeminiChatPPManager
from constants.js import (
    UPDATE_SEARCH_RESULTS, OPEN_CHAT_IF,
    CLOSE_CHAT_IF, UPDATE_CHAT_HISTORY
)

from datetime import datetime, timedelta
from background import process_arxiv_ids
from apscheduler.schedulers.background import BackgroundScheduler

gemini_api_key, hf_token, dataset_repo_id, request_arxiv_repo_id, restart_repo_id = get_secrets()
initialize_repos(dataset_repo_id, request_arxiv_repo_id, hf_token)

titles, date_dict, requested_arxiv_ids_df, arxivid2data = initialize_data(dataset_repo_id, request_arxiv_repo_id)

from ui import (
    get_paper_by_year, get_paper_by_month, get_paper_by_day,
    set_papers, set_paper, set_date, change_exp_type, add_arxiv_ids_to_queue,
    before_chat_begin, chat_stream, chat_reset
)

if len(date_dict.keys()) > 0:
    sorted_year = sorted(date_dict.keys())
    last_year = sorted_year[-1] if len(sorted_year) > 0 else ""
    sorted_month = sorted(date_dict[last_year].keys())
    last_month = sorted_month[-1] if len(sorted_year) > 0 else ""
    sorted_day = sorted(date_dict[last_year][last_month].keys())
    last_day = sorted_day[-1] if len(sorted_year) > 0 else ""
    last_papers = date_dict[last_year][last_month][last_day] if len(sorted_year) > 0 else [""]    
    selected_paper = last_papers[0]
    visible = True
else:
    sorted_year = ["2024"]
    last_year = sorted_year[-1]
    sorted_month = ["01"]
    last_month = sorted_month[-1]
    sorted_day = ["01"]
    last_day = sorted_day[-1]

    selected_paper = {}
    selected_paper["title"] = ""
    selected_paper["summary"] = ""
    selected_paper["arxiv_id"] = ""
    selected_paper["target_date"] = "2024-01-01"
    for idx in range(10):
        selected_paper[f"{idx}_question"] = ""
        selected_paper[f"{idx}_answers:eli5"] = ""
        selected_paper[f"{idx}_answers:expert"] = ""
        selected_paper[f"{idx}_additional_depth_q:follow up question"] = ""
        selected_paper[f"{idx}_additional_depth_q:answers:eli5"] = ""
        selected_paper[f"{idx}_additional_depth_q:answers:expert"] = ""
        selected_paper[f"{idx}_additional_breath_q:follow up question"] = ""
        selected_paper[f"{idx}_additional_breath_q:answers:eli5"] = ""
        selected_paper[f"{idx}_additional_breath_q:answers:expert"] = ""

    last_papers = [selected_paper]
    visible = False

with gr.Blocks(css="constants/styles.css", theme=gr.themes.Soft()) as demo:
    cur_arxiv_id = gr.Textbox(selected_paper['arxiv_id'], visible=False)
    local_data = gr.JSON({}, visible=False)
    chat_state = gr.State({
        "ppmanager_type": GradioGeminiChatPPManager # GradioMistralChatPPManager # GradioLLaMA2ChatPPManager
    })

    with gr.Column(elem_id="chatbot-back"):
        with gr.Column(elem_id="chatbot", elem_classes=["hover-opacity"]):
            close = gr.Button("𝕏", elem_id="chatbot-right-button") #elem_id="chatbot-right-button")
            chatbot = gr.Chatbot(
                label="Gemini 1.0 Pro", show_label=True, 
                show_copy_button=True, show_share_button=True, 
                visible=True, elem_id="chatbot-inside"
            )

            with gr.Row(elem_id="chatbot-bottm"):
                reset = gr.Button("πŸ—‘οΈ Reset")
                regen = gr.Button("πŸ”„ Regenerate", visible=False)

            prompt_txtbox = gr.Textbox(placeholder="Ask anything.....", elem_id="chatbot-txtbox", elem_classes=["textbox-no-label"])

    gr.Markdown("# Let's explore papers with auto generated Q&As")
    
    with gr.Column(elem_id="control-panel", elem_classes=["group"], visible=visible):
        with gr.Column():
            with gr.Row():
                year_dd = gr.Dropdown(sorted_year, value=last_year, label="Year", interactive=True, filterable=False)    
                month_dd = gr.Dropdown(sorted_month, value=last_month, label="Month", interactive=True, filterable=False)    
                day_dd = gr.Dropdown(sorted_day, value=last_day, label="Day", interactive=True, filterable=False)
                
            papers_dd = gr.Dropdown(
                list(set([paper["title"] for paper in last_papers])),
                value=selected_paper["title"],
                label="Select paper title", 
                interactive=True,
                filterable=False
            )

        with gr.Column(elem_classes=["no-gap"]):
            search_in = gr.Textbox("", placeholder="Enter keywords to search...", elem_classes=["textbox-no-label"])
            search_r1 = gr.Button(visible=False, elem_id="search_r1", elem_classes=["no-radius"])
            search_r2 = gr.Button(visible=False, elem_id="search_r2", elem_classes=["no-radius"])
            search_r3 = gr.Button(visible=False, elem_id="search_r3", elem_classes=["no-radius"])
            search_r4 = gr.Button(visible=False, elem_id="search_r4", elem_classes=["no-radius"])
            search_r5 = gr.Button(visible=False, elem_id="search_r5", elem_classes=["no-radius"])
            search_r6 = gr.Button(visible=False, elem_id="search_r6", elem_classes=["no-radius"])
            search_r7 = gr.Button(visible=False, elem_id="search_r7", elem_classes=["no-radius"])
            search_r8 = gr.Button(visible=False, elem_id="search_r8", elem_classes=["no-radius"])
            search_r9 = gr.Button(visible=False, elem_id="search_r9", elem_classes=["no-radius"])
            search_r10 = gr.Button(visible=False, elem_id="search_r10", elem_classes=["no-radius"])

    with gr.Column(scale=7, visible=visible):
        title = gr.Markdown(f"# {selected_paper['title']}", elem_classes=["markdown-center"])
        # with gr.Row():
        with gr.Row():
            arxiv_link = gr.Markdown(
                "[![arXiv](https://img.shields.io/badge/arXiv-%s-b31b1b.svg?style=for-the-badge)](https://arxiv.org/abs/%s)" % (selected_paper['arxiv_id'], selected_paper['arxiv_id']) + " "
                "[![Paper page](https://huggingface.co/datasets/huggingface/badges/resolve/main/paper-page-lg.svg)](https://huggingface.co/papers/%s)" % selected_paper['arxiv_id'] + " ",
                elem_id="link-md",
            )
        chat_button = gr.Button("Chat about any custom questions", interactive=True, elem_id="chat-button")
            
        summary = gr.Markdown(f"{selected_paper['summary']}", elem_classes=["small-font"])

        with gr.Column(elem_id="qna_block", visible=True):
            with gr.Row():
                with gr.Column(scale=7):
                    gr.Markdown("## Auto generated Questions & Answers")

                exp_type = gr.Radio(choices=["ELI5", "Technical"], value="ELI5", elem_classes=["exp-type"], scale=3)

            # 1
            with gr.Column(elem_classes=["group"], visible=True) as q_0:
                basic_q_0 = gr.Markdown(f"### πŸ™‹ {selected_paper['0_question']}")
                basic_q_eli5_0 = gr.Markdown(f"β†ͺ **(ELI5)** {selected_paper['0_answers:eli5']}", elem_classes=["small-font"]) 
                basic_q_expert_0 = gr.Markdown(f"β†ͺ **(Technical)** {selected_paper['0_answers:expert']}", visible=False, elem_classes=["small-font"]) 

                with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_0_0:
                    depth_q_0 = gr.Markdown(f"### πŸ™‹πŸ™‹ {selected_paper['0_additional_depth_q:follow up question']}")
                    depth_q_eli5_0 = gr.Markdown(f"β†ͺ **(ELI5)** {selected_paper['0_additional_depth_q:answers:eli5']}", elem_classes=["small-font"])
                    depth_q_expert_0 = gr.Markdown(f"β†ͺ **(Technical)** {selected_paper['0_additional_depth_q:answers:expert']}", visible=False, elem_classes=["small-font"])

                with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_0_1:
                    breath_q_0 = gr.Markdown(f"### πŸ™‹πŸ™‹ {selected_paper['0_additional_breath_q:follow up question']}")
                    breath_q_eli5_0 = gr.Markdown(f"β†ͺ **(ELI5)** {selected_paper['0_additional_breath_q:answers:eli5']}", elem_classes=["small-font"])
                    breath_q_expert_0 = gr.Markdown(f"β†ͺ **(Technical)** {selected_paper['0_additional_breath_q:answers:expert']}", visible=False, elem_classes=["small-font"])

            # 2
            with gr.Column(elem_classes=["group"], visible=True) as q_1:
                basic_q_1 = gr.Markdown(f"### πŸ™‹ {selected_paper['1_question']}")
                basic_q_eli5_1 = gr.Markdown(f"β†ͺ **(ELI5)** {selected_paper['1_answers:eli5']}", elem_classes=["small-font"]) 
                basic_q_expert_1 = gr.Markdown(f"β†ͺ **(Technical)** {selected_paper['1_answers:expert']}", visible=False, elem_classes=["small-font"]) 

                with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_1_0:
                    depth_q_1 = gr.Markdown(f"### πŸ™‹πŸ™‹ {selected_paper['1_additional_depth_q:follow up question']}")
                    depth_q_eli5_1 = gr.Markdown(f"β†ͺ **(ELI5)** {selected_paper['1_additional_depth_q:answers:eli5']}", elem_classes=["small-font"])
                    depth_q_expert_1 = gr.Markdown(f"β†ͺ **(Technical)** {selected_paper['1_additional_depth_q:answers:expert']}", visible=False, elem_classes=["small-font"])

                with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_1_1:
                    breath_q_1 = gr.Markdown(f"### πŸ™‹πŸ™‹ {selected_paper['1_additional_breath_q:follow up question']}")
                    breath_q_eli5_1 = gr.Markdown(f"β†ͺ **(ELI5)** {selected_paper['1_additional_breath_q:answers:eli5']}", elem_classes=["small-font"])
                    breath_q_expert_1 = gr.Markdown(f"β†ͺ **(Technical)** {selected_paper['1_additional_breath_q:answers:expert']}", visible=False, elem_classes=["small-font"])

            # 3
            with gr.Column(elem_classes=["group"], visible=True) as q_2:
                basic_q_2 = gr.Markdown(f"### πŸ™‹ {selected_paper['2_question']}")
                basic_q_eli5_2 = gr.Markdown(f"β†ͺ **(ELI5)** {selected_paper['2_answers:eli5']}", elem_classes=["small-font"]) 
                basic_q_expert_2 = gr.Markdown(f"β†ͺ **(Technical)** {selected_paper['2_answers:expert']}", visible=False, elem_classes=["small-font"]) 

                with gr.Accordion("Additional question #1", open=False, elem_classes=["accordion"]) as aq_2_0:
                    depth_q_2 = gr.Markdown(f"### πŸ™‹πŸ™‹ {selected_paper['2_additional_depth_q:follow up question']}")
                    depth_q_eli5_2 = gr.Markdown(f"β†ͺ **(ELI5)** {selected_paper['2_additional_depth_q:answers:eli5']}", elem_classes=["small-font"])
                    depth_q_expert_2 = gr.Markdown(f"β†ͺ **(Technical)** {selected_paper['2_additional_depth_q:answers:expert']}", visible=False, elem_classes=["small-font"])

                with gr.Accordion("Additional question #2", open=False, elem_classes=["accordion"]) as aq_2_1:
                    breath_q_2 = gr.Markdown(f"### πŸ™‹πŸ™‹ {selected_paper['2_additional_breath_q:follow up question']}")
                    breath_q_eli5_2 = gr.Markdown(f"β†ͺ **(ELI5)** {selected_paper['2_additional_breath_q:answers:eli5']}", elem_classes=["small-font"])
                    breath_q_expert_2 = gr.Markdown(f"β†ͺ **(Technical)** {selected_paper['2_additional_breath_q:answers:expert']}", visible=False, elem_classes=["small-font"])

    gr.Markdown("## Request any arXiv ids")
    arxiv_queue = gr.Dataframe(
        headers=["Requested arXiv IDs"], col_count=(1, "fixed"),
        value=requested_arxiv_ids_df,
        datatype=["str"],
        interactive=False,
    )

    arxiv_id_enter = gr.Textbox(placeholder="Enter comma separated arXiv IDs...", elem_classes=["textbox-no-label"])
    arxiv_id_enter.submit(
        add_arxiv_ids_to_queue,
        [arxiv_queue, arxiv_id_enter],
        [arxiv_queue, arxiv_id_enter],
        concurrency_limit=20,
    )

    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")

    gr.Markdown("The target papers are collected from [Hugging Face πŸ€— Daily Papers](https://huggingface.co/papers) on a daily basis. "
                "The entire data is generated by [Google's Gemini 1.0](https://deepmind.google/technologies/gemini/) Pro. "
                "If you are curious how it is done, visit the [Auto Paper Q&A Generation project repository](https://github.com/deep-diver/auto-paper-analysis) "
                "Also, the generated dataset is hosted on Hugging Face πŸ€— Dataset repository as well([Link](https://huggingface.co/datasets/chansung/auto-paper-qa2)). ")
    
    search_r1.click(set_date, search_r1, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r1],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )

    search_r2.click(set_date, search_r2, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r2],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )

    search_r3.click(set_date, search_r3, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r3],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )

    search_r4.click(set_date, search_r4, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r4],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )

    search_r5.click(set_date, search_r5, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r5],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )

    search_r6.click(set_date, search_r6, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r6],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )

    search_r7.click(set_date, search_r7, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r7],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )    

    search_r8.click(set_date, search_r8, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r8],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )

    search_r9.click(set_date, search_r9, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r9],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )

    search_r10.click(set_date, search_r10, [year_dd, month_dd, day_dd]).then(
        set_papers,
        inputs=[year_dd, month_dd, day_dd, search_r10],
        outputs=[cur_arxiv_id, papers_dd, search_in],
        concurrency_limit=20,
    )

    year_dd.input(get_paper_by_year, inputs=[year_dd], outputs=[month_dd, day_dd, papers_dd]).then(
        set_paper, [year_dd, month_dd, day_dd, papers_dd],
        [
            cur_arxiv_id,
            title, arxiv_link, summary,
            basic_q_0, basic_q_eli5_0, basic_q_expert_0,
            depth_q_0, depth_q_eli5_0, depth_q_expert_0,
            breath_q_0, breath_q_eli5_0, breath_q_expert_0,

            basic_q_1, basic_q_eli5_1, basic_q_expert_1,
            depth_q_1, depth_q_eli5_1, depth_q_expert_1,
            breath_q_1, breath_q_eli5_1, breath_q_expert_1,

            basic_q_2, basic_q_eli5_2, basic_q_expert_2,
            depth_q_2, depth_q_eli5_2, depth_q_expert_2,
            breath_q_2, breath_q_eli5_2, breath_q_expert_2
        ],
        concurrency_limit=20,
    )

    month_dd.input(get_paper_by_month, inputs=[year_dd, month_dd], outputs=[day_dd, papers_dd]).then(
        set_paper, [year_dd, month_dd, day_dd, papers_dd],
        [
            cur_arxiv_id,
            title, arxiv_link, summary,
            basic_q_0, basic_q_eli5_0, basic_q_expert_0,
            depth_q_0, depth_q_eli5_0, depth_q_expert_0,
            breath_q_0, breath_q_eli5_0, breath_q_expert_0,

            basic_q_1, basic_q_eli5_1, basic_q_expert_1,
            depth_q_1, depth_q_eli5_1, depth_q_expert_1,
            breath_q_1, breath_q_eli5_1, breath_q_expert_1,

            basic_q_2, basic_q_eli5_2, basic_q_expert_2,
            depth_q_2, depth_q_eli5_2, depth_q_expert_2,
            breath_q_2, breath_q_eli5_2, breath_q_expert_2
        ],
        concurrency_limit=20, 
    )

    day_dd.input(get_paper_by_day, inputs=[year_dd, month_dd, day_dd], outputs=[papers_dd]).then(
        set_paper, [year_dd, month_dd, day_dd, papers_dd],
        [
            cur_arxiv_id,
            title, arxiv_link, summary,
            basic_q_0, basic_q_eli5_0, basic_q_expert_0,
            depth_q_0, depth_q_eli5_0, depth_q_expert_0,
            breath_q_0, breath_q_eli5_0, breath_q_expert_0,

            basic_q_1, basic_q_eli5_1, basic_q_expert_1,
            depth_q_1, depth_q_eli5_1, depth_q_expert_1,
            breath_q_1, breath_q_eli5_1, breath_q_expert_1,

            basic_q_2, basic_q_eli5_2, basic_q_expert_2,
            depth_q_2, depth_q_eli5_2, depth_q_expert_2,
            breath_q_2, breath_q_eli5_2, breath_q_expert_2
        ],
        concurrency_limit=20,
    )

    papers_dd.change(set_paper, [year_dd, month_dd, day_dd, papers_dd],
        [
            cur_arxiv_id,
            title, arxiv_link, summary,
            basic_q_0, basic_q_eli5_0, basic_q_expert_0,
            depth_q_0, depth_q_eli5_0, depth_q_expert_0,
            breath_q_0, breath_q_eli5_0, breath_q_expert_0,

            basic_q_1, basic_q_eli5_1, basic_q_expert_1,
            depth_q_1, depth_q_eli5_1, depth_q_expert_1,
            breath_q_1, breath_q_eli5_1, breath_q_expert_1,

            basic_q_2, basic_q_eli5_2, basic_q_expert_2,
            depth_q_2, depth_q_eli5_2, depth_q_expert_2,
            breath_q_2, breath_q_eli5_2, breath_q_expert_2
        ],
        concurrency_limit=20,
    )

    search_in.change(
        inputs=[search_in],
        outputs=[
            search_r1, search_r2, search_r3, search_r4, search_r5,
            search_r6, search_r7, search_r8, search_r9, search_r10
        ],
        js=UPDATE_SEARCH_RESULTS % str(list(titles)),
        fn=None
    )

    exp_type.select(
        change_exp_type,
        exp_type,
        [
            basic_q_eli5_0, basic_q_expert_0, depth_q_eli5_0, depth_q_expert_0, breath_q_eli5_0, breath_q_expert_0,
            basic_q_eli5_1, basic_q_expert_1, depth_q_eli5_1, depth_q_expert_1, breath_q_eli5_1, breath_q_expert_1,
            basic_q_eli5_2, basic_q_expert_2, depth_q_eli5_2, depth_q_expert_2, breath_q_eli5_2, breath_q_expert_2
        ],
        concurrency_limit=20,
    )
    
    chat_button.click(None, [cur_arxiv_id], [local_data, chatbot], js=OPEN_CHAT_IF)
    close.click(None, None, None,js=CLOSE_CHAT_IF) 

    prompt_txtbox.submit(
        before_chat_begin, None, [close, reset, regen],
        concurrency_limit=20,
    ).then(
        chat_stream, 
        [cur_arxiv_id, local_data, prompt_txtbox, chat_state],
        [prompt_txtbox, chatbot, local_data, close, reset, regen],
        concurrency_limit=20,
    ).then(
        None, [cur_arxiv_id, local_data], None, 
        js=UPDATE_CHAT_HISTORY
    )

    reset.click(
        before_chat_begin, None, [close, reset, regen],
        concurrency_limit=20,
    ).then(
        chat_reset,
        [local_data, chat_state],
        [prompt_txtbox, chatbot, local_data, close, reset, regen],
        concurrency_limit=20,
    ).then(
        None, [cur_arxiv_id, local_data], None, 
        js=UPDATE_CHAT_HISTORY
    )

    demo.load(lambda: update_dataframe(request_arxiv_repo_id), None, arxiv_queue, every=180)
    # demo.load(None, None, [chatbot, local_data], js=GET_LOCAL_STORAGE % idx.value)

start_date = datetime.now() + timedelta(minutes=1)
scheduler = BackgroundScheduler()
scheduler.add_job(
    process_arxiv_ids,
    trigger='interval',
    seconds=300,
    args=[
        gemini_api_key, 
        dataset_repo_id,
        request_arxiv_repo_id,
        hf_token, 
        restart_repo_id
    ],
    start_date=start_date
)
scheduler.start()

demo.queue(
    default_concurrency_limit=20,
    max_size=256
).launch(
    share=True, debug=True
)