forked from facebookresearch/audiocraft
-
Notifications
You must be signed in to change notification settings - Fork 14
/
Copy pathapp.py
566 lines (502 loc) · 26.1 KB
/
app.py
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
"""
Copyright (c) Meta Platforms, Inc. and affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
from tempfile import NamedTemporaryFile
import argparse
import torch
import gradio as gr
import os
import subprocess
import sys
from pathlib import Path
import time
import typing as tp
import warnings
from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write
from audiocraft.data.audio_utils import apply_fade, apply_tafade, apply_splice_effect
from audiocraft.utils.extend import generate_music_segments, add_settings_to_image, INTERRUPTING
import numpy as np
import random
#from pathlib import Path
#from typing import List, Union
import librosa
MODEL = None
MODELS = None
IS_SHARED_SPACE = "Surn/UnlimitedMusicGen" in os.environ.get('SPACE_ID', '')
INTERRUPTED = False
UNLOAD_MODEL = False
MOVE_TO_CPU = False
MAX_PROMPT_INDEX = 0
git = os.environ.get('GIT', "git")
def interrupt_callback():
return INTERRUPTED
def interrupt():
global INTERRUPTING
INTERRUPTING = True
class FileCleaner:
def __init__(self, file_lifetime: float = 3600):
self.file_lifetime = file_lifetime
self.files = []
def add(self, path: tp.Union[str, Path]):
self._cleanup()
self.files.append((time.time(), Path(path)))
def _cleanup(self):
now = time.time()
for time_added, path in list(self.files):
if now - time_added > self.file_lifetime:
if path.exists():
path.unlink()
self.files.pop(0)
else:
break
#file_cleaner = FileCleaner()
def toggle_audio_src(choice):
if choice == "mic":
return gr.update(source="microphone", value=None, label="Microphone")
else:
return gr.update(source="upload", value=None, label="File")
def make_waveform(*args, **kwargs):
# Further remove some warnings.
be = time.time()
with warnings.catch_warnings():
warnings.simplefilter('ignore')
out = gr.make_waveform(*args, **kwargs)
print("Make a video took", time.time() - be)
return out
def load_model(version):
global MODEL, MODELS, UNLOAD_MODEL
print("Loading model", version)
if MODELS is None:
return MusicGen.get_pretrained(version)
else:
t1 = time.monotonic()
if MODEL is not None:
MODEL.to('cpu') # move to cache
print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
t1 = time.monotonic()
if MODELS.get(version) is None:
print("Loading model %s from disk" % version)
result = MusicGen.get_pretrained(version)
MODELS[version] = result
print("Model loaded in %.2fs" % (time.monotonic() - t1))
return result
result = MODELS[version].to('cuda')
print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
return result
def get_filename(file):
# extract filename from file object
filename = None
if file is not None:
filename = file.name
return filename
def get_filename_from_filepath(filepath):
file_name = os.path.basename(filepath)
file_base, file_extension = os.path.splitext(file_name)
return file_base, file_extension
def get_melody(melody_filepath):
audio_data= list(librosa.load(melody_filepath, sr=None))
audio_data[0], audio_data[1] = audio_data[1], audio_data[0]
melody = tuple(audio_data)
return melody
def commit_hash():
try:
return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
except Exception:
return "<none>"
def git_tag():
try:
return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
except Exception:
try:
from pathlib import Path
changelog_md = Path(__file__).parent.parent / "CHANGELOG.md"
with changelog_md.open(encoding="utf-8") as file:
return next((line.strip() for line in file if line.strip()), "<none>")
except Exception:
return "<none>"
def versions_html():
import torch
python_version = ".".join([str(x) for x in sys.version_info[0:3]])
commit = commit_hash()
#tag = git_tag()
import xformers
xformers_version = xformers.__version__
return f"""
version: <a href="https://github.com/Oncorporation/audiocraft/commit/{"" if commit == "<none>" else commit}" target="_blank">{"click" if commit == "<none>" else commit}</a>
 • 
python: <span title="{sys.version}">{python_version}</span>
 • 
torch: {getattr(torch, '__long_version__',torch.__version__)}
 • 
xformers: {xformers_version}
 • 
gradio: {gr.__version__}
"""
def load_melody_filepath(melody_filepath, title):
# get melody filename
#$Union[str, os.PathLike]
symbols = ['_', '.', '-']
if (melody_filepath is None) or (melody_filepath == ""):
return title, gr.update(maximum=0, value=0) , gr.update(value="melody", interactive=True)
if (title is None) or ("MusicGen" in title) or (title == ""):
melody_name, melody_extension = get_filename_from_filepath(melody_filepath)
# fix melody name for symbols
for symbol in symbols:
melody_name = melody_name.replace(symbol, ' ').title()
else:
melody_name = title
print(f"Melody name: {melody_name}, Melody Filepath: {melody_filepath}\n")
# get melody length in number of segments and modify the UI
melody = get_melody(melody_filepath)
sr, melody_data = melody[0], melody[1]
segment_samples = sr * 30
total_melodys = max(min((len(melody_data) // segment_samples), 25), 0)
print(f"Melody length: {len(melody_data)}, Melody segments: {total_melodys}\n")
MAX_PROMPT_INDEX = total_melodys
return gr.Textbox.update(value=melody_name), gr.update(maximum=MAX_PROMPT_INDEX, value=0), gr.update(value="melody", interactive=False)
def predict(model, text, melody_filepath, duration, dimension, topk, topp, temperature, cfg_coef, background, title, settings_font, settings_font_color, seed, overlap=1, prompt_index = 0, include_title = True, include_settings = True, harmony_only = False):
global MODEL, INTERRUPTED, INTERRUPTING, MOVE_TO_CPU
output_segments = None
melody_name = "Not Used"
melody = None
if melody_filepath:
melody_name, melody_extension = get_filename_from_filepath(melody_filepath)
melody = get_melody(melody_filepath)
INTERRUPTED = False
INTERRUPTING = False
if temperature < 0:
raise gr.Error("Temperature must be >= 0.")
if topk < 0:
raise gr.Error("Topk must be non-negative.")
if topp < 0:
raise gr.Error("Topp must be non-negative.")
if MODEL is None or MODEL.name != model:
MODEL = load_model(model)
else:
if MOVE_TO_CPU:
MODEL.to('cuda')
# prevent hacking
duration = min(duration, 720)
overlap = min(overlap, 15)
#
output = None
segment_duration = duration
initial_duration = duration
output_segments = []
while duration > 0:
if not output_segments: # first pass of long or short song
if segment_duration > MODEL.lm.cfg.dataset.segment_duration:
segment_duration = MODEL.lm.cfg.dataset.segment_duration
else:
segment_duration = duration
else: # next pass of long song
if duration + overlap < MODEL.lm.cfg.dataset.segment_duration:
segment_duration = duration + overlap
else:
segment_duration = MODEL.lm.cfg.dataset.segment_duration
# implement seed
if seed < 0:
seed = random.randint(0, 0xffff_ffff_ffff)
torch.manual_seed(seed)
print(f'Segment duration: {segment_duration}, duration: {duration}, overlap: {overlap}')
MODEL.set_generation_params(
use_sampling=True,
top_k=topk,
top_p=topp,
temperature=temperature,
cfg_coef=cfg_coef,
duration=segment_duration,
two_step_cfg=False,
rep_penalty=0.5
)
if melody:
# todo return excess duration, load next model and continue in loop structure building up output_segments
if duration > MODEL.lm.cfg.dataset.segment_duration:
output_segments, duration = generate_music_segments(text, melody, seed, MODEL, duration, overlap, MODEL.lm.cfg.dataset.segment_duration, prompt_index, harmony_only=False)
else:
# pure original code
sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t().unsqueeze(0)
print(melody.shape)
if melody.dim() == 2:
melody = melody[None]
melody = melody[..., :int(sr * MODEL.lm.cfg.dataset.segment_duration)]
output = MODEL.generate_with_chroma(
descriptions=[text],
melody_wavs=melody,
melody_sample_rate=sr,
progress=False
)
# All output_segments are populated, so we can break the loop or set duration to 0
break
else:
#output = MODEL.generate(descriptions=[text], progress=False)
if not output_segments:
next_segment = MODEL.generate(descriptions=[text], progress=False)
duration -= segment_duration
else:
last_chunk = output_segments[-1][:, :, -overlap*MODEL.sample_rate:]
next_segment = MODEL.generate_continuation(last_chunk, MODEL.sample_rate, descriptions=[text], progress=True)
duration -= segment_duration - overlap
output_segments.append(next_segment)
if INTERRUPTING:
INTERRUPTED = True
INTERRUPTING = False
print("Function execution interrupted!")
raise gr.Error("Interrupted.")
if output_segments:
try:
# Combine the output segments into one long audio file or stack tracks
#output_segments = [segment.detach().cpu().float()[0] for segment in output_segments]
#output = torch.cat(output_segments, dim=dimension)
output = output_segments[0]
for i in range(1, len(output_segments)):
if overlap > 0:
overlap_samples = overlap * MODEL.sample_rate
#stack tracks and fade out/in
overlapping_output_fadeout = output[:, :, -overlap_samples:]
#overlapping_output_fadeout = apply_fade(overlapping_output_fadeout,sample_rate=MODEL.sample_rate,duration=overlap,out=True,start=True, curve_end=0.0, current_device=MODEL.device)
overlapping_output_fadeout = apply_tafade(overlapping_output_fadeout,sample_rate=MODEL.sample_rate,duration=overlap,out=True,start=True,shape="linear")
overlapping_output_fadein = output_segments[i][:, :, :overlap_samples]
#overlapping_output_fadein = apply_fade(overlapping_output_fadein,sample_rate=MODEL.sample_rate,duration=overlap,out=False,start=False, curve_start=0.0, current_device=MODEL.device)
overlapping_output_fadein = apply_tafade(overlapping_output_fadein,sample_rate=MODEL.sample_rate,duration=overlap,out=False,start=False, shape="linear")
overlapping_output = torch.cat([overlapping_output_fadeout[:, :, :-(overlap_samples // 2)], overlapping_output_fadein],dim=2)
###overlapping_output, overlap_sample_rate = apply_splice_effect(overlapping_output_fadeout, MODEL.sample_rate, overlapping_output_fadein, MODEL.sample_rate, overlap)
print(f" overlap size Fade:{overlapping_output.size()}\n output: {output.size()}\n segment: {output_segments[i].size()}")
##overlapping_output = torch.cat([output[:, :, -overlap_samples:], output_segments[i][:, :, :overlap_samples]], dim=1) #stack tracks
##print(f" overlap size stack:{overlapping_output.size()}\n output: {output.size()}\n segment: {output_segments[i].size()}")
#overlapping_output = torch.cat([output[:, :, -overlap_samples:], output_segments[i][:, :, :overlap_samples]], dim=2) #stack tracks
#print(f" overlap size cat:{overlapping_output.size()}\n output: {output.size()}\n segment: {output_segments[i].size()}")
output = torch.cat([output[:, :, :-overlap_samples], overlapping_output, output_segments[i][:, :, overlap_samples:]], dim=dimension)
else:
output = torch.cat([output, output_segments[i]], dim=dimension)
output = output.detach().cpu().float()[0]
except Exception as e:
print(f"Error combining segments: {e}. Using the first segment only.")
output = output_segments[0].detach().cpu().float()[0]
else:
output = output.detach().cpu().float()[0]
with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
video_description = f"{text}\n Duration: {str(initial_duration)} Dimension: {dimension}\n Top-k:{topk} Top-p:{topp}\n Randomness:{temperature}\n cfg:{cfg_coef} overlap: {overlap}\n Seed: {seed}\n Model: {model}\n Melody Condition:{melody_name}\n Sample Segment: {prompt_index}"
if include_settings or include_title:
background = add_settings_to_image(title if include_title else "", video_description if include_settings else "", background_path=background, font=settings_font, font_color=settings_font_color)
audio_write(
file.name, output, MODEL.sample_rate, strategy="loudness",
loudness_headroom_db=18, loudness_compressor=True, add_suffix=False, channels=2)
waveform_video = make_waveform(file.name,bg_image=background, bar_count=45)
if MOVE_TO_CPU:
MODEL.to('cpu')
if UNLOAD_MODEL:
MODEL = None
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
return waveform_video, file.name, seed
def ui(**kwargs):
css="""
#col-container {max-width: 910px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
#btn-generate {background-image:linear-gradient(to right bottom, rgb(157, 255, 157), rgb(229, 255, 235));}
#btn-generate:hover {background-image:linear-gradient(to right bottom, rgb(229, 255, 229), rgb(255, 255, 255));}
#btn-generate:active {background-image:linear-gradient(to right bottom, rgb(229, 255, 235), rgb(157, 255, 157));}
#versions {margin-top: 1em; width:100%; text-align:center;}
.small-btn {max-width:75px;}
"""
with gr.Blocks(title="UnlimitedMusicGen", css=css) as interface:
gr.Markdown(
"""
# Unlimited MusicGen
This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
Todo: Working on improved transitions between 30 second segments, improve Interrupt.
"""
)
if IS_SHARED_SPACE and not torch.cuda.is_available():
gr.Markdown("""
⚠ This Space doesn't work in this shared UI ⚠
<a href="https://huggingface.co/spaces/musicgen/MusicGen?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
to use it privately, or use the <a href="https://huggingface.co/spaces/facebook/MusicGen">public demo</a>
""")
with gr.Row():
with gr.Column():
with gr.Row():
text = gr.Text(label="Describe your music", interactive=True, value="4/4 100bpm 320kbps 48khz, Industrial/Electronic Soundtrack, Dark, Intense, Sci-Fi")
with gr.Column():
duration = gr.Slider(minimum=1, maximum=720, value=10, label="Duration (s)", interactive=True)
model = gr.Radio(["melody", "medium", "small", "large"], label="AI Model", value="melody", interactive=True)
with gr.Row():
submit = gr.Button("Generate", elem_id="btn-generate")
# Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
_ = gr.Button("Interrupt", elem_id="btn-interrupt").click(fn=interrupt, queue=False)
with gr.Row():
with gr.Column():
radio = gr.Radio(["file", "mic"], value="file", label="Condition on a melody (optional) File or Mic")
melody_filepath = gr.Audio(source="upload", type="filepath", label="Melody Condition (optional)", interactive=True, elem_id="melody-input")
with gr.Column():
harmony_only = gr.Radio(label="Use Harmony Only",choices=["No", "Yes"], value="No", interactive=True, info="Remove Drums?")
prompt_index = gr.Slider(label="Melody Condition Sample Segment", minimum=-1, maximum=MAX_PROMPT_INDEX, step=1, value=0, interactive=True, info="Which 30 second segment to condition with, - 1 condition each segment independantly")
with gr.Accordion("Video", open=False):
with gr.Row():
background= gr.Image(value="./assets/background.png", source="upload", label="Background", shape=(768,512), type="filepath", interactive=True)
with gr.Column():
include_title = gr.Checkbox(label="Add Title", value=True, interactive=True)
include_settings = gr.Checkbox(label="Add Settings to background", value=True, interactive=True)
with gr.Row():
title = gr.Textbox(label="Title", value="UnlimitedMusicGen", interactive=True)
settings_font = gr.Text(label="Settings Font", value="./assets/arial.ttf", interactive=True)
settings_font_color = gr.ColorPicker(label="Settings Font Color", value="#c87f05", interactive=True)
with gr.Accordion("Expert", open=False):
with gr.Row():
overlap = gr.Slider(minimum=0, maximum=15, value=2, step=1, label="Verse Overlap", interactive=True)
dimension = gr.Slider(minimum=-2, maximum=2, value=2, step=1, label="Dimension", info="determines which direction to add new segements of audio. (1 = stack tracks, 2 = lengthen, -2..0 = ?)", interactive=True)
with gr.Row():
topk = gr.Number(label="Top-k", value=280, precision=0, interactive=True)
topp = gr.Number(label="Top-p", value=1150, precision=0, interactive=True)
temperature = gr.Number(label="Randomness Temperature", value=0.7, precision=None, interactive=True)
cfg_coef = gr.Number(label="Classifier Free Guidance", value=8.5, precision=None, interactive=True)
with gr.Row():
seed = gr.Number(label="Seed", value=-1, precision=0, interactive=True)
gr.Button('\U0001f3b2\ufe0f', elem_classes="small-btn").click(fn=lambda: -1, outputs=[seed], queue=False)
reuse_seed = gr.Button('\u267b\ufe0f', elem_classes="small-btn")
with gr.Column() as c:
output = gr.Video(label="Generated Music")
wave_file = gr.File(label=".wav file", elem_id="output_wavefile", interactive=True)
seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
radio.change(toggle_audio_src, radio, [melody_filepath], queue=False, show_progress=False)
melody_filepath.change(load_melody_filepath, inputs=[melody_filepath, title], outputs=[title, prompt_index , model], api_name="melody_filepath_change", queue=False)
reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False, api_name="reuse_seed")
submit.click(predict, inputs=[model, text,melody_filepath, duration, dimension, topk, topp, temperature, cfg_coef, background, title, settings_font, settings_font_color, seed, overlap, prompt_index, include_title, include_settings, harmony_only], outputs=[output, wave_file, seed_used], api_name="submit")
gr.Examples(
fn=predict,
examples=[
[
"4/4 120bpm 320kbps 48khz, An 80s driving pop song with heavy drums and synth pads in the background",
"./assets/bach.mp3",
"melody",
"80s Pop Synth"
],
[
"4/4 120bpm 320kbps 48khz, A cheerful country song with acoustic guitars",
"./assets/bolero_ravel.mp3",
"melody",
"Country Guitar"
],
[
"4/4 120bpm 320kbps 48khz, 90s rock song with electric guitar and heavy drums",
None,
"medium",
"90s Rock Guitar"
],
[
"4/4 120bpm 320kbps 48khz, a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions",
"./assets/bach.mp3",
"melody",
"EDM my Bach"
],
[
"4/4 320kbps 48khz, lofi slow bpm electro chill with organic samples",
None,
"medium",
"LoFi Chill"
],
],
inputs=[text, melody_filepath, model, title],
outputs=[output]
)
gr.Markdown(
"""
### More details
The model will generate a short music extract based on the description you provided.
You can generate up to 30 seconds of audio.
We present 4 model variations:
1. Melody -- a music generation model capable of generating music condition on text and melody inputs. **Note**, you can also use text only.
2. Small -- a 300M transformer decoder conditioned on text only.
3. Medium -- a 1.5B transformer decoder conditioned on text only.
4. Large -- a 3.3B transformer decoder conditioned on text only (might OOM for the longest sequences.)
When using `melody`, ou can optionaly provide a reference audio from
which a broad melody will be extracted. The model will then try to follow both the description and melody provided.
You can also use your own GPU or a Google Colab by following the instructions on our repo.
See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
for more details.
"""
)
gr.HTML(value=versions_html(), visible=True, elem_id="versions")
# Show the interface
launch_kwargs = {}
username = kwargs.get('username')
password = kwargs.get('password')
server_port = kwargs.get('server_port', 0)
inbrowser = kwargs.get('inbrowser', False)
share = kwargs.get('share', False)
server_name = kwargs.get('listen')
launch_kwargs['server_name'] = server_name
if username and password:
launch_kwargs['auth'] = (username, password)
if server_port > 0:
launch_kwargs['server_port'] = server_port
if inbrowser:
launch_kwargs['inbrowser'] = inbrowser
if share:
launch_kwargs['share'] = share
launch_kwargs['favicon_path']= "./assets/favicon.ico"
interface.queue().launch(**launch_kwargs, max_threads=1)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
'--listen',
type=str,
default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
help='IP to listen on for connections to Gradio',
)
parser.add_argument(
'--username', type=str, default='', help='Username for authentication'
)
parser.add_argument(
'--password', type=str, default='', help='Password for authentication'
)
parser.add_argument(
'--server_port',
type=int,
default=7859,
help='Port to run the server listener on',
)
parser.add_argument(
'--inbrowser', action='store_true', help='Open in browser'
)
parser.add_argument(
'--share', action='store_true', help='Share the gradio UI'
)
parser.add_argument(
'--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
)
parser.add_argument(
'--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
)
parser.add_argument(
'--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
)
args = parser.parse_args()
launch_kwargs = {}
launch_kwargs['server_name'] = args.listen
if args.username and args.password:
launch_kwargs['auth'] = (args.username, args.password)
if args.server_port:
launch_kwargs['server_port'] = args.server_port
if args.inbrowser:
launch_kwargs['inbrowser'] = args.inbrowser
if args.share:
launch_kwargs['share'] = args.share
launch_kwargs['favicon_path']= "./assets/favicon.ico"
if args.cache:
MODELS = {}
ui(
username=args.username,
password=args.password,
inbrowser=args.inbrowser,
server_port=args.server_port,
share=args.share,
listen=args.listen
)