Spaces:
Build error
Build error
File size: 20,172 Bytes
dab2de2 077e1eb d6fcb23 dab2de2 077e1eb 3d38118 f4cdef5 077e1eb dab2de2 f4cdef5 dab2de2 077e1eb d5ff673 077e1eb dab2de2 a65b4e5 dab2de2 2f6222b dab2de2 d5ff673 dab2de2 a65b4e5 077e1eb a65b4e5 07f1d23 dab2de2 3d38118 2f6222b dab2de2 d5ff673 dab2de2 2f6222b dab2de2 077e1eb dab2de2 07f1d23 dab2de2 077e1eb dab2de2 2f6222b dab2de2 8e72bb5 d6fcb23 8e72bb5 d6fcb23 8e72bb5 07f1d23 efedef5 a65b4e5 d5ff673 dab2de2 2f6222b dab2de2 8e72bb5 dab2de2 51055fa d5ff673 51055fa 2f6222b 79b8183 51055fa 2f6222b 3d38118 2f6222b 3d38118 2f6222b dab2de2 d5ff673 efedef5 9b23c5c efedef5 dab2de2 8e72bb5 dab2de2 3d38118 dab2de2 2f6222b dab2de2 3d38118 dab2de2 3d38118 2f6222b 3d38118 dab2de2 2f6222b 51055fa 3d38118 51055fa 3d38118 dab2de2 3d38118 51055fa dab2de2 8e72bb5 dab2de2 3d38118 efedef5 8e72bb5 efedef5 8e72bb5 efedef5 8e72bb5 3d38118 d5ff673 3d38118 d5ff673 3d38118 2f6222b d5ff673 dab2de2 3d38118 077e1eb d5ff673 077e1eb d5ff673 f4cdef5 d5ff673 3d38118 dab2de2 3d38118 52771bf 3d38118 56441fd d5ff673 3d38118 dab2de2 2f6222b efedef5 2f6222b 1755d03 2f6222b dc641b6 3d38118 077e1eb 1bdc1b2 dc641b6 efedef5 dbac531 efedef5 4666df2 dbac531 4666df2 dbac531 efedef5 d5ff673 4666df2 d5ff673 8e72bb5 2f6222b dab2de2 3d38118 dab2de2 7327eea dab2de2 efedef5 d5ff673 dab2de2 d5ff673 f4cdef5 4666df2 dab2de2 efedef5 8e72bb5 dab2de2 |
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 |
"""Run interactively."""
# pylint: disable=invalid-name, too-many-arguments, unused-argument, redefined-builtin, wrong-import-position, too-many-locals, too-many-statements
from typing import Any, Tuple, Optional, Union # noqa
import sys
from pathlib import Path
import platform
import signal
from random import randint
from textwrap import dedent
from itertools import zip_longest
# import socket
from socket import socket, AF_INET, SOCK_STREAM
from sklearn.cluster import DBSCAN # noqa
import joblib
from varname import nameof
import logzero
from logzero import logger
# import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt # noqa
# from tabulate import tabulate
from fastlid import fastlid
# for embeddable python
if "." not in sys.path:
sys.path.insert(0, ".")
import gradio as gr
from radiobee.process_upload import process_upload
from radiobee.files2df import files2df
from radiobee.file2text import file2text
from radiobee.lists2cmat import lists2cmat
from radiobee.gen_pset import gen_pset
from radiobee.gen_aset import gen_aset
from radiobee.align_texts import align_texts
from radiobee.cmat2tset import cmat2tset
# from radiobee.plot_df import plot_df
# from radiobee.plot_cmat import plot_cmat
from radiobee.trim_df import trim_df
sns.set()
sns.set_style("darkgrid")
fastlid.set_languages = ["en", "zh"]
signal.signal(signal.SIGINT, signal.SIG_DFL)
print("Press Ctrl+C to quit\n")
def savelzma(obj, fileloc: str = None):
"""Aux funciton."""
if fileloc is None:
fileloc = nameof(obj) # this wont work
joblib.dump(obj, f"data/{fileloc}.lzma")
def greet(input):
"""Greet yo."""
return f"'Sup yo! (your input: {input})"
def upfile1(file1, file2=None) -> Tuple[str, str]:
"""Upload file1, file2."""
del file2
return file1.name, f"'Sup yo! (your input: {input})"
def process_2upoads(file1, file2):
"""Process stuff."""
# return f"{process_upload(file1)}\n===***\n{process_upload(file2)}"
text1 = [_.strip() for _ in process_upload(file1).splitlines() if _.strip()]
text2 = [_.strip() for _ in process_upload(file2).splitlines() if _.strip()]
text1, text2 = zip(*zip_longest(text1, text2, fillvalue=""))
df = pd.DataFrame({"text1": text1, "text2": text2})
# return tabulate(df)
# return tabulate(df, tablefmt="grid")
# return tabulate(df, tablefmt='html')
return df
def error_msg(
msg: Optional[str],
title: str = "error message",
# ) -> Tuple[Union[pd.DataFrame, None], None, None, None, None]:
):
"""Prepare error message for fn outputs."""
if msg is None:
msg = "none..."
try:
msg = msg.__str__()
except Exception as exc:
msg = str(exc)
df = pd.DataFrame([msg], columns=[title])
return (df, *((None,) * 4))
if __name__ == "__main__":
debug = True
# debug = False
uname = platform.uname()
# match = re.search(r'[a-z\d]{10,}', gethostname())
# hf spaces release: '4.14.248-189.473.amzn2.x86_64'
# match = re.search(r'[a-z\d]{10,}', node)
# if match and node.system.lower() in ["linux"]:
if "amzn2" in uname.release:
# likely hf spaces
server_name = "0.0.0.0"
debug = False
else:
server_name = "127.0.0.1"
if debug:
logzero.loglevel(10)
logger.debug(" debug ")
logger.info(" info ")
# _ = """
inputs = [
gr.inputs.Textbox(
# placeholder="Input something here",
default="test text"
)
]
inputs = ["file", "file"]
inputs = [
gr.inputs.File(label="file 1"),
# gr.inputs.File(file_count="multiple", label="file 2", optional=True),
gr.inputs.File(label="file 2", optional=True),
]
# modi 1
_ = """
tf_type: Literal[linear, sqrt, log, binary] = 'linear'
idf_type: Optional[Literal[standard, smooth, bm25]] = None
dl_type: Optional[Literal[linear, sqrt, log]] = None
norm: norm: Optional[Literal[l1, l2]] = None
x min_df: int | float = 1
x max_df: int | float = 1.0
# """
input_tf_type = gr.inputs.Dropdown(
["linear", "sqrt", "log", "binary"], default="linear"
)
input_idf_type = gr.inputs.Radio(
["None", "standard", "smooth", "bm25"], default="None"
) # need to convert "None" this to None in fn
input_dl_type = gr.inputs.Radio(
["None", "linear", "sqrt", "log"], default="None"
) # ditto
input_norm_type = gr.inputs.Radio(["None", "l1", "l2"], default="None") # ditto
inputs = [
gr.inputs.File(label="file 1"),
gr.inputs.File(label="file 2", optional=True),
input_tf_type, # modi inputs
input_idf_type,
input_dl_type,
input_norm_type,
gr.inputs.Slider(
minimum=1,
maximum=20,
step=0.1,
default=10,
),
gr.inputs.Slider(
minimum=1,
maximum=20,
step=1,
default=6,
),
]
examples = [
[
"data/test_zh.txt",
"data/test_en.txt",
"linear",
"None",
"None",
"None",
10,
6,
],
[
"data/test_en.txt",
"data/test_zh.txt",
"linear",
"None",
"None",
"None",
10,
6,
],
[
"data/shakespeare_zh500.txt",
"data/shakespeare_en500.txt",
"linear",
"None",
"None",
"None",
10,
6,
],
[
"data/shakespeare_en500.txt",
"data/shakespeare_zh500.txt",
"linear",
"None",
"None",
"None",
10,
6,
],
[
"data/hlm-ch1-zh.txt",
"data/hlm-ch1-en.txt",
"linear",
"None",
"None",
"None",
10,
6,
],
[
"data/hlm-ch1-en.txt",
"data/hlm-ch1-zh.txt",
"linear",
"None",
"None",
"None",
10,
6,
],
[
"data/ps-cn.txt",
"data/ps-en.txt",
"linear",
"None",
"None",
"None",
10,
4,
],
[
"data/test-dual.txt",
"data/empty.txt",
"linear",
"None",
"None",
"None",
10,
6,
],
]
# modi examples setup
outputs = ["dataframe", "plot"]
outputs = ["plot"]
outputs = ["dataframe", "plot"]
out_df = gr.outputs.Dataframe(
headers=None,
max_rows=12, # 20
max_cols=None,
overflow_row_behaviour="paginate",
type="auto",
label="To be aligned",
)
out_df_aligned = gr.outputs.Dataframe(
headers=None,
# max_rows=12, # 20
max_cols=3,
overflow_row_behaviour="paginate",
type="auto",
label="aligned pairs",
)
out_file_dl = gr.outputs.File(
label="Click to download csv",
)
out_file_dl_excel = gr.outputs.File(
label="Click to download xlsx",
)
# modi outputs
outputs = [
out_df,
"plot",
out_file_dl,
out_file_dl_excel,
out_df_aligned,
]
# outputs = ["dataframe", "plot", "plot"] # wont work
# outputs = ["dataframe"]
# outputs = ["dataframe", "dataframe", ]
# def fn(file1, file2):
# def fn(file1, file2, min_samples, eps):
def fn(
file1,
file2,
tf_type,
idf_type,
dl_type,
norm,
eps,
min_samples,
):
# modi fn
"""Process inputs and return outputs."""
logger.debug(" *debug* ")
# conver "None" to None for those Radio types
for _ in [idf_type, dl_type, norm]:
if _ in "None":
_ = None
# logger.info("file1: *%s*, file2: *%s*", file1, file2)
if file2 is not None:
logger.info("file1.name: *%s*, file2.name: *%s*", file1.name, file2.name)
else:
logger.info("file1.name: *%s*, file2: *%s*", file1.name, file2)
# bypass if file1 or file2 is str input
# if not (isinstance(file1, str) or isinstance(file2, str)):
text1 = file2text(file1)
if file2 is None:
logger.debug("file2 is None")
text2 = ""
# TODO split text1 to text1 and text2
else:
logger.debug("file2.name: %s", file2.name)
text2 = file2text(file2)
if not text1 or not text2:
msg = (
"one of the files is empty: "
f"text1[:10]: [{text1[:10]}], text2[:10]: [{text2[:10]}]"
)
# return (pd.DataFrame([msg], columns=['error message']), *((None,) * 4))
return error_msg(msg)
lang1, _ = fastlid(text1)
lang2, _ = fastlid(text2)
df1 = files2df(file1, file2)
lst1 = [elm for elm in df1.text1 if elm]
lst2 = [elm for elm in df1.text2 if elm]
# len1 = len(lst1) # noqa
# len2 = len(lst2) # noqa
cmat = lists2cmat(
lst1,
lst2,
tf_type=tf_type,
idf_type=idf_type,
dl_type=dl_type,
norm=norm,
)
tset = pd.DataFrame(cmat2tset(cmat))
tset.columns = ["x", "y", "cos"]
df_trimmed = trim_df(df1)
_ = """
df_trimmed = pd.concat(
[
df1.iloc[:4, :],
pd.DataFrame(
[
[
"...",
"...",
]
],
columns=df1.columns,
),
df1.iloc[-4:, :],
],
ignore_index=1,
)
# """
# process lst1, lst2 to obtained df_aligned
# quick fix ValueError: not enough values to unpack (expected at least 1, got 0)
# fixed in gen_pet, but we leave the loop here
for min_s in range(min_samples):
logger.info(" min_samples, using %s", min_samples - min_s)
try:
pset = gen_pset(
cmat,
eps=eps,
min_samples=min_samples - min_s,
delta=7,
)
break
except ValueError:
logger.info(" decrease min_samples by %s", min_s + 1)
continue
except Exception as e:
logger.error(e)
continue
else:
# break should happen above when min_samples = 2
raise Exception("bummer, this shouldn't happen, probably another bug")
min_samples = gen_pset.min_samples
# will result in error message:
# UserWarning: Starting a Matplotlib GUI outside of
# the main thread will likely fail."
_ = """
plot_cmat(
cmat,
eps=eps,
min_samples=min_samples,
xlabel=lang1,
ylabel=lang2,
)
# """
# move plot_cmat's code to the main thread here
# to make it work
xlabel = lang1
ylabel = lang2
len1, len2 = cmat.shape
ylim, xlim = len1, len2
# does not seem to show up
logger.debug(" len1 (ylim): %s, len2 (xlim): %s", len1, len2)
if debug:
print(f" len1 (ylim): {len1}, len2 (xlim): {len2}")
df_ = pd.DataFrame(cmat2tset(cmat))
df_.columns = ["x", "y", "cos"]
sns.set()
sns.set_style("darkgrid")
# close all existing figures, necesssary for hf spaces
plt.close("all")
# if sys.platform not in ["win32", "linux"]:
plt.switch_backend('Agg') # to cater for Mac, thanks to WhiteFox
# figsize=(13, 8), (339, 212) mm on '1280x800+0+0'
fig = plt.figure(figsize=(13, 8))
# gs = fig.add_gridspec(2, 2, wspace=0.4, hspace=0.58)
gs = fig.add_gridspec(1, 2, wspace=0.4, hspace=0.58)
ax_heatmap = fig.add_subplot(gs[0, 0]) # ax2
ax0 = fig.add_subplot(gs[0, 1])
# ax1 = fig.add_subplot(gs[1, 0])
cmap = "viridis_r"
sns.heatmap(cmat, cmap=cmap, ax=ax_heatmap).invert_yaxis()
ax_heatmap.set_xlabel(xlabel)
ax_heatmap.set_ylabel(ylabel)
ax_heatmap.set_title("cos similarity heatmap")
fig.suptitle(f"alignment projection\n(eps={eps}, min_samples={min_samples})")
_ = DBSCAN(min_samples=min_samples, eps=eps).fit(df_).labels_ > -1
# _x = DBSCAN(min_samples=min_samples, eps=eps).fit(df_).labels_ < 0
_x = ~_
# max cos along columns
df_.plot.scatter("x", "y", c="cos", cmap=cmap, ax=ax0)
# outliers
df_[_x].plot.scatter("x", "y", c="r", marker="x", alpha=0.6, ax=ax0)
ax0.set_xlabel(xlabel)
ax0.set_ylabel(ylabel)
ax0.set_xlim(xmin=0, xmax=xlim)
ax0.set_ylim(ymin=0, ymax=ylim)
ax0.set_title(
"max along columns ('x': outliers)\n"
"potential aligned pairs (green line)\n"
f"({round(sum(_) / xlim, 2):.0%})"
)
# clustered
# df_[_].plot.scatter("x", "y", c="cos", cmap=cmap, ax=ax1)
# ax1.set_xlabel(xlabel)
# ax1.set_ylabel(ylabel)
# ax1.set_xlim(0, len1)
# ax1.set_title(f"potential aligned pairs ({round(sum(_) / len1, 2):.0%})")
# end of plot_cmat
src_len, tgt_len = cmat.shape
aset = gen_aset(pset, src_len, tgt_len)
final_list = align_texts(aset, lst2, lst1) # note the order
# df_aligned = df_trimmed
df_aligned = pd.DataFrame(final_list, columns=["text1", "text2", "likelihood"])
# swap text1 text2
df_aligned = df_aligned[["text2", "text1", "likelihood"]]
df_aligned.columns = ["text1", "text2", "likelihood"]
_ = df_aligned.to_csv(index=False)
file_dl = Path(f"{Path(file1.name).stem[:-8]}-{Path(file2.name).stem[:-8]}.csv")
file_dl.write_text(_, encoding="utf8")
# file_dl.write_text(_, encoding="gb2312") # no go
file_dl_xlsx = Path(
f"{Path(file1.name).stem[:-8]}-{Path(file2.name).stem[:-8]}.xlsx"
)
df_aligned.to_excel(file_dl_xlsx)
# return df_trimmed, plt
return df_trimmed, plt, file_dl, file_dl_xlsx, df_aligned
# modi outputs
server_port = 7860
with socket(AF_INET, SOCK_STREAM) as sock:
sock.settimeout(0.01) # 10ms
# try numb times before giving up
numb = 5
for _ in range(numb):
if sock.connect_ex(("127.0.0.1", server_port)) != 0: # port idle
break
server_port = server_port + randint(0, 50)
else:
raise SystemExit(f"Tried {numb} times to no avail, giving up...")
# moved to userguide.rst in docs
article = dedent(
"""
## NB
* `radiobee aligner` is a sibling of `bumblebee aligner`. To know more about these aligners, please join qq group `316287378`.
* Uploaded files should be in pure text format (txt, md, csv etc). `docx`, `pdf`, `srt`, `html` etc may be supported later on.
* Click "Clear" first for subsequent submits when uploading files.
* `tf_type` `idf_type` `dl_type` `norm`: Normally there is no need to touch these unless you know what you are doing.
* Suggested `esp` and `min_samples` values -- `esp` (minimum epsilon): 8-12, `min_samples`: 4-8.
- Smaller larger `esp` or `min_samples` will result in more aligned pairs but also more **false positives** (pairs
falsely identified as candidates). On the other hand,
larger smaller `esp` or `min_samples` values tend to miss
'good' pairs.
* If you need to have a better look at the image, you can right-click on the image and select copy-image-address and open a new tab in the browser with the copied image address.
* `Flag`: Should something go wrong, you can click Flag to save the output and inform the developer.
"""
).strip()
# "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.11641' target='_blank'>JoJoGAN: One Shot Face Stylization</a>| <a href='https://github.com/mchong6/JoJoGAN' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_jojogan' alt='visitor badge'></center> <p style='text-align: center'>samples from repo: <img src='https://raw.githubusercontent.com/mchong6/JoJoGAN/main/teaser.jpg' alt='animation'/></p>" # noqa
article = dedent(
""" <p style="text-align: center">readiobee docs:
<a href="https://radiobee.readthedocs.io/" target="_blank">readthedocs</a>
or in Chinese but rather short <a href="https://radiobee.readthedocs.io/en/latest/userguide-zh.html#" target="_blank">中文使用说明</a>
</p>
"""
).strip()
css_image = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
# css = ".output_image, .input_image {height: 20rem !important; width: 100% !important;}"
css_input_file = (
".input_file {height: 8rem !important; width: 100% !important;}"
)
css_output_file = (
".output_file {height: 4rem !important; width: 100% !important;}"
)
logger.info("running at port %s", server_port)
iface = gr.Interface(
# fn=greet,
# inputs="text",
# fn=process_upload,
# fn=process_2upoads,
# inputs=["file", "file"],
# outputs="text",
# outputs="html",
fn=fn,
inputs=inputs,
outputs=outputs,
title="radiobee-aligner🔠",
description="WIP showcasing a blazing fast dualtext aligner, currrently supported language pairs: en-zh/zh-en",
article=article,
examples=examples,
# theme="darkgrass",
theme="grass",
layout="vertical", # horizontal unaligned
allow_flagging="auto",
flagging_options=[
"fatal",
"bug",
"brainstorm",
"excelsior",
], # "paragon"],
css=f"{css_image} {css_input_file} {css_output_file}",
)
iface.launch(
share=False,
# share=True,
debug=debug,
server_name=server_name,
# server_name="127.0.0.1",
server_port=server_port,
# show_tips=True,
enable_queue=True,
# height=150, # 500
width=900, # 900
)
_ = """
ax = sns.heatmap(cmat, cmap="viridis_r")
ax.invert_yaxis()
ax.set_xlabel(fastlid(df.text1)[0])
ax.set_xlabel(fastlid(df.text2)[0])
# return df, plt
return plt.gca()
https://colab.research.google.com/drive/1Gz9624VeAQLT7wlETgjOjPVURzQckXI0#scrollTo=qibtTvwecgsL colab gradio-file-inputs-upload.ipynb
iface = gr.Interface(plot_text, "file", "image")
def is_port_in_use(port):
import socket
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
return s.connect_ex(('localhost', port)) == 0
socket.socket(socket.AF_INET, socket.SOCK_STREAM).connect_ex(('127.0.0.1', 7911))
---
css https://huggingface.co./spaces/nielsr/LayoutLMv2-FUNSD/blob/main/app.py#L83
css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
#css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
# css = ".output_image, .input_image {height: 600px !important}"
mod = 'en2zh'
packname = packx.__name__
globals()[mod] = getattr(importlib.import_module(f"{packname}.{mod}"), mod)
"""
|