Spaces:
Runtime error
Runtime error
File size: 20,792 Bytes
e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 cc5f850 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 9e0003c c1dd675 9e0003c c1dd675 9e0003c c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 9e0003c e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 9e0003c e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 9e0003c e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 9e0003c e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 9e0003c e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 9e0003c e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 e1d0c26 c1dd675 |
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 |
import streamlit as st
import pandas as pd
from transformers import BertTokenizer, TFBertForSequenceClassification
from transformers import TextClassificationPipeline
from transformers import pipeline
from stqdm import stqdm
from simplet5 import SimpleT5
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import BertTokenizer, TFBertForSequenceClassification
import logging
from datasets import load_dataset
import gc
from typing import List
from collections import OrderedDict
from datetime import datetime
tokenizer_kwargs = dict(max_length=128, truncation=True, padding=True)
flan_t5_kwargs = dict(repetition_penalty=1.2)
SLEEP = 2
date = datetime.now().strftime(r"%Y-%m-%d")
def clean_memory(obj: TextClassificationPipeline):
del obj
gc.collect()
@st.cache_data
def get_all_cats():
data = load_dataset("ashhadahsan/amazon_theme")
data = data["train"].to_pandas()
labels = [x for x in list(set(data.iloc[:, 1].values.tolist())) if x != "Unknown"]
del data
return labels
@st.cache_data
def get_all_subcats():
data = load_dataset("ashhadahsan/amazon_subtheme")
data = data["train"].to_pandas()
labels = [x for x in list(set(data.iloc[:, 1].values.tolist())) if x != "Unknown"]
del data
return labels
@st.cache_resource
def load_zero_shot_classification_large():
classifier_zero = pipeline(
"zero-shot-classification",
model="facebook/bart-large-mnli",
)
return classifier_zero
def assign_label_zeroshot(zero, to: str, old: List):
assigned = zero(to, old)
assigned_dict = dict(zip(assigned["labels"], assigned["scores"]))
od = OrderedDict(sorted(assigned_dict.items(), key=lambda x: x[1], reverse=True))
print(list(od.keys())[0])
print(type(list(od.keys())[0]))
return list(od.keys())[0]
def assign_labels_flant5(pipe, what: str, to: str, old: List):
old = ", ".join(old)
return pipe(
f"""'Generate a new one word {what} to this summary of the text of a review
{to} for context
already assigned {what} are , {themes}
theme:"""
)[0]["generated_text"]
@st.cache_resource
def load_t5() -> (AutoModelForSeq2SeqLM, AutoTokenizer):
model = AutoModelForSeq2SeqLM.from_pretrained(
"t5-base",
)
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path="t5-base",
)
return model, tokenizer
@st.cache_resource
def load_flan_t5_large():
return pipeline(
task="text2text-generation",
model="google/flan-t5-large",
model_kwargs=flan_t5_kwargs,
)
@st.cache_resource
def summarizationModel():
return pipeline(
task="summarization",
model="my_awesome_sum/",
)
@st.cache_resource
def convert_df(df: pd.DataFrame):
return df.to_csv(index=False).encode("utf-8")
def load_one_line_summarizer(model):
return model.load_model(
"t5",
"snrspeaks/t5-one-line-summary",
)
@st.cache_resource
def classify_theme() -> TextClassificationPipeline:
tokenizer = BertTokenizer.from_pretrained(
"ashhadahsan/amazon-theme-bert-base-finetuned",
)
model = TFBertForSequenceClassification.from_pretrained(
"ashhadahsan/amazon-theme-bert-base-finetuned",
)
pipeline = TextClassificationPipeline(
model=model,
tokenizer=tokenizer,
**tokenizer_kwargs,
)
return pipeline
@st.cache_resource
def classify_sub_theme() -> TextClassificationPipeline:
tokenizer = BertTokenizer.from_pretrained(
"ashhadahsan/amazon-subtheme-bert-base-finetuned",
)
model = TFBertForSequenceClassification.from_pretrained(
"ashhadahsan/amazon-subtheme-bert-base-finetuned",
)
pipeline = TextClassificationPipeline(
model=model, tokenizer=tokenizer, **tokenizer_kwargs
)
return pipeline
st.set_page_config(layout="wide", page_title="Amazon Review | Summarizer")
st.title(body="Amazon Review Summarizer")
uploaded_file = st.file_uploader(label="Choose a file", type=["xlsx", "xls", "csv"])
summarizer_option = st.selectbox(
label="Select Summarizer",
options=("Custom trained on the dataset", "t5-base", "t5-one-line-summary"),
)
col1, col2, col3 = st.columns(spec=[1, 1, 1])
with col1:
summary_yes = st.checkbox(label="Summrization", value=False)
with col2:
classification = st.checkbox(label="Classify Category", value=True)
with col3:
sub_theme = st.checkbox(label="Sub theme classification", value=True)
treshold = st.slider(
label="Model Confidence value",
min_value=0.1,
max_value=0.8,
step=0.1,
value=0.6,
help="If the model has a confidence score below this number , then a new label is assigned (0.6) means 60 percent and so on",
)
ps = st.empty()
if st.button("Process", type="primary"):
themes = get_all_cats()
subthemes = get_all_subcats()
oneline = SimpleT5()
load_one_line_summarizer(model=oneline)
zeroline = load_zero_shot_classification_large()
bot = load_flan_t5_large()
cancel_button = st.empty()
cancel_button2 = st.empty()
cancel_button3 = st.empty()
if uploaded_file is not None:
if uploaded_file.name.split(".")[-1] in ["xls", "xlsx"]:
df = pd.read_excel(io=uploaded_file, engine="openpyxl")
if uploaded_file.name.split(".")[-1] in [".csv"]:
df = pd.read_csv(filepath_or_buffer=uploaded_file)
columns = df.columns.values.tolist()
columns = [x.lower() for x in columns]
df.columns = columns
print(summarizer_option)
outputdf = pd.DataFrame()
try:
text = df["text"].values.tolist()
outputdf["text"] = text
if summarizer_option == "Custom trained on the dataset":
if summary_yes:
model = summarizationModel()
progress_text = "Summarization in progress. Please wait."
summary = []
for x in stqdm(iterable=range(len(text))):
if cancel_button.button("Cancel", key=x):
del model
break
try:
summary.append(
model(
f"summarize: {text[x]}",
max_length=50,
early_stopping=True,
)[0]["summary_text"]
)
except:
pass
outputdf["summary"] = summary
del model
if classification:
themePipe = classify_theme()
classes = []
classesUnlabel = []
classesUnlabelZero = []
for x in stqdm(
iterable=text,
desc="Assigning Themes ...",
total=len(text),
colour="#BF1A1A",
):
output = themePipe(x)[0]["label"]
classes.append(output)
score = round(number=themePipe(x)[0]["score"], ndigits=2)
if score <= treshold:
onelineoutput = oneline.predict(source_text=x)[0]
print("hit")
classesUnlabel.append(
assign_labels_flant5(
bot,
what="theme",
to=onelineoutput,
old=themes,
)
)
classesUnlabelZero.append(
assign_label_zeroshot(
zero=zeroline, to=onelineoutput, old=themes
)
)
else:
classesUnlabel.append("")
classesUnlabelZero.append("")
outputdf["Review Theme"] = classes
outputdf["Review Theme-issue-new"] = classesUnlabel
outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
clean_memory(themePipe)
if sub_theme:
subThemePipe = classify_sub_theme()
classes = []
classesUnlabel = []
classesUnlabelZero = []
for x in stqdm(
iterable=text,
desc="Assigning Subthemes ...",
total=len(text),
colour="green",
):
output = subThemePipe(x)[0]["label"]
classes.append(output)
score = round(subThemePipe(x)[0]["score"], 2)
if score <= treshold:
onelineoutput = oneline.predict(x)[0]
print("hit")
classesUnlabel.append(
assign_labels_flant5(
bot,
what="subtheme",
to=onelineoutput,
old=subthemes,
)
)
classesUnlabelZero.append(
assign_label_zeroshot(
zero=zeroline,
to=onelineoutput,
old=subthemes,
)
)
else:
classesUnlabel.append("")
classesUnlabelZero.append("")
outputdf["Review SubTheme"] = classes
outputdf["Review SubTheme-issue-new"] = classesUnlabel
outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
clean_memory(subThemePipe)
csv = convert_df(outputdf)
st.download_button(
label="Download output as CSV",
data=csv,
file_name=f"{summarizer_option}_{date}_df.csv",
mime="text/csv",
use_container_width=True,
)
if summarizer_option == "t5-base":
if summary_yes:
model, tokenizer = load_t5()
summary = []
for x in stqdm(range(len(text))):
if cancel_button2.button("Cancel", key=x):
del model, tokenizer
break
tokens_input = tokenizer.encode(
"summarize: " + text[x],
return_tensors="pt",
max_length=tokenizer.model_max_length,
truncation=True,
)
summary_ids = model.generate(
tokens_input,
min_length=80,
max_length=150,
length_penalty=20,
num_beams=2,
)
summary_gen = tokenizer.decode(
summary_ids[0], skip_special_tokens=True
)
summary.append(summary_gen)
del model, tokenizer
outputdf["summary"] = summary
if classification:
themePipe = classify_theme()
classes = []
classesUnlabel = []
classesUnlabelZero = []
for x in stqdm(
text, desc="Assigning Themes ...", total=len(text), colour="red"
):
output = themePipe(x)[0]["label"]
classes.append(output)
score = round(themePipe(x)[0]["score"], 2)
if score <= treshold:
onelineoutput = oneline.predict(x)[0]
print("hit")
classesUnlabel.append(
assign_labels_flant5(
bot,
what="theme",
to=onelineoutput,
old=themes,
)
)
classesUnlabelZero.append(
assign_label_zeroshot(
zero=zeroline, to=onelineoutput, old=themes
)
)
else:
classesUnlabel.append("")
classesUnlabelZero.append("")
outputdf["Review Theme"] = classes
outputdf["Review Theme-issue-new"] = classesUnlabel
outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
clean_memory(themePipe)
if sub_theme:
subThemePipe = classify_sub_theme()
classes = []
classesUnlabelZero = []
for x in stqdm(
text,
desc="Assigning Subthemes ...",
total=len(text),
colour="green",
):
output = subThemePipe(x)[0]["label"]
classes.append(output)
score = round(subThemePipe(x)[0]["score"], 2)
if score <= treshold:
onelineoutput = oneline.predict(x)[0]
print("hit")
classesUnlabel.append(
assign_labels_flant5(
bot,
what="subtheme",
to=onelineoutput,
old=subthemes,
)
)
classesUnlabelZero.append(
assign_label_zeroshot(
zero=zeroline,
to=onelineoutput,
old=subthemes,
)
)
else:
classesUnlabel.append("")
classesUnlabelZero.append("")
outputdf["Review SubTheme"] = classes
outputdf["Review SubTheme-issue-new"] = classesUnlabel
outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
clean_memory(subThemePipe)
csv = convert_df(outputdf)
st.download_button(
label="Download output as CSV",
data=csv,
file_name=f"{summarizer_option}_{date}_df.csv",
mime="text/csv",
use_container_width=True,
)
if summarizer_option == "t5-one-line-summary":
if summary_yes:
model = SimpleT5()
load_one_line_summarizer(model=model)
summary = []
for x in stqdm(iterable=range(len(text))):
if cancel_button3.button(label="Cancel", key=x):
del model
break
try:
summary.append(model.predict(source_text=text[x])[0])
except:
pass
outputdf["summary"] = summary
del model
if classification:
themePipe = classify_theme()
classes = []
classesUnlabel = []
classesUnlabelZero = []
for x in stqdm(
iterable=text,
desc="Assigning Themes ...",
total=len(text),
colour="red",
):
output = themePipe(x)[0]["label"]
classes.append(output)
score = round(number=themePipe(x)[0]["score"], ndigits=2)
if score <= treshold:
onelineoutput = oneline.predict(x)[0]
print("hit")
classesUnlabel.append(
assign_labels_flant5(
bot,
what="theme",
to=onelineoutput,
old=themes,
)
)
classesUnlabelZero.append(
assign_label_zeroshot(
zero=zeroline, to=onelineoutput, old=themes
)
)
else:
classesUnlabel.append("")
classesUnlabelZero.append("")
outputdf["Review Theme"] = classes
outputdf["Review Theme-issue-new"] = classesUnlabel
outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
if sub_theme:
subThemePipe = classify_sub_theme()
classes = []
classesUnlabelZero = []
for x in stqdm(
iterable=text,
desc="Assigning Subthemes ...",
total=len(text),
colour="green",
):
output = subThemePipe(x)[0]["label"]
classes.append(output)
score = round(subThemePipe(x)[0]["score"], 2)
if score <= treshold:
print("hit")
onelineoutput = oneline.predict(source_text=x)[0]
classesUnlabel.append(
assign_labels_flant5(
bot,
what="subtheme",
to=onelineoutput,
old=subthemes,
)
)
classesUnlabelZero.append(
assign_label_zeroshot(
zero=zeroline,
to=onelineoutput,
old=subthemes,
)
)
else:
classesUnlabel.append("")
classesUnlabelZero.append("")
outputdf["Review SubTheme"] = classes
outputdf["Review SubTheme-issue-new"] = classesUnlabel
outputdf["Review SubTheme-issue-zero"] = classesUnlabelZero
clean_memory(subThemePipe)
csv = convert_df(outputdf)
st.download_button(
label="Download output as CSV",
data=csv,
file_name=f"{summarizer_option}_{date}_df.csv",
mime="text/csv",
use_container_width=True,
)
except KeyError as e:
st.error(
body="Please Make sure that your data must have a column named text",
icon="🚨",
)
st.info(body="Text column must have amazon reviews", icon="ℹ️")
st.exception(e)
except BaseException as e:
logging.exception(msg="An exception was occurred")
|