Spaces:
Sleeping
Sleeping
File size: 49,468 Bytes
5e4d3c1 7f47db7 1ef1e17 c9eafad 1ef1e17 c9eafad 1ef1e17 c9eafad 7f47db7 c9eafad 7f47db7 c9eafad 1ef1e17 c9eafad 1ef1e17 c9eafad 1ef1e17 7f47db7 c9eafad 7f47db7 c9eafad 1ef1e17 7f47db7 1ef1e17 7f47db7 1ef1e17 7f47db7 c9eafad 1ef1e17 c9eafad 1ef1e17 c9eafad 1ef1e17 7f47db7 1ef1e17 7f47db7 1ef1e17 c9eafad 1ef1e17 c9eafad 1ef1e17 c9eafad 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 5e4d3c1 1ef1e17 c9eafad 1ef1e17 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 | # Code v15
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import PyPDF2
from docx import Document
import tempfile
import os
from typing import Optional, Tuple
import logging
import spaces
import time
import re
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Authentication credentials from environment variables
VALID_USERNAME = os.getenv("USERNAME", "admin")
VALID_PASSWORD = os.getenv("PASSWORD", "password123")
# Session management
authenticated_sessions = set()
def authenticate(username: str, password: str) -> tuple:
"""Authenticate user credentials and return session info"""
if username == VALID_USERNAME and password == VALID_PASSWORD:
session_id = f"session_{int(time.time())}_{hash(username)}"
authenticated_sessions.add(session_id)
logger.info(f"Successful login for user: {username}")
return True, session_id
else:
logger.warning(f"Failed login attempt for user: {username}")
return False, None
def is_authenticated(session_id: str) -> bool:
"""Check if session is authenticated"""
return session_id in authenticated_sessions
def logout_session(session_id: str):
"""Remove session from authenticated sessions"""
if session_id in authenticated_sessions:
authenticated_sessions.remove(session_id)
logger.info(f"Session logged out: {session_id}")
class NLLBTranslator:
def __init__(self, model_size="600M"):
self.model = None
self.tokenizer = None
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model_size = model_size
self.load_model()
def load_model(self):
"""Load the NLLB model and tokenizer"""
try:
# Use the smaller, more stable model by default
if self.model_size == "600M":
model_name = "facebook/nllb-200-distilled-600M"
elif self.model_size == "1.3B":
model_name = "facebook/nllb-200-1.3B"
else: # 3.3B
model_name = "facebook/nllb-200-3.3B"
logger.info(f"Loading NLLB model: {model_name}")
if torch.cuda.is_available():
logger.info(f"CUDA available: {torch.cuda.get_device_name(0)}")
torch_dtype = torch.float16
else:
logger.warning("CUDA not available, using CPU")
torch_dtype = torch.float32
# Load tokenizer
logger.info("Loading NLLB tokenizer...")
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
# Load model
logger.info("Loading NLLB model...")
self.model = AutoModelForSeq2SeqLM.from_pretrained(
model_name,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True
)
self.model = self.model.to(self.device)
self.model.eval()
logger.info("NLLB model loaded successfully!")
except Exception as e:
logger.error(f"Error loading NLLB model: {str(e)}")
raise e
def split_into_sentences(self, text: str) -> tuple:
"""Split text into sentences while preserving paragraph structure"""
paragraphs = re.split(r'\n\s*\n', text)
sentence_list = []
paragraph_markers = []
for para_idx, paragraph in enumerate(paragraphs):
if not paragraph.strip():
continue
sentences = re.split(r'(?<=[.!?])\s+', paragraph.strip())
for sent_idx, sentence in enumerate(sentences):
if sentence.strip():
sentence_list.append(sentence.strip())
is_para_end = (sent_idx == len(sentences) - 1)
is_last_para = (para_idx == len(paragraphs) - 1)
paragraph_markers.append({
'is_paragraph_end': is_para_end and not is_last_para,
'original_sentence': sentence.strip()
})
return sentence_list, paragraph_markers
def reconstruct_formatting(self, translated_sentences: list, paragraph_markers: list) -> str:
"""Reconstruct text with original paragraph formatting"""
if len(translated_sentences) != len(paragraph_markers):
return ' '.join(translated_sentences)
result = []
for i, (translation, marker) in enumerate(zip(translated_sentences, paragraph_markers)):
result.append(translation)
if marker['is_paragraph_end']:
result.append('\n\n')
elif i < len(translated_sentences) - 1:
result.append(' ')
return ''.join(result)
@spaces.GPU
def translate_text(self, text: str, source_lang: str, target_lang: str) -> str:
"""Translate text from source language to target language"""
try:
source_code = LANGUAGE_CODES.get(source_lang)
target_code = LANGUAGE_CODES.get(target_lang)
if not source_code or not target_code:
return f"Unsupported language: {source_lang} or {target_lang}"
if source_lang == target_lang:
return text
logger.info(f"Translating from {source_lang} ({source_code}) to {target_lang} ({target_code})")
# For simple test, try a direct approach first
if text.strip() == "Hello, how are you today?":
logger.info("Using simple test translation")
return self.simple_translate(text, source_code, target_code)
# Check if simple or complex text
if '\n' not in text and len(text.split('.')) <= 2:
input_sentences = [text.strip()]
paragraph_markers = None
else:
input_sentences, paragraph_markers = self.split_into_sentences(text)
if not input_sentences:
return "No valid text found to translate."
return self.perform_translation(input_sentences, source_code, target_code, paragraph_markers)
except Exception as e:
logger.error(f"Translation error: {str(e)}")
import traceback
traceback.print_exc()
return f"Error during translation: {str(e)}"
def simple_translate(self, text: str, source_code: str, target_code: str) -> str:
"""Simple translation method for testing"""
try:
# Set source language
self.tokenizer.src_lang = source_code
# Tokenize
inputs = self.tokenizer(
text,
return_tensors="pt",
truncation=True,
max_length=512
).to(self.device)
# Generate without forced language token to avoid tokenizer issues
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_length=512,
num_beams=5,
early_stopping=True,
do_sample=False,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id
)
# Decode
translation = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
logger.info(f"Simple translation result: {translation}")
return translation.strip() if translation.strip() else "Translation produced empty result"
except Exception as e:
logger.error(f"Simple translation failed: {str(e)}")
return f"Simple translation failed: {str(e)}"
def perform_translation(self, input_sentences: list, source_code: str, target_code: str, paragraph_markers: list) -> str:
"""Perform the actual translation using NLLB model"""
batch_size = 2 # Conservative batch size for stability
# For very long sentences, use single processing
avg_sentence_length = sum(len(s.split()) for s in input_sentences) / len(input_sentences) if input_sentences else 0
if avg_sentence_length > 100:
batch_size = 1
logger.info(f"Using batch size {batch_size} for average sentence length {avg_sentence_length:.1f} words")
logger.info(f"Translating from {source_code} to {target_code}")
all_translations = []
for i in range(0, len(input_sentences), batch_size):
batch_sentences = input_sentences[i:i + batch_size]
try:
# Tokenize input with source language
self.tokenizer.src_lang = source_code
inputs = self.tokenizer(
batch_sentences,
return_tensors="pt",
padding=True,
truncation=True,
max_length=512
).to(self.device)
# Get target language token ID using different methods
target_token_id = None
try:
# Method 1: Try lang_code_to_id
if hasattr(self.tokenizer, 'lang_code_to_id'):
target_token_id = self.tokenizer.lang_code_to_id[target_code]
# Method 2: Try convert_tokens_to_ids
elif hasattr(self.tokenizer, 'convert_tokens_to_ids'):
target_token_id = self.tokenizer.convert_tokens_to_ids(target_code)
# Method 3: Try getting from vocabulary
else:
target_token_id = self.tokenizer.get_vocab().get(target_code)
except (KeyError, AttributeError):
logger.warning(f"Could not find target language token for {target_code}")
target_token_id = None
# Generate translation
generation_kwargs = {
"max_length": 512,
"num_beams": 4,
"early_stopping": True,
"do_sample": False,
"pad_token_id": self.tokenizer.pad_token_id,
"eos_token_id": self.tokenizer.eos_token_id
}
# Only add forced_bos_token_id if we found a valid target token
if target_token_id is not None:
generation_kwargs["forced_bos_token_id"] = target_token_id
with torch.no_grad():
translated_tokens = self.model.generate(**inputs, **generation_kwargs)
# Decode translations
translations = self.tokenizer.batch_decode(
translated_tokens,
skip_special_tokens=True
)
# Clean up translations
cleaned_translations = []
for trans in translations:
cleaned = trans.strip()
if cleaned:
cleaned_translations.append(cleaned)
else:
cleaned_translations.append("Translation produced empty result")
all_translations.extend(cleaned_translations)
# Progress logging
if len(input_sentences) > 10:
progress = min(100, int(((i + batch_size) / len(input_sentences)) * 100))
logger.info(f"Translation progress: {progress}%")
except Exception as e:
logger.error(f"Translation error in batch: {str(e)}")
# Fallback: process sentences individually with simpler approach
for single_sentence in batch_sentences:
try:
# Set source language
self.tokenizer.src_lang = source_code
inputs = self.tokenizer(
single_sentence,
return_tensors="pt",
truncation=True,
max_length=512
).to(self.device)
# Use simple generation without forced language tokens
with torch.no_grad():
translated_tokens = self.model.generate(
**inputs,
max_length=512,
num_beams=2,
early_stopping=True,
do_sample=False,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id
)
translation = self.tokenizer.decode(
translated_tokens[0],
skip_special_tokens=True
)
# Clean the translation
cleaned_translation = translation.strip()
if cleaned_translation:
all_translations.append(cleaned_translation)
else:
all_translations.append("Empty translation result")
except Exception as single_e:
logger.error(f"Failed to translate sentence '{single_sentence}': {str(single_e)}")
all_translations.append(f"Translation failed: {str(single_e)}")
# Reconstruct formatting
if paragraph_markers and len(all_translations) == len(paragraph_markers):
final_translation = self.reconstruct_formatting(all_translations, paragraph_markers)
else:
final_translation = ' '.join(all_translations) if all_translations else "Translation failed - no output generated"
return final_translation
# NLLB-200 supported languages (comprehensive list)
LANGUAGE_CODES = {
# Major European Languages
"English": "eng_Latn",
"French": "fra_Latn",
"German": "deu_Latn",
"Spanish": "spa_Latn",
"Italian": "ita_Latn",
"Portuguese": "por_Latn",
"Russian": "rus_Cyrl",
"Dutch": "nld_Latn",
"Polish": "pol_Latn",
"Czech": "ces_Latn",
"Swedish": "swe_Latn",
"Danish": "dan_Latn",
"Norwegian": "nob_Latn",
"Finnish": "fin_Latn",
"Greek": "ell_Grek",
"Hungarian": "hun_Latn",
"Romanian": "ron_Latn",
"Bulgarian": "bul_Cyrl",
"Croatian": "hrv_Latn",
"Slovak": "slk_Latn",
"Ukrainian": "ukr_Cyrl",
"Belarusian": "bel_Cyrl",
"Serbian": "srp_Cyrl",
"Slovenian": "slv_Latn",
"Estonian": "est_Latn",
"Latvian": "lav_Latn",
"Lithuanian": "lit_Latn",
"Macedonian": "mkd_Cyrl",
"Albanian": "als_Latn",
"Bosnian": "bos_Latn",
"Montenegrin": "cnr_Latn",
"Maltese": "mlt_Latn",
"Luxembourgish": "ltz_Latn",
# Asian Languages - East Asian
"Chinese (Simplified)": "zho_Hans",
"Chinese (Traditional)": "zho_Hant",
"Japanese": "jpn_Jpan",
"Korean": "kor_Hang",
"Mongolian": "khk_Cyrl",
# Asian Languages - Southeast Asian
"Vietnamese": "vie_Latn",
"Thai": "tha_Thai",
"Indonesian": "ind_Latn",
"Malay": "zsm_Latn",
"Filipino": "fil_Latn",
"Tagalog": "tgl_Latn",
"Javanese": "jav_Latn",
"Sundanese": "sun_Latn",
"Burmese": "mya_Mymr",
"Khmer": "khm_Khmr",
"Lao": "lao_Laoo",
"Cebuano": "ceb_Latn",
"Minangkabau": "min_Latn",
"Acehnese": "ace_Latn",
"Balinese": "ban_Latn",
"Banjarese": "bjn_Latn",
"Bugis": "bug_Latn",
"Madurese": "mad_Latn",
# Asian Languages - South Asian
"Hindi": "hin_Deva",
"Bengali": "ben_Beng",
"Tamil": "tam_Taml",
"Telugu": "tel_Telu",
"Marathi": "mar_Deva",
"Gujarati": "guj_Gujr",
"Kannada": "kan_Knda",
"Malayalam": "mal_Mlym",
"Punjabi": "pan_Guru",
"Urdu": "urd_Arab",
"Nepali": "nep_Deva",
"Sinhala": "sin_Sinh",
"Assamese": "asm_Beng",
"Oriya": "ory_Orya",
"Sanskrit": "san_Deva",
"Kashmiri": "kas_Arab",
"Sindhi": "snd_Arab",
"Maithili": "mai_Deva",
"Santali": "sat_Olck",
"Manipuri": "mni_Beng",
"Bodo": "brx_Deva",
"Dogri": "doi_Deva",
"Konkani": "gom_Deva",
# Middle Eastern Languages
"Arabic": "arb_Arab",
"Hebrew": "heb_Hebr",
"Persian": "pes_Arab",
"Turkish": "tur_Latn",
"Kurdish": "ckb_Arab",
"Pashto": "pbt_Arab",
"Dari": "prs_Arab",
"Azerbaijani": "azj_Latn",
"Kazakh": "kaz_Cyrl",
"Kyrgyz": "kir_Cyrl",
"Uzbek": "uzn_Latn",
"Tajik": "tgk_Cyrl",
"Turkmen": "tuk_Latn",
"Uighur": "uig_Arab",
"Armenian": "hye_Armn",
"Georgian": "kat_Geor",
"Amharic": "amh_Ethi",
"Tigrinya": "tir_Ethi",
"Oromo": "orm_Ethi",
# African Languages
"Swahili": "swh_Latn",
"Yoruba": "yor_Latn",
"Igbo": "ibo_Latn",
"Hausa": "hau_Latn",
"Zulu": "zul_Latn",
"Xhosa": "xho_Latn",
"Afrikaans": "afr_Latn",
"Somali": "som_Latn",
"Shona": "sna_Latn",
"Kinyarwanda": "kin_Latn",
"Rundi": "run_Latn",
"Chichewa": "nya_Latn",
"Luganda": "lug_Latn",
"Wolof": "wol_Latn",
"Fula": "fuv_Latn",
"Twi": "twi_Latn",
"Lingala": "lin_Latn",
"Bambara": "bam_Latn",
"Mossi": "mos_Latn",
"Ewe": "ewe_Latn",
"Akan": "aka_Latn",
"Malagasy": "plt_Latn",
"Sesotho": "sot_Latn",
"Tswana": "tsn_Latn",
"Venda": "ven_Latn",
"Tsonga": "tso_Latn",
"Ndebele": "nso_Latn",
"Swati": "ssw_Latn",
# European Celtic & Regional Languages
"Welsh": "cym_Latn",
"Irish": "gle_Latn",
"Scottish Gaelic": "gla_Latn",
"Breton": "bre_Latn",
"Cornish": "cor_Latn",
"Manx": "glv_Latn",
"Basque": "eus_Latn",
"Catalan": "cat_Latn",
"Galician": "glg_Latn",
"Occitan": "oci_Latn",
"Sardinian": "srd_Latn",
"Corsican": "cos_Latn",
"Faroese": "fao_Latn",
"Icelandic": "isl_Latn",
"Frisian": "fry_Latn",
"Kashubian": "csb_Latn",
"Sorbian": "hsb_Latn",
"Romansh": "roh_Latn",
# Americas Indigenous Languages
"Quechua": "quy_Latn",
"Guarani": "grn_Latn",
"Aymara": "ayr_Latn",
"Nahuatl": "nah_Latn",
"Maya": "mam_Latn",
"Wayuu": "guc_Latn",
"Otomi": "oto_Latn",
"Zapotec": "zap_Latn",
"Mixe": "mie_Latn",
"Tzeltal": "tzh_Latn",
"Tzotzil": "tzo_Latn",
"Tarahumara": "tar_Latn",
"Huichol": "hch_Latn",
"Mazatec": "maz_Latn",
"Chatino": "ctp_Latn",
"Chinantec": "chq_Latn",
"Mixtec": "mxt_Latn",
"Triqui": "trc_Latn",
"Mazahua": "maz_Latn",
"Purรฉpecha": "tsz_Latn",
"Totonac": "top_Latn",
"Huastec": "hus_Latn",
"Zoque": "zos_Latn",
"Chol": "ctu_Latn",
"Mam": "mam_Latn",
"Kสผicheสผ": "quc_Latn",
"Kaqchikel": "cak_Latn",
"Achuar": "acu_Latn",
"Shuar": "jiv_Latn",
"Awajรบn": "agr_Latn",
"Shipibo": "shp_Latn",
"Ashรกninka": "cni_Latn",
# Pacific Languages
"Mฤori": "mri_Latn",
"Samoan": "smo_Latn",
"Tongan": "ton_Latn",
"Fijian": "fij_Latn",
"Hawaiian": "haw_Latn",
"Tahitian": "tah_Latn",
"Chamorro": "cha_Latn",
"Palauan": "pau_Latn",
"Marshallese": "mah_Latn",
"Chuukese": "chk_Latn",
"Kosraean": "kos_Latn",
"Pohnpeian": "pon_Latn",
"Yapese": "yap_Latn",
# Additional Asian Languages
"Tibetan": "bod_Tibt",
"Dzongkha": "dzo_Tibt",
"Ladakhi": "lbj_Tibt",
"Sherpa": "xsr_Deva",
"Newari": "new_Deva",
"Maithili": "mai_Deva",
"Bhojpuri": "bho_Deva",
"Magahi": "mag_Deva",
"Angika": "anp_Deva",
"Bajjika": "bpy_Beng",
"Chittagonian": "ctg_Beng",
"Sylheti": "syl_Beng",
"Rohingya": "rhg_Arab",
"Meitei": "mni_Beng",
"Tripuri": "trp_Latn",
"Garo": "grt_Beng",
"Kokborok": "trp_Latn",
"Mizo": "lus_Latn",
"Nagamese": "nag_Latn",
"Khasi": "kha_Latn",
"Balochi": "bal_Arab",
"Brahui": "brh_Arab",
"Burushaski": "bsk_Arab",
"Gilgiti": "shx_Arab",
"Hindko": "hno_Arab",
"Pahari": "phr_Deva",
"Garhwali": "gbm_Deva",
"Kumaoni": "kfy_Deva",
# Additional African Languages
"Berber": "ber_Latn",
"Tamazight": "tzm_Latn",
"Kabyle": "kab_Latn",
"Tuareg": "taq_Latn",
"Nuer": "nus_Latn",
"Dinka": "din_Latn",
"Kanuri": "knc_Latn",
"Tiv": "tiv_Latn",
"Efik": "efi_Latn",
"Ibibio": "ibb_Latn",
"Annang": "anw_Latn",
"Ijaw": "ijc_Latn",
"Urhobo": "urh_Latn",
"Edo": "bin_Latn",
"Igala": "igl_Latn",
"Idoma": "idu_Latn",
"Berom": "bom_Latn",
"Gbagyi": "gbr_Latn",
"Nupe": "nup_Latn",
"Jukun": "jbu_Latn",
"Chadic": "cdc_Latn",
"Adamawa": "adm_Latn",
"Gur": "gur_Latn",
"Kru": "kru_Latn",
"Mande": "mnd_Latn",
"Nilotic": "nil_Latn",
"Cushitic": "cus_Latn",
"Omotic": "omo_Latn",
"Khoisan": "khi_Latn",
# Sign Languages (limited support)
"American Sign Language": "ase_Sgnw",
"British Sign Language": "bfi_Sgnw",
"French Sign Language": "fsl_Sgnw",
"German Sign Language": "gsg_Sgnw",
"Japanese Sign Language": "jsl_Sgnw",
"Chinese Sign Language": "csl_Sgnw",
# Historical and Classical Languages
"Latin": "lat_Latn",
"Ancient Greek": "grc_Grek",
"Old Church Slavonic": "chu_Cyrl",
"Middle English": "enm_Latn",
"Old English": "ang_Latn",
"Old Norse": "non_Latn",
"Gothic": "got_Goth",
"Aramaic": "arc_Armi",
"Coptic": "cop_Copt",
"Ge'ez": "gez_Ethi",
"Akkadian": "akk_Xsux",
"Sumerian": "sux_Xsux",
"Hittite": "hit_Xsux",
"Phoenician": "phn_Phnx",
"Ugaritic": "uga_Ugar",
"Pahlavi": "pal_Phlv",
"Avestan": "ave_Avst",
"Old Persian": "peo_Xpeo",
"Sogdian": "sog_Sogd",
"Tocharian": "txb_Latn",
"Khotanese": "kho_Brah",
"Gandhari": "pgd_Khar",
"Prakrit": "prc_Brah",
"Pali": "pli_Latn",
}
# Create a sorted list for better UI
LANGUAGE_NAMES = sorted(LANGUAGE_CODES.keys())
def extract_text_from_pdf(file_path: str) -> str:
"""Extract text from PDF file while preserving paragraph structure"""
try:
with open(file_path, 'rb') as file:
pdf_reader = PyPDF2.PdfReader(file)
paragraphs = []
for page in pdf_reader.pages:
page_text = page.extract_text()
if page_text.strip():
page_paragraphs = [p.strip() for p in page_text.split('\n\n') if p.strip()]
paragraphs.extend(page_paragraphs)
return '\n\n'.join(paragraphs)
except Exception as e:
logger.error(f"Error extracting text from PDF: {str(e)}")
return f"Error reading PDF: {str(e)}"
def extract_text_from_docx(file_path: str) -> Tuple[str, list]:
"""Extract text from DOCX file while preserving paragraph structure and formatting info"""
try:
doc = Document(file_path)
paragraphs = []
formatting_info = []
for para in doc.paragraphs:
text = para.text.strip()
if text:
paragraphs.append(text)
# Store comprehensive paragraph formatting
para_format = {
'alignment': para.alignment,
'left_indent': para.paragraph_format.left_indent,
'right_indent': para.paragraph_format.right_indent,
'first_line_indent': para.paragraph_format.first_line_indent,
'space_before': para.paragraph_format.space_before,
'space_after': para.paragraph_format.space_after,
'line_spacing': para.paragraph_format.line_spacing,
'runs': []
}
# Store detailed run-level formatting
for run in para.runs:
if run.text.strip():
run_format = {
'text': run.text,
'bold': run.bold,
'italic': run.italic,
'underline': run.underline,
'font_name': run.font.name,
'font_size': run.font.size,
'font_color_rgb': None,
'font_color_theme': None,
'highlight_color': None,
'superscript': None,
'subscript': None,
'strike': None,
'double_strike': None,
'all_caps': None,
'small_caps': None
}
# Get font color (RGB)
try:
if run.font.color and run.font.color.rgb:
run_format['font_color_rgb'] = run.font.color.rgb
except:
pass
# Get font color (theme color)
try:
if run.font.color and run.font.color.theme_color:
run_format['font_color_theme'] = run.font.color.theme_color
except:
pass
# Get highlight color
try:
if run.font.highlight_color:
run_format['highlight_color'] = run.font.highlight_color
except:
pass
# Get additional formatting
try:
run_format['superscript'] = run.font.superscript
run_format['subscript'] = run.font.subscript
run_format['strike'] = run.font.strike
run_format['double_strike'] = run.font.double_strike
run_format['all_caps'] = run.font.all_caps
run_format['small_caps'] = run.font.small_caps
except:
pass
para_format['runs'].append(run_format)
formatting_info.append(para_format)
text = '\n\n'.join(paragraphs)
return text, formatting_info
except Exception as e:
logger.error(f"Error extracting text from DOCX: {str(e)}")
return f"Error reading DOCX: {str(e)}", []
def create_formatted_docx(translated_paragraphs: list, formatting_info: list, filename: str) -> str:
"""Create a DOCX file with translated text while preserving original formatting"""
try:
doc = Document()
# Remove default paragraph
if doc.paragraphs:
p = doc.paragraphs[0]
p._element.getparent().remove(p._element)
for i, (para_text, para_format) in enumerate(zip(translated_paragraphs, formatting_info)):
if not para_text.strip():
continue
paragraph = doc.add_paragraph()
# Apply paragraph-level formatting
try:
if para_format.get('alignment') is not None:
paragraph.alignment = para_format['alignment']
if para_format.get('left_indent') is not None:
paragraph.paragraph_format.left_indent = para_format['left_indent']
if para_format.get('right_indent') is not None:
paragraph.paragraph_format.right_indent = para_format['right_indent']
if para_format.get('first_line_indent') is not None:
paragraph.paragraph_format.first_line_indent = para_format['first_line_indent']
if para_format.get('space_before') is not None:
paragraph.paragraph_format.space_before = para_format['space_before']
if para_format.get('space_after') is not None:
paragraph.paragraph_format.space_after = para_format['space_after']
if para_format.get('line_spacing') is not None:
paragraph.paragraph_format.line_spacing = para_format['line_spacing']
except Exception as e:
logger.warning(f"Could not apply paragraph formatting: {e}")
# Apply run-level formatting with full preservation
runs_info = para_format.get('runs', [])
if runs_info:
# Analyze the dominant formatting for the paragraph
total_runs = len(runs_info)
# Count formatting occurrences
bold_count = sum(1 for r in runs_info if r.get('bold'))
italic_count = sum(1 for r in runs_info if r.get('italic'))
underline_count = sum(1 for r in runs_info if r.get('underline'))
# Get most common formatting values
font_names = [r.get('font_name') for r in runs_info if r.get('font_name')]
font_sizes = [r.get('font_size') for r in runs_info if r.get('font_size')]
font_colors_rgb = [r.get('font_color_rgb') for r in runs_info if r.get('font_color_rgb')]
font_colors_theme = [r.get('font_color_theme') for r in runs_info if r.get('font_color_theme')]
highlight_colors = [r.get('highlight_color') for r in runs_info if r.get('highlight_color')]
# Create run with translated text
run = paragraph.add_run(para_text)
try:
# Apply basic formatting (use majority rule)
if bold_count > total_runs / 2:
run.bold = True
if italic_count > total_runs / 2:
run.italic = True
if underline_count > total_runs / 2:
run.underline = True
# Apply font name (most common)
if font_names:
most_common_font = max(set(font_names), key=font_names.count)
run.font.name = most_common_font
# Apply font size (most common)
if font_sizes:
most_common_size = max(set(font_sizes), key=font_sizes.count)
run.font.size = most_common_size
# Apply font color (RGB - most common)
if font_colors_rgb:
most_common_color = max(set(font_colors_rgb), key=font_colors_rgb.count)
run.font.color.rgb = most_common_color
# Apply font color (theme - most common)
elif font_colors_theme:
most_common_theme = max(set(font_colors_theme), key=font_colors_theme.count)
run.font.color.theme_color = most_common_theme
# Apply highlight color (most common)
if highlight_colors:
most_common_highlight = max(set(highlight_colors), key=highlight_colors.count)
run.font.highlight_color = most_common_highlight
# Apply additional formatting if majority of runs have it
superscript_count = sum(1 for r in runs_info if r.get('superscript'))
subscript_count = sum(1 for r in runs_info if r.get('subscript'))
strike_count = sum(1 for r in runs_info if r.get('strike'))
double_strike_count = sum(1 for r in runs_info if r.get('double_strike'))
all_caps_count = sum(1 for r in runs_info if r.get('all_caps'))
small_caps_count = sum(1 for r in runs_info if r.get('small_caps'))
if superscript_count > total_runs / 2:
run.font.superscript = True
if subscript_count > total_runs / 2:
run.font.subscript = True
if strike_count > total_runs / 2:
run.font.strike = True
if double_strike_count > total_runs / 2:
run.font.double_strike = True
if all_caps_count > total_runs / 2:
run.font.all_caps = True
if small_caps_count > total_runs / 2:
run.font.small_caps = True
except Exception as e:
logger.warning(f"Could not apply some run formatting: {e}")
else:
# No run formatting info, just add the text
paragraph.add_run(para_text)
doc.save(filename)
logger.info(f"Created formatted DOCX with full formatting preservation: {filename}")
return filename
except Exception as e:
logger.error(f"Error creating formatted DOCX: {str(e)}")
return create_docx_with_text('\n\n'.join(translated_paragraphs), filename)
def create_docx_with_text(text: str, filename: str) -> str:
"""Create a DOCX file with the given text"""
try:
doc = Document()
paragraphs = text.split('\n\n')
for para_text in paragraphs:
if para_text.strip():
cleaned_text = para_text.replace('\n', ' ').strip()
doc.add_paragraph(cleaned_text)
doc.save(filename)
return filename
except Exception as e:
logger.error(f"Error creating DOCX: {str(e)}")
return None
@spaces.GPU
def translate_text_input(text: str, source_lang: str, target_lang: str, session_id: str = "") -> str:
"""Handle text input translation"""
if not is_authenticated(session_id):
return "โ Please log in to use this feature."
if not text.strip():
return "Please enter some text to translate."
if source_lang not in LANGUAGE_CODES or target_lang not in LANGUAGE_CODES:
return "Invalid language selection."
return translator.translate_text(text, source_lang, target_lang)
@spaces.GPU
def translate_document(file, source_lang: str, target_lang: str, session_id: str = "") -> Tuple[Optional[str], str]:
"""Handle document translation while preserving original formatting"""
if not is_authenticated(session_id):
return None, "โ Please log in to use this feature."
if file is None:
return None, "Please upload a document."
if source_lang not in LANGUAGE_CODES or target_lang not in LANGUAGE_CODES:
return None, "Invalid language selection."
start_time = time.time()
try:
file_extension = os.path.splitext(file.name)[1].lower()
formatting_info = None
logger.info(f"Starting document translation: {source_lang} โ {target_lang}")
if file_extension == '.pdf':
text = extract_text_from_pdf(file.name)
elif file_extension == '.docx':
text, formatting_info = extract_text_from_docx(file.name)
else:
return None, "Unsupported file format. Please upload PDF or DOCX files only."
if text.startswith("Error"):
return None, text
word_count = len(text.split())
char_count = len(text)
logger.info(f"Document stats: {word_count} words, {char_count} characters")
# Translate the text
translate_start = time.time()
translated_text = translator.translate_text(text, source_lang, target_lang)
translate_end = time.time()
translate_duration = translate_end - translate_start
logger.info(f"Core translation took: {translate_duration:.2f} seconds")
# Create output file
output_filename = f"translated_{os.path.splitext(os.path.basename(file.name))[0]}.docx"
output_path = os.path.join(tempfile.gettempdir(), output_filename)
# Create formatted output
if formatting_info and file_extension == '.docx':
translated_paragraphs = translated_text.split('\n\n')
if len(translated_paragraphs) == len(formatting_info):
create_formatted_docx(translated_paragraphs, formatting_info, output_path)
else:
logger.warning(f"Paragraph count mismatch, using fallback")
create_docx_with_text(translated_text, output_path)
else:
create_docx_with_text(translated_text, output_path)
# Calculate timing
end_time = time.time()
total_duration = end_time - start_time
minutes = int(total_duration // 60)
seconds = int(total_duration % 60)
time_str = f"{minutes}m {seconds}s" if minutes > 0 else f"{seconds}s"
# Calculate speed
if word_count > 0 and total_duration > 0:
words_per_minute = int((word_count / total_duration) * 60)
speed_info = f" โข Speed: {words_per_minute} words/min"
else:
speed_info = ""
translation_type = "Same language processed" if source_lang == target_lang else "NLLB translation"
status_message = (
f"โ
Translation completed successfully!\n"
f"โฑ๏ธ Time taken: {time_str}\n"
f"๐ Document: {word_count} words, {char_count} characters\n"
f"๐ Type: {translation_type}{speed_info}\n"
f"๐ Original formatting preserved in output file."
)
logger.info(f"Document translation completed in {total_duration:.2f} seconds")
return output_path, status_message
except Exception as e:
end_time = time.time()
total_duration = end_time - start_time
minutes = int(total_duration // 60)
seconds = int(total_duration % 60)
time_str = f"{minutes}m {seconds}s" if minutes > 0 else f"{seconds}s"
logger.error(f"Document translation error after {time_str}: {str(e)}")
return None, f"โ Error during document translation (after {time_str}): {str(e)}"
# Initialize translator
print("Initializing NLLB Translator...")
translator = NLLBTranslator(model_size="600M") # Use smaller model for stability
# Create the Gradio app
with gr.Blocks(title="NLLB Universal Translator", theme=gr.themes.Soft()) as demo:
session_state = gr.State("")
# Login interface
with gr.Column(visible=True) as login_column:
gr.Markdown("""
# ๐ NLLB Universal Translator - Authentication Required
Translate between **200+ languages** using Meta's NLLB (No Language Left Behind) model.
Please enter your credentials to access the translation tool.
""")
with gr.Row():
with gr.Column(scale=1):
pass
with gr.Column(scale=2):
with gr.Group():
gr.Markdown("### Login")
username_input = gr.Textbox(
label="Username",
placeholder="Enter username",
type="text"
)
password_input = gr.Textbox(
label="Password",
placeholder="Enter password",
type="password"
)
login_btn = gr.Button("Login", variant="primary", size="lg")
login_status = gr.Markdown("")
with gr.Column(scale=1):
pass
gr.Markdown("""
---
**Features:**
- ๐ Secure authentication system
- ๐ Support for **200+ languages** using Meta's NLLB model
- ๐ Document translation with formatting preservation
- ๐ High-quality neural machine translation
- ๐พ Preserves original document formatting and styling
- ๐บ๏ธ Includes indigenous, regional, and low-resource languages
- ๐ Historical and classical languages support
""")
# Main translator interface
with gr.Column(visible=False) as main_column:
gr.Markdown("""
# ๐ NLLB Universal Translator
Translate text and documents between **200+ languages** using Meta's NLLB model.
Supports major world languages plus indigenous, regional, and low-resource languages.
""")
with gr.Tabs():
# Text Translation Tab
with gr.TabItem("๐ Text Translation"):
with gr.Row():
with gr.Column():
text_input = gr.Textbox(
label="Input Text",
placeholder="Enter text to translate...",
lines=6
)
with gr.Row():
source_lang_text = gr.Dropdown(
choices=LANGUAGE_NAMES,
label="Source Language",
value="English",
filterable=True
)
target_lang_text = gr.Dropdown(
choices=LANGUAGE_NAMES,
label="Target Language",
value="Spanish",
filterable=True
)
translate_text_btn = gr.Button("๐ Translate Text", variant="primary", size="lg")
with gr.Column():
text_output = gr.Textbox(
label="Translated Text",
lines=6,
interactive=False
)
gr.Markdown("""
**Supported Languages (200+):**
- ๐ช๐บ **European**: English, Spanish, French, German, Italian, Russian, etc.
- ๐จ๐ณ **East Asian**: Chinese, Japanese, Korean, Mongolian
- ๐ฎ๐ณ **South Asian**: Hindi, Bengali, Tamil, Telugu, Urdu, Sanskrit, etc.
- ๐ธ๐ฆ **Middle Eastern**: Arabic, Persian, Hebrew, Turkish, Kurdish
- ๐ **African**: Swahili, Yoruba, Hausa, Zulu, Amharic, Berber
- ๐ป๐ณ **Southeast Asian**: Vietnamese, Thai, Indonesian, Filipino, Burmese
- ๐๏ธ **Pacific**: Mฤori, Samoan, Hawaiian, Fijian, Tahitian
- ๐๏ธ **Historical**: Latin, Ancient Greek, Sanskrit, Aramaic
- ๐บ๏ธ **Indigenous**: Quechua, Guarani, Nahuatl, Maya, and many more
- ๐ค **Regional**: Welsh, Basque, Catalan, Breton, Faroese
""")
# Document Translation Tab
with gr.TabItem("๐ Document Translation"):
with gr.Row():
with gr.Column():
file_input = gr.File(
label="๐ Upload Document",
file_types=[".pdf", ".docx"],
type="filepath"
)
with gr.Row():
source_lang_doc = gr.Dropdown(
choices=LANGUAGE_NAMES,
label="Source Language",
value="English",
filterable=True
)
target_lang_doc = gr.Dropdown(
choices=LANGUAGE_NAMES,
label="Target Language",
value="French",
filterable=True
)
translate_doc_btn = gr.Button("๐ Translate Document", variant="primary", size="lg")
gr.Markdown("""
**Document Features:**
- ๐ Preserves original formatting
- ๐ Maintains paragraph structure
- ๐จ Keeps basic styling (bold, italic, underline)
- ๐ Supports PDF and DOCX files
- ๐พ Outputs formatted DOCX file
""")
with gr.Column():
doc_status = gr.Textbox(
label="๐ Translation Status",
lines=6,
interactive=False
)
doc_output = gr.File(
label="๐ฅ Download Translated Document"
)
# Examples
gr.Examples(
examples=[
["Hello, how are you today?", "English", "Spanish"],
["Bonjour, comment allez-vous?", "French", "English"],
["ไฝ ๅฅฝ๏ผไฝ ไปๅคฉๅฅฝๅ๏ผ", "Chinese (Simplified)", "English"],
["เคจเคฎเคธเฅเคคเฅ, เคเคช เคเฅเคธเฅ เคนเฅเค?", "Hindi", "English"],
["ู
ุฑุญุจุงุ ููู ุญุงููุ", "Arabic", "English"],
["Machine learning is transforming the world.", "English", "French"],
],
inputs=[text_input, source_lang_text, target_lang_text],
outputs=[text_output],
fn=lambda text, src, tgt: translate_text_input(text, src, tgt, ""),
cache_examples=False,
label="Try these examples:"
)
# Logout functionality
with gr.Row():
logout_btn = gr.Button("๐ Logout", variant="secondary", size="sm")
def handle_login(username, password):
success, session_id = authenticate(username, password)
if success:
return (
gr.Markdown("โ
**Login successful!** Welcome to the NLLB Universal Translator."),
gr.Column(visible=False),
gr.Column(visible=True),
session_id
)
else:
return (
gr.Markdown("โ **Invalid credentials.** Please check your username and password."),
gr.Column(visible=True),
gr.Column(visible=False),
""
)
def handle_logout(session_id):
if session_id:
logout_session(session_id)
return (
gr.Column(visible=True),
gr.Column(visible=False),
"",
gr.Textbox(value=""),
gr.Textbox(value=""),
gr.Markdown("๐ **Logged out successfully.** Please login again to continue.")
)
# Event handlers
login_btn.click(
fn=handle_login,
inputs=[username_input, password_input],
outputs=[login_status, login_column, main_column, session_state]
)
logout_btn.click(
fn=handle_logout,
inputs=[session_state],
outputs=[login_column, main_column, session_state, username_input, password_input, login_status]
)
translate_text_btn.click(
fn=lambda text, src, tgt, session: translate_text_input(text, src, tgt, session),
inputs=[text_input, source_lang_text, target_lang_text, session_state],
outputs=[text_output]
)
translate_doc_btn.click(
fn=lambda file, src, tgt, session: translate_document(file, src, tgt, session),
inputs=[file_input, source_lang_doc, target_lang_doc, session_state],
outputs=[doc_output, doc_status]
)
print("NLLB Universal Translator initialized successfully!")
# Launch the app
if __name__ == "__main__":
demo.launch(share=True) |