Spaces:
Building
Building
import display_gloss as dg | |
import synonyms_preprocess as sp | |
from NLP_Spacy_base_translator import NlpSpacyBaseTranslator | |
from flask import Flask, render_template, Response, request, jsonify | |
import requests | |
nlp, dict_docs_spacy = sp.load_spacy_values() | |
dataset, list_2000_tokens = dg.load_data() | |
from googletrans import Translator | |
app = Flask(__name__, static_folder='static') | |
app.config['TITLE'] = 'Sign Language Translate' | |
translator = Translator() | |
def translate_korean_to_english(text): | |
try: | |
# 한글 감지 | |
if any('\u3131' <= char <= '\u318F' or '\uAC00' <= char <= '\uD7A3' for char in text): | |
translation = translator.translate(text, src='ko', dest='en') | |
return translation.text | |
return text | |
except Exception as e: | |
print(f"Translation error: {e}") | |
return text | |
def index(): | |
return render_template('index.html', title=app.config['TITLE']) | |
def result(): | |
if request.method == 'POST': | |
korean_sentence = request.form['inputSentence'] | |
try: | |
english_translation = translate_korean_to_english(korean_sentence) | |
eng_to_asl_translator = NlpSpacyBaseTranslator(sentence=english_translation) | |
generated_gloss = eng_to_asl_translator.translate_to_gloss() | |
gloss_list_lower = [gloss.lower() for gloss in generated_gloss.split() if gloss.isalnum()] | |
gloss_sentence_before_synonym = " ".join(gloss_list_lower) | |
gloss_list = [sp.find_synonyms(gloss, nlp, dict_docs_spacy, list_2000_tokens) | |
for gloss in gloss_list_lower] | |
gloss_sentence_after_synonym = " ".join(gloss_list) | |
return render_template('result.html', | |
title=app.config['TITLE'], | |
original_sentence=korean_sentence, | |
english_translation=english_translation, | |
gloss_sentence_before_synonym=gloss_sentence_before_synonym, | |
gloss_sentence_after_synonym=gloss_sentence_after_synonym) | |
except Exception as e: | |
return render_template('error.html', error=str(e)) | |
def video_feed(): | |
sentence = request.args.get('gloss_sentence_to_display', '') | |
gloss_list = sentence.split() | |
return Response(dg.generate_video(gloss_list, dataset, list_2000_tokens), | |
mimetype='multipart/x-mixed-replace; boundary=frame') | |
if __name__ == "__main__": | |
app.run(host="0.0.0.0", port=5000, debug=True) |