Spaces:
Building
Building
File size: 2,528 Bytes
9303d62 |
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 |
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
app = Flask(__name__, static_folder='static')
app.config['TITLE'] = 'Sign Language Translate'
nlp, dict_docs_spacy = sp.load_spacy_values()
dataset, list_2000_tokens = dg.load_data()
def translate_korean_to_english(text):
url = "https://translate.googleapis.com/translate_a/single"
params = {
"client": "gtx",
"sl": "ko",
"tl": "en",
"dt": "t",
"q": text
}
response = requests.get(url, params=params)
return response.json()[0][0][0]
@app.route('/')
def index():
return render_template('index.html', title=app.config['TITLE'])
@app.route('/translate/', methods=['POST'])
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))
@app.route('/video_feed')
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) |