Spaces:
Building
Building
File size: 1,729 Bytes
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import display_gloss as dg
import numpy as np
import synonyms_preprocess as sp
from NLP_Spacy_base_translator import NlpSpacyBaseTranslator
from flask import Flask, render_template, Response, request
app = Flask(__name__)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/translate/', methods=['POST'])
def result():
nlp, dict_docs_spacy = sp.load_spacy_values()
_, list_2000_tokens = dg.load_data()
if request.method == 'POST':
sentence = request.form['inputSentence']
eng_to_asl_translator = NlpSpacyBaseTranslator(sentence=sentence)
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('translate.html',\
sentence=sentence,\
gloss_sentence_before_synonym=gloss_sentence_before_synonym,\
gloss_sentence_after_synonym=gloss_sentence_after_synonym)
@app.route('/video_feed')
def video_feed():
dataset, list_2000_tokens = dg.load_data()
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.debug = True
app.run(host="0.0.0.0", port=5000, debug=True)
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