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Building
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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
app = Flask(__name__)
@app.route('/')
def index():
global dataset, vocabulary_list, dict_2000_tokens, nlp, dict_docs_spacy
dataset, vocabulary_list = dg.load_data()
dict_2000_tokens = dataset["gloss"].unique()
nlp, dict_docs_spacy = sp.load_spacy_values()
return render_template('index.html')
@app.route('/translate/', methods=['POST'])
def result():
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_list = gloss_list_lower
#gloss_list = sentence.split()
#print('gloss before synonym:', gloss_list_lower)
gloss_list = [sp.find_synonyms(gloss, nlp, dict_docs_spacy, dict_2000_tokens) for gloss in gloss_list_lower]
#print('synonym list:', gloss_list)
gloss_sentence = " ".join(gloss_list)
return render_template('translate.html', sentence=sentence, gloss_list=gloss_list, gloss_sentence=gloss_sentence)
@app.route('/video_feed')
def video_feed():
#dataset, vocabulary_list = dg.load_data()
sentence = request.args.get('gloss_sentence', '')
gloss_list = sentence.split()
return Response(dg.generate_video(gloss_list, dataset, vocabulary_list), 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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