import display_gloss as dg
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():
    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 = generated_gloss.split()
        print(gloss_list)
        return render_template('translate.html', sentence=sentence, gloss_list=gloss_list)

@app.route('/video_feed')
def video_feed():
    sentence = request.args.get('sentence', '')
    eng_to_asl_translator = NlpSpacyBaseTranslator(sentence=sentence)
    generated_gloss = eng_to_asl_translator.translate_to_gloss()
    gloss_list = [gloss.lower() for gloss in generated_gloss.split()]
    print(f'video_feed gloss_list: {gloss_list}')
    dataset, vocabulary_list = dg.load_data()
    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)