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)