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 from googletrans import Translator app = Flask(__name__) # Initialize translators and data nlp, dict_docs_spacy = sp.load_spacy_values() dataset, list_2000_tokens = dg.load_data() ko_to_en_translator = Translator() @app.route('/') def index(): return render_template('index.html') @app.route('/translate/', methods=['POST']) def result(): if request.method == 'POST': # Get Korean input and translate to English korean_sentence = request.form['inputSentence'] english_translation = ko_to_en_translator.translate(korean_sentence, src='ko', dest='en').text # Translate English to ASL gloss 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) # Process synonyms 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', original_sentence=korean_sentence, english_translation=english_translation, gloss_sentence_before_synonym=gloss_sentence_before_synonym, gloss_sentence_after_synonym=gloss_sentence_after_synonym) @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.debug = True app.run(host="0.0.0.0", port=5000, debug=True)