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 # ---- Initialise Flask App # app = Flask(__name__) # ---- Render the homepage template # @app.route('/') def index(): return render_template('index.html') # ---- Translate english input sentence into gloss sentence # @app.route('/translate/', methods=['POST']) def result(): # ---- Load NLP models and data # nlp, dict_docs_spacy = sp.load_spacy_values() _, list_2000_tokens = dg.load_data() if request.method == 'POST': # ---- Get the raw sentence and translate it to gloss # 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) # ---- Substitute gloss tokens with synonyms if not in the common token list # 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) # ---- Render the result template with both versions of the gloss sentence # return render_template('translate.html',\ sentence=sentence,\ gloss_sentence_before_synonym=gloss_sentence_before_synonym,\ gloss_sentence_after_synonym=gloss_sentence_after_synonym) # ---- Generate video streaming from gloss_sentence # @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)