Upload main.py

src/main.py

@@ -1,4 +1,5 @@
 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
 
@@ -7,6 +8,12 @@ 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'])
@@ -15,18 +22,17 @@ def result():
         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)
+        gloss_list_lower = [gloss.lower() for gloss in generated_gloss.split() if gloss.isalnum() ]
+        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():
-    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()
+    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__":