Update app.py

app.py

@@ -31,9 +31,29 @@ import string
 import pke
 import traceback
 import spacy
+warnings.filterwarnings("ignore")
 
+###############################################
+# Models #
+###############################################
 
-warnings.filterwarnings("ignore")
+summary_model = T5ForConditionalGeneration.from_pretrained('t5-base')
+summary_tokenizer = T5Tokenizer.from_pretrained('t5-base')
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+summary_model = summary_model.to(device)
+
+
+glove_file = 'glove.6B.300d.txt'
+tmp_file = 'word2vec-glove.6B.300d.txt'
+glove2word2vec(glove_file, tmp_file)
+model = KeyedVectors.load_word2vec_format(tmp_file)
+
+
+question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
+question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')
+question_model = question_model.to(device)
+
+###############################################
 def Process_audio(fileName):
   text=''
   txtf=open("The_audio.txt","w+")
@@ -72,14 +92,6 @@ def UrlToAudio(VideoUrl):
       Process_audio(str(wav_file_path))
       break
       
-
-summary_model = T5ForConditionalGeneration.from_pretrained('t5-base')
-summary_tokenizer = T5Tokenizer.from_pretrained('t5-base')
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-summary_model = summary_model.to(device)
-
-
 def set_seed(seed: int):
     random.seed(seed)
     np.random.seed(seed)
@@ -92,8 +104,6 @@ def postprocesstext (content):
     sent = sent.capitalize()
     final = final +" "+sent
   return final
-
-
 def summarizer(text,model,tokenizer):
   text = text.strip().replace("\n"," ")
   text = "summarize: "+text
@@ -173,9 +183,7 @@ def get_keywords(originaltext,summarytext):
 
   return important_keywords[:4]
  
-question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1')
-question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1')
-question_model = question_model.to(device)
+
 
 def get_question(context,answer,model,tokenizer):
   text = "context: {} answer: {}".format(context,answer)
@@ -222,11 +230,7 @@ def get_distractors_wordnet(word):
       print ("Wordnet distractors not found")
     return distractors
     
-glove_file = 'glove.6B.300d.txt'
-tmp_file = 'word2vec-glove.6B.300d.txt'
 
-glove2word2vec(glove_file, tmp_file)
-model = KeyedVectors.load_word2vec_format(tmp_file)
 def generate_distractors(answer, count):
     answer = str.lower(answer)
     
@@ -284,7 +288,6 @@ def generate_question(context1,radiobutton):
   #      return "Something Went Wrong...Please Check Link or try Again"
   
 
-
 iface = gr.Interface(
   fn=generate_question, 
   inputs=[context1,radiobutton],