test

app.py

@@ -9,9 +9,6 @@ from transformers import PreTrainedModel
 from transformers import PreTrainedTokenizer
 from transformers import AutoTokenizer
 
-tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
-model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
-
 
 # Question launcher
 class E2EQGPipeline:
@@ -79,9 +76,13 @@ class E2EQGPipeline:
         return inputs
 
 
+qg_model = T5ForConditionalGeneration.from_pretrained('valhalla/t5-base-e2e-qg')
+qg_tokenizer = T5Tokenizer.from_pretrained('valhalla/t5-base-e2e-qg')
+
+
 def generate_questions(text):
-    qg_model = T5ForConditionalGeneration.from_pretrained('valhalla/t5-base-e2e-qg')
-    qg_tokenizer = T5Tokenizer.from_pretrained('valhalla/t5-base-e2e-qg')
+    print('qg')
+    print(text)
     qg_final_model = E2EQGPipeline(qg_model, qg_tokenizer)
     questions = qg_final_model.generate_questions(text)
     translator = Translator()
@@ -89,6 +90,10 @@ def generate_questions(text):
     return translated_questions
 
 
+tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
+model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
+
+
 def generate_summary(text):
     inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
     summary_ids = model.generate(inputs, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)