Happy Hugging Face!

generator.py

@@ -50,10 +50,9 @@ def run_model(input_string, **generator_args):
   output = hftokenizer.batch_decode(res, skip_special_tokens=True)
   output = [item.split("<sep>") for item in output]
   return output
-
 al_tokenizer = att.from_pretrained("deepset/electra-base-squad2")
 al_model = amqa.from_pretrained("deepset/electra-base-squad2")
- 
+
 # al_model = pickle.load(open('models/al_model.sav', 'rb'))
 # al_tokenizer = pickle.load(open('models/al_tokenizer.sav', 'rb'))
 def QA(question, context):
@@ -62,11 +61,11 @@ def QA(question, context):
   format = {
       'question':question,
       'context':context
-  }
+      }
   res = nlp(format)
   output = f"{question}\n{string.capwords(res['answer'])}\tscore : [{res['score']}] \n"
   return output
-  # inputs = tokenizer(question, context, return_tensors="pt")
+# inputs = tokenizer(question, context, return_tensors="pt")
   # # Run the model, the deepset way
   # with torch.no_grad():
   #   output = model(**inputs)
@@ -85,7 +84,7 @@ def QA(question, context):
 
 def gen_question(inputs):
 
- questions = run_model(inputs)
+  questions = run_model(inputs)
 
  return questions
 
@@ -100,7 +99,7 @@ def read_file(filepath_name):
   return context
 
 def create_string_for_generator(context):
-    gen_list = gen_question(context)
+  gen_list = gen_question(context)
     return (gen_list[0][0]).split('? ')
 
 def creator(context):