Updated fixed context

app.py

@@ -9,7 +9,8 @@ from transformers import DistilBertTokenizer, TFDistilBertForQuestionAnswering
 # Load model & tokenizer
 # model_name = "deepset/roberta-base-squad2"
 # model_name = "AmazonScience/qanlu"
-model_name = 'distilbert-base-cased-distilled-squad'
+# model_name = 'distilbert-base-cased-distilled-squad'
+model_name = "bert-large-uncased-whole-word-masking-finetuned-squad"
 # tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
@@ -45,8 +46,7 @@ nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
 fixed_context = """Ishaan is 6 year old kid. he is very good in football. He is very good sports person.
 he is smart kid. He can run very fast. as fast as 10 meters in 1 minute. 
 He goes to Vidyani ketan school. He goes to school from 8 am to 3:30 pm.
-Ishaan has many friends. 
-Vineet is his brother. """
+Ishaan has many friends. Vineet is Ishaan's brother. """
 
 # def get_answer(fixed_context,question):
 #     QA_input = {