Update app.py

app.py

@@ -1,18 +1,9 @@
 import tensorflow as tf
-
 from transformers import pipeline
-
-# importing necessary libraries
-from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering
-
-tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
-model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad",return_dict=False)
-
-nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
+nlp = pipeline("question-answering")
 
 import gradio as gr
 
-# creating the function
 def func(context, question):
   result = nlp(question = question, context=context)
   return result['answer']
@@ -23,13 +14,11 @@ qst_1 =  "what is christian's profession?"
 example_2 = "(2) Natural Language Processing (NLP) allows machines to break down and interpret human language. It's at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools."
 qst_2 =  "What is NLP used for?"
 
-# creating the interface
 app = gr.Interface(fn=func, inputs = ['textbox', 'text'], outputs = 'textbox', 
                    title = 'Question Answering bot', theme = 'dark-grass',
                    description = 'Input context and question, then get answers!',
                    examples = [[example_1, qst_1],
                                [example_2, qst_2]]
                    )
-
-# launching the app
+                   
 app.launch(share=True, inline=False)