Update app.py

app.py

@@ -1,9 +1,22 @@
 import tensorflow as tf
+
+#!pip install transformers
+
 from transformers import pipeline
-nlp = pipeline("question-answering")
 
+# 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)
+
+#!pip install gradio
 import gradio as gr
 
+# creating the function
 def func(context, question):
   result = nlp(question = question, context=context)
   return result['answer']
@@ -14,11 +27,13 @@ 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(inline=False)