Create app.py

app.py

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+import tensorflow as tf
+import gradio as gr
+# 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)
+from transformers import pipeline
+
+nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+context = "My name is Ajulor Christian, i am a data scientist and machine learning engineer"
+question = "what is my profession?"
+
+result = nlp(question = question, context=context)
+
+print(f"QUESTION: {question}")
+print(f"ANSWER: {result['answer']}")
+
+# creating the function
+def func(context, question):
+  result = nlp(question = question, context=context)
+  return result['answer']
+
+example_1 = "(1) My name is Ajulor Christian, I am a data scientist and machine learning engineer"
+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 = gr.Textbox( lines=10), 
+                   title = 'Question Answering bot',
+                   description = 'Input context and question, then get answers!',
+                   examples = [[example_1, qst_1],
+                               [example_2, qst_2]],
+                    theme = "darkhuggingface",
+                   Timeout =120,
+                   flagging_options=["incorrect", "ambiguous", "offensive", "other"],
+              
+                   ).queue()
+# launching the app
+app.launch(auth = ('user','saitmhema'), auth_message = "Check your Login details sent to your email")