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| import tensorflow as tf | |
| #!pip install transformers | |
| 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) | |
| #!pip install gradio | |
| import gradio as gr | |
| # 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 = '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) |