app.py

import gradio as gr
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "bert-large-uncased-whole-word-masking-finetuned-squad"

nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)

def chat(context, question):
  QA_input = {
      "question" : question,
      "context" : context
  }
  res = nlp(QA_input)

  return res['answer']

screen = gr.Interface(
    fn = chat,
    inputs = [gr.Textbox(lines = 8, placeholder = "Enter your context here 👉"), gr.Textbox(lines = 2, placeholder = "Enter your question here 👉")],
    outputs = gr.Textbox(lines = 15, placeholder = "Your answer will be here soon 🚀"),
    title="Facilitating the QnA with bert-large-uncased-whole-word-masking-finetuned-squad 👩🏻‍💻📓✍🏻💡",
    description="This app aims to facilitate the simple QnA with the provided context💡",
    theme="soft",
    article = """### Disclaimer : This model is purely used for QnA. User is expected to paste the text from which they want the answer in context section. <br> &emsp;&emsp;&emsp;&emsp;&emsp;&emsp; Then paste the question in the question section. <br> &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;     User will get the answer in the output section."""
)

screen.launch()