app.py

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

model_name = "IProject-10/roberta-base-finetuned-squad2"
nlp = pipeline("question-answering", model=model_name, tokenizer=model_name)

def predict(context, question):
    res = nlp({"question": question, "context": context})
    return res["answer"]

md = """In this project work we build a Text Retrieval Question-Answering system using BERT model. QA system is an important NLP task in which the user asks a question in natural language to the model as input and the model provides the answer in natural language as output.

The language representation model BERT stands for Bidirectional Encoder Representations from Transformers. The model is based on the Devlin et al. paper: [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805).

Dataset used is SQuAD 2.0 [Stanford Question Answering Dataset 2.0](https://rajpurkar.github.io/SQuAD-explorer/). It is a reading comprehension dataset which consists of question-answer pairs derived from Wikipedia articles written by crowdworkers. The answer to all the questions is in the form of a span of text.

"""

context = "The Amazon rainforest, also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America..."
question = "Which continent is the Amazon rainforest in?"

apple_context = "An apple is an edible fruit produced by an apple tree (Malus domestica)..."
apple_question = "How many years have apples been grown for?"

gr.Interface(
    predict,
    inputs=[
        gr.Textbox(lines=7, value=context, label="Context Paragraph"),
        gr.Textbox(lines=2, value=question, label="Question"),
    ],
    outputs=gr.Textbox(label="Answer"),
    examples=[[apple_context, apple_question]],
    title="Question & Answering with BERT using the SQuAD 2 dataset",
    description=md,
).launch()