app.py

import gradio as gr
from transformers import pipeline

# Load the models only once at the beginning of the script.
# This happens when the Gradio app is first launched, not with every button click.
bert_pipeline = pipeline(
    "question-answering", 
    model="bert-large-uncased-whole-word-masking-finetuned-squad"
)
roberta_pipeline = pipeline(
    "question-answering", 
    model="deepset/roberta-base-squad2"
)

# The function now simply uses the already-loaded pipeline objects.
def answer_question(c, q):
    bert_answer = bert_pipeline(question=q, context=c)['answer']
    roberta_answer = roberta_pipeline(question=q, context=c)['answer']
    return bert_answer, roberta_answer

# Interface using gradio
with gr.Blocks() as demo:
    gr.Markdown("Question Answering session with BERT and RoBERTa Model")
    context_input = gr.Textbox(label='Context', placeholder="Enter the context information here....")
    question_input = gr.Textbox(label='Question', placeholder="Enter the question here one by one....")
    bert_output = gr.Textbox(label='Bert Answer')
    robert_output = gr.Textbox(label='Roberta Answer')
    submit_button = gr.Button("Get Answers")
    
    # Click action
    submit_button.click(answer_question, inputs=[context_input, question_input], outputs=[bert_output, robert_output])

demo.launch()