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from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
import gradio as gr
import time
# Author information
author = "Ajeetkumar Ukande"
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased-distilled-squad")
model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-cased-distilled-squad")
qa_pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
def response(context, question):
result = qa_pipe(context=context, question=question)
return result['answer']
input_context = gr.Textbox(lines=10, label='Input Context', placeholder='Enter context here...')
input_question = gr.Textbox(label='Input Question', placeholder='Ask your question here...')
output_text = gr.Textbox(label="Response", placeholder='Response will display here..')
interface = gr.Interface(response, inputs=[input_context, input_question], outputs=output_text,
title="<div style='color: #336699; font-size: 24px; font-weight: bold; border: 2px solid #336699; padding: 10px; border-radius: 10px;'>Bert Context Based Question Answering</div>",
description=f"""<div style='color: #666666; font-family: Arial, sans-serif;'>
<p style='margin-top: 10px;'>Enter context and question to get the response.</p>
<p>Developed by <span style='color: #336699; font-weight: bold;'>{author}</span>.</p>
</div>""",
theme="default" # Change theme to default
)
# Define example contexts, questions, and expected responses
examples = [
["The capital of France is Paris.", "What is the capital of France?", "Paris"],
["Water boils at 100 degrees Celsius or 212 degrees Fahrenheit.", "At what temperature does water boil?", "100 degrees Celsius"],
["The Mona Lisa was painted by Leonardo da Vinci.", "Who painted the Mona Lisa?", "Leonardo da Vinci"],
]
def simulate_interaction():
for example in examples:
context, question, expected_response = example
input_context.value = context
input_question.value = question
time.sleep(2) # Simulating user typing delay
response_text = response(context, question)
output_text.value = response_text
time.sleep(2) # Simulating response delay
# Simulate user interaction
simulate_interaction()
# Deploy the interface
interface.launch(share=True, debug=True)
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