| import os |
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|
| os.system("pip install gradio==3.11") |
| os.system("pip install transformers") |
| os.system("pip install torch") |
|
|
| import requests |
| import gradio as gr |
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| from transformers import pipeline |
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| qa_model = pipeline("question-answering") |
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| def question(context,question): |
| output = qa_model(question = question, context = context) |
| return output['answer'] |
| |
| demo = gr.Interface( |
| fn=question, |
| inputs=[gr.Textbox(lines=8, placeholder="context Here..."), gr.Textbox(lines=2, placeholder="question Here...")], |
| outputs="text",title="Question answering app", |
| description="This is a question answering app, it can prove useful when you want to extract an information from a large text. All you need to do is copy and paste the text you want to query and then query it with a relevant question", |
| examples=[ |
| ["My name is Oluwafunbi Adeneye and I attended Federal University of Agriculture Abeokuta", "What is the name of Oluwafunbi's school?"], |
| ["Cocoa house is the tallest building in Ibadan","what is the name of the tallest building in Ibadan?"], |
| ], |
| ) |
| demo.launch() |
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|
