| import os | |
| os.system("pip install gradio==3.11") | |
| os.system("pip install transformers") | |
| os.system("pip install torch") | |
| import requests | |
| import gradio as gr | |
| from transformers import pipeline | |
| qa_model = pipeline("question-answering") | |
| 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() | |