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import gradio as gr | |
from transformers import pipeline | |
model = pipeline("question-answering", model="Eitanli/distilbert-qa-checkpoint-v3") | |
questions = ['which ingredients are mentioned?', | |
'what is the amount of ingredient mentioned?', | |
'what are the special instructions mentioned?'] | |
def predict(context, topk, answer_threshold): | |
output = {} | |
for question in questions: | |
output['Question'] = question | |
answers = model(question=question, context=context, topk=topk) | |
output['Answer'] = '\n'.join([ans['answer'] for ans in answers if ans['score'] > answer_threshold]) | |
return output | |
iface = gr.Interface( | |
fn=predict, | |
inputs=[ | |
gr.Textbox(label="Recipe line"), | |
gr.Slider(0, 0.99, step=0.01, value=0.8, label="answer_threshold", info="Select a threshold in [0, 0.99]"), | |
gr.Slider(1, 5, step=1.0, value=2, label="top k", info="Choose between 1 and 5")], | |
outputs=gr.Textbox(label='Questions and answers') | |
) | |
iface.launch() |