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import gradio as gr
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="NbAiLab/nb-bert-base-mnli")
def sequence_to_classify(sequence, labels):
#sequence_to_classify = 'Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september.'
#candidate_labels = ['politikk', 'helse', 'sport', 'religion']
hypothesis_template = 'Dette eksempelet er {}.'
#classifier(sequence_to_classify, candidate_labels, hypothesis_template=hypothesis_template, multi_class=True)
response = classifier(sequence, labels, hypothesis_template=hypothesis_template, multi_class=True)
clean_output = {labels[idx]: str(respose['scores'].pop(1)) for idx in response['labels']}
print("response is:{}".format(response))
print("clean_output: {}".format(clean_output))
return clean_output
example_text="""
Folkehelseinstituttets mest optimistiske anslag er at alle voksne er
ferdigvaksinert innen midten av september.'"""
example_labels=['politikk', 'helse', 'sport', 'religion']
def greet(name):
return "Hello " + name + "!!"
iface = gr.Interface(
title = "Zero-shot Classification of Norwegian Text",
description = "Demo of zero-shot classification using NB-Bert base model (Norwegian).",
fn=sequence_to_classify,
inputs=[gr.inputs.Textbox(lines=2,
label="Write a norwegian text you would like to classify...",
placeholder="Text here..."),
gr.inputs.Textbox(lines=2,
label="Possible candidate labels",
placeholder="labels here...")],
outputs=gr.outputs.Label(num_top_classes=3, label="Categories"),
capture_session=True,
interpretation="default"
,examples=[
[example_text, example_labels]
])
iface.launch() |