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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):
hypothesis_template = 'Dette eksempelet er {}.'
label_clean = str(labels).split(",")
response = classifier(sequence, label_clean, hypothesis_template=hypothesis_template, multi_class=True)
labels = response['labels']
scores = response['scores']
clean_output = {labels[idx]: float(scores[idx]) for idx in range(len(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.","Kutt smør i terninger, og la det temperere seg litt mens deigen elter. Ha hvetemel, sukker, gjær, salt og kardemomme i en bakebolle til kjøkkenmaskin. Bruker du fersk gjær kan du smuldre gjæren i bollen, eller røre den ut i melken. Alt vil ettehvert blande seg godt, så begge deler er like bra."]
example_labels=["politikk,helse,sport,religion", "helse,sport,religion, mat"]
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=10,
label="Possible candidate labels",
placeholder="labels here...")],
outputs=gr.outputs.Label(num_top_classes=3),
capture_session=True,
interpretation="default"
,examples=[
[example_text, example_labels]
])
iface.launch() |