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import gradio as gr
import datetime
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)
predicted_labels = response['labels']
predicted_scores = response['scores']
clean_output = {idx: float(predicted_scores.pop(0)) for idx in predicted_labels}
print("Date:{} , Sequece:{}, Labels: {}".format(
str(datetime.datetime.now()),
sequence,
predicted_labels)
)
return clean_output
example_text1="Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september."
example_labels1="politikk,helse,sport,religion"
example_text2="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_labels2="helse,sport,religion, mat"
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_text1, example_labels1],
[example_text2, example_labels2]
])
iface.launch() |