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"""Gradio app that showcases Scandinavian zero-shot text classification models."""
import gradio as gr
from transformers import pipeline
from luga import language as detect_language
# Load the zero-shot classification pipeline
classifier = pipeline(
"zero-shot-classification", model="alexandrainst/scandi-nli-large"
)
def sentiment_classification(doc: str) -> str:
"""Classify text into sentiment categories.
Args:
doc (str):
Text to classify.
Returns:
str:
The predicted sentiment category.
"""
# Detect the language of the text
language = detect_language(doc).name
# Get hypothesis template and candidate labels depending on the language
if language == "da":
hypothesis_template = "Dette eksempel er {}."
candidate_labels = ["positivt", "negativt", "neutralt"]
elif language == "sv":
hypothesis_template = "Detta exempel är {}."
candidate_labels = ["positivt", "negativt", "neutralt"]
elif language == "no":
hypothesis_template = "Dette eksemplet er {}."
candidate_labels = ["positivt", "negativt", "nøytralt"]
# Run the classifier on the text
result = classifier(
doc, candidate_labels=candidate_labels, hypothesis_template=hypothesis_template
)
# Return the predicted label
return result["labels"][0]
# Create the Gradio interface
interface = gr.Interface(
fn=sentiment_classification,
inputs=gr.inputs.Textbox(lines=5, label="Text"),
outputs=gr.outputs.Label(type="text"),
title="Scandinavian Zero-Shot Text Classification",
description="Classify text into sentiment categories.",
)
# Run the app
interface.launch()
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