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from transformers import pipeline
import gradio as gr
# Initialize the classifier
classifier = pipeline(
"sentiment-analysis",
model="wjbmattingly/human-remains-classifier-modernbert-large",
max_length=4000,
truncation=True
)
# Define the prediction function
def predict_text(text):
result = classifier(text)
return result[0]['label'], result[0]['score']
# Create the Gradio interface
demo = gr.Interface(
fn=predict_text,
inputs=gr.Textbox(label="Enter text to analyze"),
outputs=[
gr.Label(label="Classification"),
gr.Number(label="Confidence Score")
],
title="Human Remains Text Classifier",
description="Enter text to classify whether it contains references to human remains."
)
# Launch the app
if __name__ == "__main__":
demo.launch()
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