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import gradio as gr | |
from transformers import pipeline | |
# Load model from Hugging Face Hub | |
classifier = pipeline("text-classification", model="sandbox338/hatespeech") | |
# Map model labels to readable labels | |
label_map = { | |
"LABEL_0": "Non-hate speech", | |
"LABEL_1": "Political hate speech", | |
"LABEL_2": "Offensive language" | |
} | |
# Classification function | |
def classify_text(text): | |
result = classifier(text)[0] | |
label = result['label'] | |
return label_map.get(label, "Unknown") | |
# Example inputs for testing | |
examples = [ | |
["Hii ni ujumbe wa kawaida bila matusi."], | |
["Wanasiasa hawa ni wabaya na lazima waondoke!"], | |
["Unasema upuuzi na wewe ni mjinga kabisa!"] | |
] | |
# Gradio Interface | |
interface = gr.Interface( | |
fn=classify_text, | |
inputs=gr.Textbox(lines=4, placeholder="Andika maandishi ya Kiswahili hapa..."), | |
outputs="text", | |
title="Swahili Hate Speech Classifier", | |
examples=examples | |
) | |
if __name__ == "__main__": | |
interface.launch() | |