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import gradio as gr | |
import markdownify | |
import html as pyhtml | |
from setfit import SetFitModel | |
model = SetFitModel.from_pretrained("./predictor_2") | |
def clean_text(text): | |
text = markdownify.markdownify( | |
pyhtml.unescape(text or ""), | |
heading_style="ATX").strip() | |
if len(text) > 250: | |
text = text[:250] + "..." | |
return text | |
def text_template(title="",description="",url="",comment=""): | |
description = clean_text(description) | |
comment = clean_text(comment) | |
return f"""Title: "{title}" | |
Description: "{description}" | |
Url: "{url}" | |
First Comment: "{ | |
comment | |
}\"""" | |
def text_classifier(title="",description="",url="",comment=""): | |
text = text_template(title,description,url,comment) | |
prediction = model.predict_proba([text])[0] | |
a,b = prediction | |
return {'non-ai': float(a), 'ai': float(b)} | |
inputs = [ | |
gr.Textbox( | |
value="", | |
label="Title" | |
), | |
gr.Textbox( | |
value="", | |
label="Description" | |
), | |
gr.Textbox( | |
value="", | |
label="URL" | |
), | |
gr.Textbox( | |
value="", | |
label="Comment" | |
), | |
] | |
demo = gr.Interface(fn=text_classifier, | |
inputs=inputs, | |
outputs="label") | |
demo.launch(show_api=True) | |