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import gradio as gr
from off_topic import OffTopicDetector, Translator
translator = Translator("facebook/nllb-200-distilled-600M")
detector = OffTopicDetector("openai/clip-vit-base-patch32", image_size="V", translator=translator)
def validate(item_id: str, use_title: bool, threshold: float):
images, domain, probas, valid_probas, invalid_probas = detector.predict_probas_item(item_id, use_title=use_title)
valid_images = [x for i, x in enumerate(images) if valid_probas[i].squeeze() >= threshold]
invalid_images = [x for i, x in enumerate(images) if valid_probas[i].squeeze() < threshold]
return f"## Domain: {domain}", valid_images, invalid_images
with gr.Blocks() as demo:
gr.Markdown("""
# Off topic image detector
### This app takes an item ID and classifies its pictures as valid/invalid depending on whether they relate to the domain in which it's been listed.
Input an item ID or select one of the preloaded examples below.""")
with gr.Row():
item_id = gr.Textbox(label="Item ID")
with gr.Column():
use_title = gr.Checkbox(label="Use translated item title", value=True)
threshold = gr.Number(label="Threshold", value=0.25, precision=2)
submit = gr.Button("Submit")
gr.HTML("<hr>")
domain = gr.Markdown()
valid = gr.Gallery(label="Valid images").style(grid=[1, 2, 3], height="auto")
gr.HTML("<hr>")
invalid = gr.Gallery(label="Invalid images").style(grid=[1, 2, 3], height="auto")
submit.click(inputs=[item_id, use_title, threshold], outputs=[domain, valid, invalid], fn=validate)
gr.HTML("<hr>")
gr.Examples(
examples=[["MLC572974424", True, 0.25], ["MLU449951849", True, 0.25], ["MLA1293465558", True, 0.25],
["MLB3184663685", True, 0.25], ["MLC1392230619", True, 0.25], ["MCO546152796", True, 0.25]],
inputs=[item_id, use_title, threshold],
outputs=[domain, valid, invalid],
fn=validate,
cache_examples=True,
)
demo.launch()
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