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("
") domain = gr.Markdown() valid = gr.Gallery(label="Valid images").style(grid=[1, 2, 3], height="auto") gr.HTML("
") 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("
") 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()