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6e6121a1ac
* placeholder * changelog * added to readme * client * implement futures * utils * scripts * lint * reorg * scripts * serialization * cleanup * fns * serialize * cache * callbacks * updates * formatting * packaging * requirements * remove changelog * client * access token * formatting * deprecate * format backend * client replace * updates * moving from utils * remove code duplication * rm duplicates * simplify * galleryserializer * serializable * load serializers * fixing errors * errors * typing * tests * changelog * lint * fix lint * fixing files * formatting * type * fix type checking * changelog * changelog * Update client/python/gradio_client/client.py Co-authored-by: Lucain <lucainp@gmail.com> * formatting, tests * formatting, tests * gr.load * refactoring * refactoring' * formatting * formatting * tests * tests * fix tests * cleanup * added tests * adding scripts * formatting * address review comments * readme * serialize info * remove from changelog * version 0.0.2 released * lint * type fix * check * type issues * hf_token * update hf token * telemetry * docs, circle dependency * hf token * formatting * updates * sort * script * external * docs * formatting * fixes * scripts * requirements * fix tests * context * changes * formatting * fixes * format fix --------- Co-authored-by: Lucain <lucainp@gmail.com>
32 lines
818 B
Python
32 lines
818 B
Python
import gradio as gr
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import pathlib
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current_dir = pathlib.Path(__file__).parent
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images = [str(current_dir / "cheetah1.jpeg"), str(current_dir / "cheetah1.jpg"), str(current_dir / "lion.jpg")]
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img_classifier = gr.load(
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"models/google/vit-base-patch16-224", examples=images, cache_examples=False
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)
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def func(img, text):
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return img_classifier(img), text
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using_img_classifier_as_function = gr.Interface(
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func,
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[gr.Image(type="filepath"), "text"],
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["label", "text"],
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examples=[
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[str(current_dir / "cheetah1.jpeg"), None],
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[str(current_dir / "cheetah1.jpg"), "cheetah"],
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[str(current_dir / "lion.jpg"), "lion"],
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],
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cache_examples=False,
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)
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demo = gr.TabbedInterface([using_img_classifier_as_function, img_classifier])
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if __name__ == "__main__":
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demo.launch() |