from transformers_js import import_transformers_js, as_url
import gradio as gr
transformers = await import_transformers_js()
pipeline = transformers.pipeline
pipe = await pipeline('object-detection', "Xenova/yolos-tiny")
async def detect(input_image):
result = await pipe(as_url(input_image))
gradio_labels = [
# List[Tuple[numpy.ndarray | Tuple[int, int, int, int], str]]
(
(
int(item["box"]["xmin"]),
int(item["box"]["ymin"]),
int(item["box"]["xmax"]),
int(item["box"]["ymax"]),
),
item["label"],
)
for item in result
]
annotated_image_data = input_image, gradio_labels
return annotated_image_data, result
demo = gr.Interface(
detect,
gr.Image(type="filepath"),
[
gr.AnnotatedImage(),
gr.JSON(),
],
examples=[
["cats.jpg"]
]
)
demo.launch()
transformers_js_py