|
<!DOCTYPE html> |
|
<html> |
|
<head> |
|
<meta charset="utf-8"> |
|
<meta name="viewport" content="width=device-width, initial-scale=1"> |
|
<title>Gradio-Lite: Serverless Gradio Running Entirely in Your Browser</title> |
|
<meta name="description" content="Gradio-Lite: Serverless Gradio Running Entirely in Your Browser"> |
|
|
|
<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script> |
|
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css" /> |
|
|
|
<style> |
|
html, body { |
|
margin: 0; |
|
padding: 0; |
|
height: 100%; |
|
} |
|
</style> |
|
</head> |
|
<body> |
|
<gradio-lite> |
|
<gradio-file name="app.py" entrypoint> |
|
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(), |
|
] |
|
) |
|
|
|
demo.launch() |
|
</gradio-file> |
|
|
|
<gradio-requirements> |
|
transformers_js_py |
|
</gradio-requirements> |
|
</gradio-lite> |
|
</body> |
|
</html> |
