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<!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()
AutoProcessor = transformers.AutoProcessor
AutoModel = transformers.AutoModel
RawImage = transformers.RawImage
processor = await AutoProcessor.from_pretrained('Xenova/yolov9-c');
# TODO: Resize the input image
model = await AutoModel.from_pretrained('Xenova/yolov9-c');
async def detect(image_path):
image = await RawImage.read(image_path)
processed_input = await processor(image)
# Predict bounding boxes
result = await model(images=processed_input["pixel_values"]);
outputs = result["outputs"] # Tensor
np_outputs = outputs.numpy() # [xmin, ymin, xmax, ymax, score, id][]
gradio_labels = [
# List[Tuple[numpy.ndarray | Tuple[int, int, int, int], str]]
(
(
int(xmin),
int(ymin),
int(xmax),
int(ymax),
),
model.config.id2label[str(int(id))],
)
for xmin, ymin, xmax, ymax, score, id in np_outputs
]
annotated_image_data = image_path, gradio_labels
return annotated_image_data, np_outputs
demo = gr.Interface(
detect,
gr.Image(type="filepath"),
[
gr.AnnotatedImage(),
gr.JSON(),
],
examples=[
["cats.jpg"],
["city-streets.jpg"],
]
)
demo.launch()
</gradio-file>
<gradio-file name="cats.jpg" url="https://huggingface.co./datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg" />
<gradio-file name="city-streets.jpg" url="https://huggingface.co./datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg" />
<gradio-requirements>
transformers_js_py
</gradio-requirements>
</gradio-lite>
</body>
</html>
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