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<!DOCTYPE html>
<html>
<head>
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto&display=swap" >
<style>
body {
font-family: 'Roboto', sans-serif;
font-size: 16px;
}
.logo {
height: 1em;
vertical-align: middle;
margin-bottom: 0.1em;
}
</style>
<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite@0.4.1/dist/lite.js"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite@0.4.1/dist/lite.css" />
</head>
<body>
<h2>
<img src="lite-logo.png" alt="logo" class="logo">
Gradio-lite (Gradio running entirely in your browser!)
</h2>
<p>Try it out! Once the Gradio app loads (can take 10-15 seconds), disconnect your Wifi and the machine learning model will still work!</p>
<gradio-lite>
<gradio-requirements>
transformers_js_py
</gradio-requirements>
<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('zero-shot-image-classification')
async def classify(image, classes):
data = await pipe(as_url(image), classes.split(","))
result = {item['label']: round(item['score'], 2) for item in data}
return result
demo = gr.Interface(classify, [gr.Image(label="Input image", type="filepath"), gr.Textbox(label="Classes separated by commas")], gr.Label())
demo.launch()
</gradio-file>
</gradio-lite>
</body>
</html> |