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<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 | |
import gradio as gr | |
transformers = await import_transformers_js() | |
pipeline = transformers.pipeline | |
pipe = await pipeline('sentiment-analysis') | |
async def classify(text): | |
return await pipe(text) | |
demo = gr.Interface(classify, "textbox", "json", examples=["It's a happy day in the neighborhood", "I'm an evil penguin", "It wasn't a bad film."]) | |
demo.launch() | |
</gradio-file> | |
</gradio-lite> | |
</body> | |
</html> |