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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/[email protected]/dist/lite.js"></script>
		<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/[email protected]/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>