| <!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 |
| 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> |
