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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/[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, 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> |