| import gradio as gr | |
| from fastbook import load_learner | |
| from fastai.vision.all import * | |
| from PIL import Image | |
| js_func = """ | |
| function refresh() { | |
| const url = new URL(window.location); | |
| if (url.searchParams.get('__theme') !== 'dark') { | |
| url.searchParams.set('__theme', 'dark'); | |
| window.location.href = url.href; | |
| } | |
| } | |
| """ | |
| def predict(im): | |
| resized_im = im['composite'].resize((28,28)) | |
| pred,idx,probs = model.predict(resized_im) | |
| return dict(zip(categories, map(float,probs))) | |
| categories = ('0', '1', '2', '3', '4', '5', '6', '7', '8', '9') | |
| model = load_learner('./dr_model.pkl') | |
| labels = model.dls.vocab | |
| with gr.Blocks(js=js_func) as demo: | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Sketchpad(image_mode='L', brush=gr.Brush(default_color="FFFFFFFF"), type='pil'), | |
| outputs = "label", | |
| theme=gr.themes.Monochrome()) | |
| demo.launch() |