| # import gradio as gr | |
| # | |
| # def greet(name): | |
| # return "Hello " + name + "!!" | |
| # | |
| # demo = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| # demo.launch(share=True) | |
| # AUTOGENERATED! DO NOT EDIT! File to edit: inference-face-crop-model.ipynb. | |
| # %% auto 0 | |
| __all__ = ['learner', 'categories', 'classify_image'] | |
| # %% inference-face-crop-model.ipynb 10 | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| learner = load_learner('./face-crop-model.pkl') | |
| categories = ('happy', 'other') | |
| def classify_image(img): | |
| img = img["composite"] | |
| # print(img.shape) | |
| img = img[:,:, :3] | |
| pred, idx, probs = learner.predict(img) | |
| return dict(zip(categories, map(float, probs))) | |
| with gr.Blocks() as demo: | |
| image_edit = gr.ImageEditor(type="numpy", crop_size="1:1.2", canvas_size=(300, 300)) | |
| # image = gr.Image() | |
| label = gr.Label() | |
| examples = ['happy.png', "happy2.png", "happy3.png", "happy4.png", 'other.png', 'other2.png', 'other3.png', 'other4.png', 'other5.png',] | |
| intf = gr.Interface(fn=classify_image, inputs=image_edit, outputs=label, examples=examples, | |
| description="Use crop button to resize around the face for best results") | |
| # intf.launch(inline=False) | |
| demo.launch(share=True) | |