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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # Cell | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| # Cell | |
| learn = load_learner('export.pkl') | |
| def classify_image(img): | |
| pred,idx,probs = learn.predict(img) | |
| return f'Prediction: {pred}; Probability: {probs[idx]:.04f}' | |
| # Cell | |
| image = gr.inputs.Image(shape=(192, 192)) | |
| label = gr.outputs.Label() | |
| examples = ['images/A_000.png'] | |
| intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples) | |
| intf.launch(inline=False) | |
| """ | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| from fastai.vision.widgets import * | |
| from IPython.display import display | |
| #|export | |
| learn_inf = load_learner('export.pkl') | |
| # %% | |
| learn_inf.dls.vocab | |
| #|export | |
| btn_upload = widgets.FileUpload() | |
| btn_upload | |
| btn_upload = SimpleNamespace(data = ['images/A_000.png']) | |
| #|export | |
| img = PILImage.create(btn_upload.data[-1]) | |
| #|export | |
| out_pl = widgets.Output() | |
| out_pl.clear_output() | |
| with out_pl: display(img.to_thumb(128,128)) | |
| out_pl | |
| #|export | |
| pred,pred_idx,probs = learn_inf.predict(img) | |
| #|export | |
| lbl_pred = widgets.Label() | |
| lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}' | |
| lbl_pred | |
| #|export | |
| btn_run = widgets.Button(description='Classify') | |
| btn_run | |
| #|export | |
| def on_click_classify(change): | |
| img = PILImage.create(btn_upload.data[-1]) | |
| out_pl.clear_output() | |
| with out_pl: display(img.to_thumb(128,128)) | |
| pred,pred_idx,probs = learn_inf.predict(img) | |
| lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}' | |
| btn_run.on_click(on_click_classify) | |
| #|export | |
| btn_upload = widgets.FileUpload() | |
| #|export | |
| VBox([widgets.Label('Select your green!'), | |
| btn_upload, btn_run, out_pl, lbl_pred]) | |
| """ | |