| from fastai.vision.all import * | |
| from fastai.vision.all import load_learner | |
| import gradio as gr | |
| import pathlib | |
| temp = pathlib.PosixPath | |
| pathlib.PosixPath = pathlib.WindowsPath | |
| #!export | |
| pollutant_labels = ( | |
| "antennas", | |
| "billboard", | |
| "broken roads", | |
| "construction sites", | |
| "electric pole", | |
| "garbage can", | |
| "graffiti", | |
| "smog", | |
| "street litter" | |
| ) | |
| model = load_learner('models/vispol-1-recognizer-v0.pkl') | |
| def recognize_image(image): | |
| pred, idx, probs = model.predict(image) | |
| return dict(zip(pollutant_labels, map(float, probs))) | |
| #!export | |
| image = gr.inputs.Image(shape=(256,256)) | |
| label = gr.outputs.Label() | |
| examples = [ | |
| 'test_images/unknown_00.jpg', | |
| 'test_images/unknown_01.jpg', | |
| 'test_images/unknown_02.jpg', | |
| 'test_images/unknown_03.jpg', | |
| 'test_images/unknown_04.jpg' | |
| ] | |
| iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples) | |
| iface.launch(inline=False, share=True) |