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| from fastai.vision.all import * | |
| from fastai.vision.all import load_learner | |
| import fastai | |
| import os | |
| import gradio as gr | |
| import pathlib | |
| # from google.colab import drive | |
| # drive.mount('/content/drive/') | |
| temp = pathlib.WindowsPath | |
| pathlib.WindowsPath = pathlib.PosixPath | |
| model_dir = "models/parrot-recognizer-v3.pkl" | |
| model = load_learner(model_dir) | |
| parrot_species = ['african grey parrot', | |
| 'australian king parrot', | |
| 'blue lorikeet', | |
| 'blue-and-yellow macaw', | |
| 'blue-headed parrot', | |
| 'budgerigar', | |
| 'burrowing parrot', | |
| 'caique parrot', | |
| 'catalina macaw', | |
| 'chestnut-fronted macaw', | |
| 'cockatiels', | |
| 'crimson rosella', | |
| 'cuban amazon', | |
| 'eclectus parrot', | |
| 'galah', | |
| 'golden parakeet', | |
| 'great green macaw', | |
| 'great hanging parrot', | |
| 'greater vasa parrot', | |
| 'hahn_s macaws', | |
| 'hooded parrot', | |
| 'hyacinth macaw', | |
| 'kea', | |
| 'kākāpō', | |
| 'lovebirds', | |
| 'monk parakeet', | |
| 'orange-winged amazon', | |
| 'palm cockatoo', | |
| 'parrotlet', | |
| 'plum-headed parakeet', | |
| 'puerto rican amazon', | |
| 'rainbow lorikeet', | |
| 'red-breasted parakeet', | |
| 'red-crowned amazon', | |
| 'red-crowned parakeet', | |
| 'red-fan parrot', | |
| 'red-shouldered macaw', | |
| 'red-tailed black cockatoos', | |
| 'rose-ringed parakeet', | |
| 'saint vincent amazon', | |
| 'scarlet macaw', | |
| 'senegal parrot', | |
| 'spixs macaw', | |
| 'sun conure', | |
| 'thick-billed parrot', | |
| 'turquoise-fronted amazon', | |
| 'vernal hanging parrot', | |
| 'white cockatoo', | |
| 'yellow-collared macaws', | |
| 'yellow-headed amazon'] | |
| def recognize_image(image): | |
| pred, idx, probs = model.predict(image) | |
| return dict(zip(parrot_species, map(float, probs))) | |
| # im = "/content/drive/MyDrive/Learnings/fai/test_images/unknown_12.jpg" | |
| # img = PILImage.create(im) | |
| # img.thumbnail((192,192)) | |
| # img | |
| # recognize_image(img) | |
| image = gr.inputs.Image(shape=(192,192)) | |
| label = gr.outputs.Label(num_top_classes=5) | |
| 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", | |
| "test_images/unknown_05.jpg", | |
| "test_images/unknown_06.jpg", | |
| "test_images/unknown_07.jpg", | |
| "test_images/unknown_08.jpg", | |
| "test_images/unknown_09.jpg", | |
| "test_images/unknown_10.jpg", | |
| "test_images/unknown_11.jpg", | |
| "test_images/unknown_12.jpg", | |
| "test_images/unknown_13.jpg", | |
| "test_images/unknown_14.jpg", | |
| ] | |
| iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples) | |
| iface.launch(inline=False) | |