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from fastai.vision.all import * |
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import gradio as gr |
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import timm |
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import dill |
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import os |
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is_catan_learn = load_learner('./models/catan-model-paperspace-2022-11-29-05-17-46.pkl', pickle_module=dill) |
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catan_category_learn = load_learner('./models/categories-of-catan-3.pkl', pickle_module=dill) |
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def classify_image(img): |
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pred, pred_idx, probs = is_catan_learn.predict(img) |
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if float(probs[1]) < 0.2: |
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categories = ('Not Catan', 'Catan') |
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message = f'Did not detect Catan in this upload: *{probs[1]:.4f}%*. Choose another photo with Catan in it and we will categorize what kind of Catan we find.' |
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details = dict(zip(categories, map(float, probs))) |
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else: |
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pred, pred_idx, probs = catan_category_learn.predict(img) |
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message = f'Prediction: *{pred}*; Probability: *{probs[pred_idx]:.04f}%*' |
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categories = catan_category_learn.dls.vocab |
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details = dict(zip(categories, map(float, probs))) |
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return details, message |
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image = gr.inputs.Image(shape=(192, 192)) |
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label = gr.outputs.Label() |
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description = gr.Markdown() |
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examples_dir_path = './examples/' |
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examples = [(examples_dir_path + filename) for filename in os.listdir(examples_dir_path) if filename[:1] != '.'] |
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intf = gr.Interface(fn=classify_image, inputs=image, outputs=[label, description], examples=examples) |
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intf.launch() |
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