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| # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb. | |
| # %% auto 0 | |
| __all__ = ['interpretation', 'enable_queue', 'title', 'description', 'learners', 'models', 'active_name', 'active_model', | |
| 'example_images', 'demo', 'classify_image', 'select_model', 'update_matrix', 'update_losses'] | |
| # %% app.ipynb 1 | |
| from fastai.vision.all import * | |
| import gradio as gr | |
| interpretation='default' | |
| enable_queue=True | |
| title = "FastAI - Big Cats Classifier" | |
| description = "Classify big cats using all Resnet models available pre-trained in FastAI" | |
| # %% app.ipynb 2 | |
| learners = { | |
| "resnet-18" : 'models/resnet18-model.pkl', | |
| "resnet-34" : 'models/resnet34-model.pkl', | |
| "resnet-50" : 'models/resnet50-model.pkl', | |
| "resnet-101": 'models/resnet101-model.pkl', | |
| "resnet-152": 'models/resnet152-model.pkl' | |
| } | |
| models = list(learners.keys()) | |
| active_name = "resnet-18" | |
| active_model = learners[active_name] | |
| # %% app.ipynb 3 | |
| def classify_image(img): | |
| learn = load_learner(active_model) | |
| pred,idx,probs = learn.predict(img) | |
| return dict(zip(learn.dls.vocab, map(float, probs))) | |
| def select_model(model_name): | |
| if model_name not in models: | |
| model_name = "resnet-18" | |
| active_name = model_name | |
| active_model = learners[active_name] | |
| return model_name.upper() | |
| def update_matrix(): | |
| return "models/" + active_name.replace('-','',1) + "-confusion-matrix.png" | |
| def update_losses(): | |
| return "models/" + active_name.replace('-','',1) + "-top-losses.png" | |
| # %% app.ipynb 5 | |
| example_images = [ 'cheetah.jpg', 'jaguar.jpg', 'tiger.jpg', 'cougar.jpg', 'lion.jpg', 'african leopard.jpg', 'clouded leopard.jpg', 'snow leopard.jpg', 'hidden.png', 'hidden2.png' ] | |
| demo = gr.Blocks() | |
| with demo: | |
| with gr.Column(variant="panel"): | |
| image = gr.inputs.Image(label="Pick an image") | |
| model = gr.inputs.Dropdown(label="Select a model", choices=models) | |
| with gr.Row(equal_height=True): | |
| btnClassify = gr.Button("Classify") | |
| btnClear = gr.Button("Clear") | |
| with gr.Column(variant="panel"): | |
| selected = gr.outputs.Textbox(label="Active Model") | |
| with gr.Row(equal_height=True): | |
| matrix=gr.outputs.Image(type='filepath', label="Confusion Matrix") | |
| losses=gr.outputs.Image(type='filepath', label="Top Losses") | |
| result = gr.outputs.Label(label="Result") | |
| img_gallery = gr.Examples(examples=example_images, inputs=image) | |
| # Register all event listeners | |
| model.change(fn=select_model, inputs=model, outputs=selected) | |
| model.change(fn=update_matrix, outputs=matrix) | |
| model.change(fn=update_losses, outputs=losses) | |
| btnClassify.click(fn=classify_image, inputs=image, outputs=result) | |
| btnClear.click(fn=lambda: gr.Image.update(value=None), inputs=None, outputs=None) | |
| demo.launch(debug=True, inline=False) | |
| # intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=example_images, title=title, description=description ) | |
| # if __name__ == "__main__": | |
| # intf.launch(debug=True, inline=False) | |