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| import gradio as gr | |
| from fastai.vision.all import * | |
| title = "Interstellar" | |
| description = ( | |
| "Experimental Astronomical Classifier built for the fast.ai 'Deep Learning' " | |
| "course by fine tuning ResNet50 (1 + 3 epochs) with a custom dataset " | |
| "of images (150 per label with augmentation)." | |
| ) | |
| inputs = gr.components.Image() | |
| outputs = gr.components.Label() | |
| examples = "examples" | |
| model_class = load_learner("models/model.class.pkl") | |
| labels_class = model_class.dls.vocab | |
| model_object = load_learner("models/model.object.pkl") | |
| labels_object = model_object.dls.vocab | |
| def predict_class(img): | |
| pred, pred_idx, probs = model_class.predict(img) | |
| return dict(zip(labels_class, map(float, probs))) | |
| def predict_object(img): | |
| pred, pred_idx, probs = model_object.predict(img) | |
| return dict(zip(labels_object, map(float, probs))) | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Class Prediction"): | |
| gr.Interface( | |
| fn=predict_class, | |
| inputs=inputs, | |
| outputs=outputs, | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| ).queue(default_concurrency_limit=5) | |
| with gr.Tab("Object Prediction"): | |
| gr.Interface( | |
| fn=predict_object, | |
| inputs=inputs, | |
| outputs=outputs, | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| ).queue(default_concurrency_limit=5) | |
| demo.launch() | |