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| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| learn = load_learner('resnet_emoji50.pkl') | |
| labels = learn.dls.vocab | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred,pred_idx,probs = learn.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| title = "Perfect_Window/Damaged_Window Classifier" | |
| description = "A Window/Damaged_Window classifier trained on the google random images. Please use the examples to try it out. Created as a demo." | |
| examples = ['w2.jpeg','w3.jpg','w4.jpg','w5.jpg'] | |
| interpretation='default' | |
| enable_queue=True | |
| gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch() |