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 = "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 = ['00000007.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,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()