import gradio as gr from fastai.vision.all import * import skimage learn = load_learner('model.pkl') labels = learn.dls.vocab labels = list(map(lambda x: x.replace('food recipe', 'Food Plate'), labels)) labels = list(map(lambda x: x.replace('empty plate', 'Empty Plate'), labels)) 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 = "Empty Plate Classifier" description = "We are passionate about the emptiness of plates, but it is often rather challenging to determine whether a plate is empty or not. This model will help you in that task!" article = "
" examples = ['empty_plate.jpeg', 'full_plate.jpeg'] 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()