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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 = "<p style='text-align: center'><a href='https://foodand.eu/' target='_blank'>Food& Magazine</a></p>" | |
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() | |