import gradio as gr from fastai.vision.all import * import skimage learn = load_learner('ripeorrotten_apple.pkl') labels = ('ripe', 'rotten') 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 = "Apple Ripeness Classifier" description = "Is your apple ripe or rotten? Use this AQCC (apple quality control classifier) trained on web images with fastai." article = "
" examples = ['apple-fruit-ripe.jpg','rotten.jpg','rotting.jpg'] gr.Interface( fn=predict, inputs=gr.Image(type="pil"), outputs=gr.Label(num_top_classes=3), title=title, description=description, article=article, examples=examples ).launch()