import gradio as gr from fastai.vision.all import * import skimage learn = load_learner('export.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))} import os for root, dirs, files in os.walk(r'sample_images/'): for filename in files: print(filename) title = "Pet Breed Classifier" description = "A pet breed classifier trained on the Oxford Pets dataset" interpretation='default' examples = ["sample_images/"+file for file in files] article="

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" 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()