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from fastai.vision.all import *
import gradio as gr
learn = load_learner("ndpaircraft.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 = "NDP Aircraft Classifier"
description = "A NDP Aircraft classifier trained on images downloaded from the internet. Created as a demo for Gradio and HuggingFace Spaces."
image = gr.Image(shape=(224, 224))
label = gr.Label()
examples = ["f15.png", "f16.jpg", "chinook.jpg", "apache.jpg"]
interpretation = "default"
intf = gr.Interface(
fn=predict,
inputs=image,
outputs=label,
title=title,
description=description,
examples=examples,
interpretation=interpretation,
)
intf.launch(share=True) |