from fastai.vision import *
from fastai.learner import load_learner
from PIL import Image
import gradio as gr

image = Image.open("examples/riku.jpg")
image.thumbnail((512,512))
image

learn = load_learner("model.pkl")
categories = list(learn.dls.vocab)


result,_,probs = learn.predict(image)
print(f"This is a: {result}")
print(f"Probability it's a {result}: {probs[categories.index(result)] * 100:.02f}%")

def classify_image(img):
    pred, ix, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))

print(classify_image(image))

# Whats the gradio input type? Image
image = gr.inputs.Image(shape=(192,192))
# Whats the gradio output type? Label
label = gr.outputs.Label()
# Set up some examples
examples = ["examples/kairi.jpg", "examples/riku.jpg", "examples/sora.jpg"]

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)