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# AUTOGENERATED! DO NOT EDIT! File to edit: test.ipynb.

# %% auto 0
__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'inf', 'classify_img']

# %% test.ipynb 2
from fastai.vision.all import *
import gradio as gr

# %% test.ipynb 4
learn = load_learner('model.pkl')

# %% test.ipynb 5
categories = ('Art Deco', 'Baroque', 'Classical', 'Craftsman', 'Gothic', 'Renaissance', 'Tudor', 'Victorian')

def classify_img(img):
    pred,idx,probs = learn.predict(img)
    # Convert each tensor probability to a float properly
    return dict(zip(categories, map(float, probs)))

# %% test.ipynb 8
# Define Gradio interface
image = gr.Image(image_mode="RGB", type="pil")
label = gr.Label()
examples = ['classical.jpg', 'artdec.jpg', 'victorian.jpeg']

inf = gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)

# Launch the app
inf.launch(inline=False)

	# AUTOGENERATED! DO NOT EDIT! File to edit: test.ipynb.

	# %% auto 0
	__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'inf', 'classify_img']

	# %% test.ipynb 2
	from fastai.vision.all import *
	import gradio as gr

	# %% test.ipynb 4
	learn = load_learner('model.pkl')

	# %% test.ipynb 5
	categories = ('Art Deco', 'Baroque', 'Classical', 'Craftsman', 'Gothic', 'Renaissance', 'Tudor', 'Victorian')

	def classify_img(img):
	pred,idx,probs = learn.predict(img)
	# Convert each tensor probability to a float properly
	return dict(zip(categories, map(float, probs)))

	# %% test.ipynb 8
	# Define Gradio interface
	image = gr.Image(image_mode="RGB", type="pil")
	label = gr.Label()
	examples = ['classical.jpg', 'artdec.jpg', 'victorian.jpeg']

	inf = gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)

	# Launch the app
	inf.launch(inline=False)