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# import gradio as gr
# def greet(name):
# return "Hello " + name + "!!"
# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()
__all__ = ['is_cat', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
import gradio as gr
from fastai.vision.all import *
from pathlib import Path
model_path = Path('models')
image_path = Path('images')
cloud_categories = (
'Cirrus',
'Cirrostratus',
'Cirrocumulus',
'Altostratus',
'Altocumulus',
'Stratus',
'Stratocumulus',
'Nimbostratus',
'Cumulus',
'Cumulonimbus',
'Lenticular'
)
cloud_examples = [image_path / f"{c}.jpg" for c in cloud_categories]
cloud_learner = load_learner(model_path/'cloudmodel.pkl')
def is_cat(x): return x[0].isupper()
learn = load_learner('model.pkl')
categories = ('Dog', 'Cat')
def classify_image(img):
pred, idx, probs =learn.predict(img)
return dict(zip(categories, map(float,probs)))
image= gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=cloud_examples)
intf.launch(inline=False) |