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from fastai.vision.all import *
import gradio as gr
import pathlib, os
# def greet(name):
# return "Hello " + name + "!!!!"
def is_cat(x): return x[0].isupper()
categories = ['Dog', 'Cat']
def classify_image(img):
if os.name == 'nt': # workaround for Windows
pathlib.PosixPath = pathlib.WindowsPath
learn = load_learner('model_cat-or-dog.pkl')
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 = ['img_20180720_163054.jpg', 'img_20210614_141029.jpg', 'img_20210614_140945.jpg', 'img_20210613_193627.jpg', 'img_20210613_184022.jpg', 'cat.4764.jpg', 'cat.4782.jpg']
iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
iface.launch() |