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import gradio as gr
from fastai.vision.all import *
import pathlib
import os
from PIL import Image
temp = pathlib.PosixPath
if os.name == 'nt':
pathlib.PosixPath = pathlib.WindowsPath
def is_cat(x): return x[0].isupper()
def greet(name):
return "Hello " + name + "!!"
learner = load_learner('model.pkl')
categories = ('Dog', "Cat")
def classify_image(img):
resized_image = img.resize((192, 192)) # Resize here manually
prediction, index, probs = learner.predict(resized_image)
return dict(zip(categories,map(float,probs)))
image = gr.Image(type="pil")
label = gr.Label()
examples = ['samples/cute_dog.jpg',"samples/cat_graffity.jpg"]
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False) |