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import gradio as gr
from fastai.vision.core import PILImage
import fastai.vision.all as fav
import os
import sys
def is_cat(x):
return x[0].isupper() # Used by model
sys.modules["__main__"].is_cat = is_cat
if os.name != "posix":
print("Converting PosixPath to WindowsPath")
import pathlib
pathlib.PosixPath = pathlib.WindowsPath
learn = fav.load_learner("model.pkl")
labels = ["That's a dog", "That's a cat"]
def predict(img):
img = PILImage.create(img)
pred, pred_idx, probs = learn.predict(img)
pred = "That's a cat" if pred else "That's a dog"
return {labels[i]: float(probs[i]) for i in range(len(labels))}
iface = gr.Interface(
fn=predict,
inputs="image",
outputs="label",
examples=["images/dog1.jpg", "images/dog2.jpg", "images/cat1.png"],
live=True,
title="My first Gradio",
description="Well it's really all been said in the title",
)
iface.launch()
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