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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() | |