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()