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# import gradio as gr
# def greet(name):
# return "Hello " + name + "!!!"
# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()
# AUTOGENERATED! DO NOT EDIT! File to edit: ../cat-vs-dog.ipynb.
# %% auto 0
__all__ = [
"learn",
"categories",
"image",
"label",
"examples",
"intf",
"is_cat",
"classify_image",
]
# %% ../cat-vs-dog.ipynb 3
from fastai import *
from fastai.vision.all import *
import gradio as gr
# %% ../cat-vs-dog.ipynb 4
# Define function to label the data based on filename rule from dataset creators
def is_cat(x):
return "cat" if x.name[0].isupper() else "dog"
# %% ../cat-vs-dog.ipynb 18
learn = load_learner("export.pkl")
# %% ../cat-vs-dog.ipynb 19
categories = ("Cat", "Dog")
def classify_image(img):
pred, idx, probs = learn.predict(img)
return dict(zip(categories, map(float, probs)))
# %% ../cat-vs-dog.ipynb 20
image = gr.Image(shape=(192, 192))
label = gr.Label()
examples = ["dog.jpg", "cat.jpg"]
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False, share=False)
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