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# Bismillahir Rahmaanir Raheem
# Almadadh Ya Gause Radi Allahu Ta'alah Anh - Ameen
from fastai.vision.all import *
import gradio as gr
def is_cat(x):
return x[0].isupper()
# load the trained fast ai model for predictions
learn = load_learner('model.pkl')
# define the function to call
categories = ('Dog', 'Cat')
def predict(img):
pred, idx, probs = learn.predict(img)
return dict(zip(categories, map(float, probs)))
title = "Cat or Dog Predictor"
description = "A cat or dog predictor model trained on the Pets dataset using ResNet18 via <a href='http://www.fast.ai/' target='_blank'>fast.ai</a>."
article = "<p style='text-align: center'><span style='font-size: 15pt;'>Cat or Dog Predictor. Zakia Salod. 2022. </span></p>"
image = gr.inputs.Image(shape=(512, 512))
label = gr.outputs.Label()
examples = [
['cat1.jpg'],
['dog1.jpg'],
['cat2.jpg'],
['dog2.jpg'],
['cat3.jpg'],
['dog3.jpg'],
['cat4.jpg'],
['dog4.jpg'],
]
interpretation = 'default'
enable_queue = True
iface = gr.Interface(
fn=predict,
title=title,
description=description,
article=article,
inputs=image,
outputs=label,
theme="default",
examples=examples,
interpretation=interpretation,
enable_queue=enable_queue
)
iface.launch(inline=False)
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