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from fastai.vision.all import load_learner
import gradio as gr
import pathlib
temp=pathlib.PosixPath
pathlib.PosixPath=pathlib.WindowsPath
snake_labels = (
"Monocled cobra",
"Egyptian cobra",
"Black-necked spitting cobra",
"Samar cobra",
"Red spitting cobra",
"Javan spitting cobra",
"Spectacled cobra",
"Russell's viper",
"Horned vipers",
"Bush vipers",
"Eyelash viper",
"Saw-scaled vipers",
"Banded krait",
"Black mamba",
"Inland taipan",
"Eastern brown snake",
"Rattle snake",
"King cobra"
)
model = load_learner('models/snake-recognizer-v0.pkl')
def recognize_snake(photo):
pred, idx, probs = model.predict(photo)
return dict(zip(snake_labels, map(float, probs)))
image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples = [
'test data/viper-2.jpg',
'test data/shutterstock_2062214282-edited-1-scaled.jpg',
'test data/download (3).jpg',
'test data/download (4).jpg',
'test data/Naja_sputatrix.jpg',
'test data/download (6).jpg',
'test data/download.jpg'
]
iface = gr.Interface(fn=recognize_snake, inputs=image, outputs=label, examples=examples)
iface.launch(inline=False,share=True) |