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from fastai.vision.all import load_learner
import gradio as gr
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('snake-recognizer-v0.pkl')
def recognize_snake(photo):
pred,idx, probs = model.predict(photo)
return pred
image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label(num_top_classes=5)
examples = [
'viper-2.jpg',
'shutterstock_2062214282-edited-1-scaled.jpg',
'download (3).jpg',
'download (4).jpg',
'Naja_sputatrix.jpg',
'download (6).jpg',
'download.jpg',
'Naja-pallida-by-Wikimedia-commons.jpg',
'istockphoto-1358737491-612x612.jpg',
'istockphoto-916895080-612x612.jpg',
'images.jpg',
'high.jpg',
'download (2).jpg',
'download (5).jpg',
'black-necked-spitting-cobra-naja-nigricollis-wklein.jpg',
'4538a8531287f0a3ab464d0b9dd69744.jpg',
'Bothriechis_schlegelii_(La_Selva_Biological_Station).jpg'
]
iface = gr.Interface(fn=recognize_snake, inputs=image, outputs=label, examples=examples)
iface.launch(inline=False) |