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File size: 1,461 Bytes
eb5721d 37602f9 eb5721d ae712a9 eb5721d 8d8ea5a eb5721d be92941 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | #__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
#import timm
# %% app.ipynb 2
from fastai.vision.all import *
import gradio as gr
#import timm
def is_cat(x): return x[0].isupper()
# %% app.ipynb 4
#f1_car_convnext_v2.pkl
#f1_car
learn = load_learner('f1_car_convnext_v2.pkl')
# %% app.ipynb 6
categories = ('McLaren F1 cars', 'Ferrari F1 racing cars', 'Redbull F1 racing cars', 'Mercedes AMG F1 racing cars', 'Aston Martin F1 racing cars', 'Alpine F1 racing cars', 'Haas F1 racing cars' , 'RB F1 racing cars', 'Williams F1 racing cars', 'Kick Sauber F1 racing cars')
#categories = ( 'Mercedes cars', 'Ferrari cars', 'BMW cars', 'Bentley cars', 'Porsche cars', 'Aston Martin cars', 'Audi cars' , 'Maserati cars', 'McLaren cars', 'Lamborghini cars', 'Bugatti cars', 'Koenigsegg cars', 'Pagani cars', 'Tesla cars')
#categories = ('Dog', 'Cat')
def classify_image(img):
pred, idx, probs = learn.predict(img)
return dict(zip(categories, map(float, probs)))
# %% app.ipynb 8
from gradio.components import Image, Label
# %% app.ipynb 9
image = Image(width=300, height=240)
label = Label()
examples = ['mclaren.jpg', 'ferrari_f1.jpg', 'redbull_f1.jpg',
'merc.jpg', 'aston_martin.jpg', 'alpine.jpg', 'haas.jpg',
'sauber.jpg', 'rb.jpg', 'williams.jpg'
]
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False) |