File size: 1,183 Bytes
f753a4b
 
 
 
 
 
 
 
 
 
0b23ffb
f753a4b
363bdc5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f753a4b
6377419
f753a4b
 
 
 
 
 
 
 
 
 
0b23ffb
5e9bba2
f753a4b
 
0b23ffb
6377419
f753a4b
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
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
# import gradio as gr

# def greet(name):
#     return "Hello " + name + "!!"

# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()

__all__ = ['is_cat', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']

import gradio as gr
from fastai.vision.all import *
from pathlib import Path

model_path = Path('models')
image_path = Path('images')

cloud_categories = (
  'Cirrus', 
  'Cirrostratus',
  'Cirrocumulus',
  'Altostratus',
  'Altocumulus',
  'Stratus',
  'Stratocumulus',
  'Nimbostratus',
  'Cumulus',
  'Cumulonimbus',
  'Lenticular'
)

cloud_examples = [image_path / f"{c}.jpg" for c in cloud_categories]

cloud_learner = load_learner(model_path/'cloudmodel.pkl')

def is_cat(x): return x[0].isupper()

learn = load_learner('model.pkl')

categories = ('Dog', 'Cat')

def classify_image(img):
    pred, idx, probs =learn.predict(img)
    return dict(zip(categories, map(float,probs)))

image= gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=cloud_examples)
intf.launch(inline=False)