File size: 885 Bytes
07f1a32 d78fd93 07f1a32 d78fd93 07f1a32 15a5bf7 1a15eb9 d78fd93 07f1a32 d78fd93 07f1a32 d78fd93 07f1a32 d78fd93 1a15eb9 07f1a32 |
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 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
#|default_exp sala
# In[2]:
#|export
from fastai.vision.all import *
import gradio as gr
def is_cat(x): return x[0].isupper()
# In[3]:
im= PILImage.create('dog.jpg')
im.thumbnail((192,192))
im
# In[4]:
#|export
import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath
# In[5]:
#|export
learn=load_learner('model.pkl')
# In[6]:
learn.predict(im)
# In[7]:
#|export
categories=('dog','cat')
def classify_image(img):
pred,idx,probs=learn.predict(img)
return dict(zip(categories, map(float,probs)))
# In[8]:
classify_image(im)
# In[9]:
#|export
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=examples)
intf.launch(inline=False)
# In[10]:
|