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DogCatClassifier
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DogCatClassifier
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#!/usr/bin/env python
# coding: utf-8

# # Dog v Cat

# In[5]:


#/export
from fastai.vision.all import *
import gradio as gr

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


# In[6]:


im = PILImage.create('dog1.jpg')
im.thumbnail((192, 192))
im


# In[8]:


#/export
import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath

learn = load_learner('model.pkl')


# In[11]:


learn.predict(im)


# In[12]:


#/export
categories = ('Dog', 'Cat')

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


# In[13]:


classify_images(im)


# In[14]:


#/export
image = gr.inputs.Image(shape = (192,192))
label = gr.outputs.Label()
examples = ['dog1.jpg', 'cat1.jpg']

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


# ## export -

# In[28]:


import nbdev
nbdev.export.nb_export('dogs_vs_cats_test.ipynb', 'app')
print('Export successful')