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# AUTOGENERATED! DO NOT EDIT! File to edit: antelopeInference.ipynb.
# %% auto 0
__all__ = ['learn', 'image', 'label', 'examples', 'intf', 'classify_image']
# %% antelopeInference.ipynb 3
#Imports
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
#hide
#[ -e /content ]
#pip install -Uqq fastbook
# import fastbook
# fastbook.setup_book()
#hide
# from fastbook import *
# from fastai.vision.widgets import *
# pip install fastai
# %% antelopeInference.ipynb 4
from fastai.vision.all import *
import gradio as gr
# %% antelopeInference.ipynb 8
learn = load_learner('antelopeClassifier.pkl')
categories = ('Eland', 'Greater Kudu', 'Hartebeest', 'Oryx', 'Defassa Waterbuck', 'Sitatunga', 'Impala ', 'The lesser Kudu', 'Grant’s Gazelle','Reedbuck','Uganda Kob','Forest duiker','Harvery’s red duiker', 'Blue duiker', 'Peter’s duiker','Black fronted duiker','Grey duiker','Oribi','Klipspringer','Guenther’s')
# %% antelopeInference.ipynb 26
def classify_image(img):
pred,idx,probs = learn.predict(img)
return dict(zip(categories, map(float,probs)))
# %% antelopeInference.ipynb 31
#create gradio interface
image = gr.inputs.Image(shape=(128,128))
label = gr.outputs.Label()
examples = ['antelopeA.jpeg', 'antelopeB.jpeg', 'antelopeC.jpeg']
title = "East African Antelope classifier"
description = "A deep learning based image classifier that tells you what breed of antelope is in a picture"
interpretation = "default"
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples, title=title, description=description, interpretation=interpretation)
intf.launch(inline=False)
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