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# | |
import fastai | |
import fastai.vision | |
import PIL | |
import gradio | |
import matplotlib | |
import numpy | |
import pandas | |
from fastai.vision.all import * | |
# | |
# create class | |
class ADA_DOGS(object): | |
# | |
# initialize the object | |
def __init__(self, name="Wallaby",verbose=True,*args, **kwargs): | |
super(ADA_DOGS, self).__init__(*args, **kwargs) | |
self.author = "Duc Haba" | |
self.name = name | |
if (verbose): | |
self._ph() | |
self._pp("Hello from class", str(self.__class__) + " Class: " + str(self.__class__.__name__)) | |
self._pp("Code name", self.name) | |
self._pp("Author is", self.author) | |
self._ph() | |
# | |
self.article = '<div><h3>Citation:</h3><ul><li>' | |
self.article += 'Author/Dev: Duc Haba, 2022.</li>' | |
self.article += '<li>https://linkedin.com/in/duchaba</li>' | |
self.article += '<li>The training dataset is from the Data Scientist at Department of Health ' | |
self.article += 'and Social Care London, England, United Kingdom.</li>' | |
self.article += '<li>https://www.kaggle.com/datasets/amandam1/120-dog-breeds-breed-classification</li>' | |
self.article += '</ul>' | |
self.article += '<h3>Train Result:</h3><ul>' | |
self.article += '<li>F1-Score, Precision, and Recall -- Take the output from method sklearn.metrics.classification_report(), import to Pandas Data Fame, sorted, and graph it.</li>' | |
self.article += '<li><img src="file/ada_f1.png" alt="F1-Score, Precision, and Recall Graph" width="640"</li>' | |
self.article += '</ul>' | |
self.article += '<h3>Dev Stack:</h3><ul>' | |
self.article += '<li>Jupyter Notebook, Python, Pandas, Matplotlib, Sklearn</li>' | |
self.article += '<li>Fast.ai, PyTorch</li>' | |
self.article += '</ul>' | |
self.article += '<h3>Licenses:</h3><ul>' | |
self.article += '<li>GNU GPL 3.0, https://www.gnu.org/licenses/gpl-3.0.txt</li>' | |
self.article += '</ul></div>' | |
self.examples = ['dog1.jpg','dog2.jpg','dog3.jpg','dog4.jpg','dog5.png','dog6.jpg', 'dog7.jpg','duc.jpg'] | |
self.title = "120 Dog Breeds Prediction" | |
return | |
# | |
# pretty print output name-value line | |
def _pp(self, a, b): | |
print("%34s : %s" % (str(a), str(b))) | |
return | |
# | |
# pretty print the header or footer lines | |
def _ph(self): | |
print("-" * 34, ":", "-" * 34) | |
return | |
# | |
def _predict_image(self,img,cat): | |
pred,idx,probs = learn.predict(img) | |
return dict(zip(cat, map(float,probs))) | |
# | |
def _draw_pred(self,df_pred): | |
canvas, pic = matplotlib.pyplot.subplots(1,1, figsize=(6,6)) | |
ti = df_pred["breeds"].head(5).values | |
# special case | |
#if (matplotlib.__version__) >= "3.5.2": | |
try: | |
df_pred["pred"].head(5).plot(ax=pic,kind="pie",figsize=(6,6), | |
cmap="Set2",labels=ti, explode=(0.02,0,0,0,0.), | |
normalize=False) | |
except: | |
df_pred["pred"].head(5).plot(ax=pic,kind="pie",figsize=(6,6), | |
cmap="Set2",labels=ti, explode=(0.02,0,0,0,0.)) | |
t = str(ti[0]) + ": " + str(numpy.round(df_pred.head(1).pred.values[0]*100, 2)) + "% Certainty" | |
pic.set_title(t,fontsize=14.0, fontweight="bold") | |
pic.axis('off') | |
# | |
# draw circle | |
centre_circle = matplotlib.pyplot.Circle((0, 0), 0.6, fc='white') | |
canvas = matplotlib.pyplot.gcf() | |
# Adding Circle in Pie chart | |
canvas.gca().add_artist(centre_circle) | |
# | |
canvas.legend(ti, loc="lower right",title="120 Dog Breeds: Top 5") | |
# | |
canvas.tight_layout() | |
return canvas | |
# | |
def predict_donut(self,img): | |
d = self._predict_image(img,self.categories) | |
df = pandas.DataFrame(d, index=[0]) | |
df = df.transpose().reset_index() | |
df.columns = ["breeds", "pred"] | |
df.sort_values("pred", inplace=True,ascending=False, ignore_index=True) | |
canvas = self._draw_pred(df) | |
return canvas | |
# | |
maxi = ADA_DOGS(verbose=False) | |
# | |
learn = fastai.learner.load_learner('ada.pkl') | |
maxi.categories = learn.dls.vocab | |
hf_image = gradio.inputs.Image(shape=(192, 192)) | |
hf_label = gradio.outputs.Label() | |
intf = gradio.Interface(fn=maxi.predict_donut, | |
inputs=hf_image, | |
outputs=["plot"], | |
examples=maxi.examples, | |
title=maxi.title, | |
live=True, | |
article=maxi.article) | |
intf.launch(inline=False,share=True) |