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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><a target="_blank" href="https://linkedin.com/in/duchaba">https://linkedin.com/in/duchaba</a></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>Articles:</h3><ul>' | |
| self.article += '<li><a target="_blank" href="https://www.linkedin.com/pulse/120-dog-breeds-hugging-face-duc-haba/">"120 Dog Breeds on Hugging Face"</a> on LinkedIn, ' | |
| self.article += 'on <a target="_blank" href="https://duchaba.medium.com/120-dog-breeds-on-hugging-face-75288c7952d6">Medium.</a></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 = ['dog1a.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) |