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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_SKIN(object): | |
| # | |
| # initialize the object | |
| def __init__(self, name="Wallaby",verbose=True,*args, **kwargs): | |
| super(ADA_SKIN, self).__init__(*args, **kwargs) | |
| self.author = "Jey" | |
| 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 += '<h3>Example Images: (left to right)</h3><ol>' | |
| self.article += '<li>Bowen Disease (AKIEC)</li>' | |
| self.article += '<li>Basal Cell Carcinoma</li>' | |
| self.article += '<li>Benign Keratosis-like Lesions</li>' | |
| self.article += '<li>Dermatofibroma</li>' | |
| self.article += '<li>Melanoma</li>' | |
| self.article += '<li>Melanocytic Nevi</li>' | |
| self.article += '<li>Squamous Cell Carcinoma</li>' | |
| self.article += '<li>Vascular Lesions</li>' | |
| self.article += '<li>Benign</li>' | |
| self.article += '<li>Benign 2</li></ol>' | |
| self.article += '<h3>Train Result:</h3><ul>' | |
| self.examples = ['akiec1.jpg','bcc1.jpg','bkl1.jpg','df1.jpg','mel1.jpg', | |
| 'nevi1.jpg','scc1.jpg','vl1.jpg','benign1.jpg','benign3.jpg'] | |
| self.title = "Skin Cancer Diagnose" | |
| 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 _predict_image2(self,img,cat): | |
| pred,idx,probs = learn2.predict(img) | |
| return dict(zip(cat, map(float,probs))) | |
| # | |
| def _draw_pred(self,df_pred, df2): | |
| canvas, pic = matplotlib.pyplot.subplots(1,2, figsize=(12,6)) | |
| ti = df_pred["vocab"].head(3).values | |
| ti2 = df2["vocab"].head(2).values | |
| # special case | |
| #if (matplotlib.__version__) >= "3.5.2": | |
| try: | |
| df_pred["pred"].head(3).plot(ax=pic[0],kind="pie", | |
| cmap="Set2",labels=ti, explode=(0.02,0,0), | |
| wedgeprops=dict(width=.4), | |
| normalize=False) | |
| df2["pred"].head(2).plot(ax=pic[1],kind="pie", | |
| colors=["cornflowerblue","darkorange"],labels=ti2, explode=(0.02,0), | |
| wedgeprops=dict(width=.4), | |
| normalize=False) | |
| except: | |
| df_pred["pred"].head(3).plot(ax=pic[0],kind="pie", | |
| cmap="Set2",labels=ti, explode=(0.02,0,0), | |
| wedgeprops=dict(width=.4)) | |
| df2["pred"].head(2).plot(ax=pic[1],kind="pie", | |
| colors=["cornflowerblue","darkorange"],labels=ti2, explode=(0.02,0), | |
| wedgeprops=dict(width=.4)) | |
| t = str(ti[0]) + ": " + str(numpy.round(df_pred.head(1).pred.values[0]*100, 2)) + "% Certainty" | |
| pic[0].set_title(t,fontsize=14.0, fontweight="bold") | |
| pic[0].axis('off') | |
| pic[0].legend(ti, loc="lower right",title="Skin Cancers: Top 3") | |
| # | |
| k0 = numpy.round(df2.head(1).pred.values[0]*100, 2) | |
| k1 = numpy.round(df2.tail(1).pred.values[0]*100, 2) | |
| if (k0 > k1): | |
| t2 = str(ti2[0]) + ": " + str(k0) + "% Certainty" | |
| else: | |
| t2 = str(ti2[1]) + ": " + str(k1) + "% Certainty" | |
| pic[1].set_title(t2,fontsize=14.0, fontweight="bold") | |
| pic[1].axis('off') | |
| pic[1].legend(ti2, loc="lower right",title="Skin Cancers:") | |
| # | |
| # # draw circle | |
| # centre_circle = matplotlib.pyplot.Circle((0, 0), 0.6, fc='white') | |
| # p = matplotlib.pyplot.gcf() | |
| # # Adding Circle in Pie chart | |
| # p.gca().add_artist(centre_circle) | |
| # | |
| #p=plt.gcf() | |
| #p.gca().add_artist(my_circle) | |
| # | |
| 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 = ["vocab", "pred"] | |
| df.sort_values("pred", inplace=True,ascending=False, ignore_index=True) | |
| # | |
| d2 = self._predict_image2(img,self.categories2) | |
| df2 = pandas.DataFrame(d2, index=[0]) | |
| df2 = df2.transpose().reset_index() | |
| df2.columns = ["vocab", "pred"] | |
| # | |
| canvas = self._draw_pred(df,df2) | |
| return canvas | |
| # | |
| maxi = ADA_SKIN(verbose=False) | |
| # | |
| learn = fastai.learner.load_learner('ada_learn_skin_norm2000.pkl') | |
| learn2 = fastai.learner.load_learner('ada_learn_malben.pkl') | |
| maxi.categories = learn.dls.vocab | |
| maxi.categories2 = learn2.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) |