Upload app.py

app.py

@@ -8,54 +8,92 @@ import numpy
 import pandas
 from fastai.vision.all import *
 #
-learn = fastai.learner.load_learner('ada.pkl')
-categories = learn.dls.vocab
-def _predict_image(img):
-  pred,idx,probs = learn.predict(img)
-  return dict(zip(categories, map(float,probs)))
-image = gradio.inputs.Image(shape=(192, 192))
-label = gradio.outputs.Label()
-examples = ['dog1.jpg','dog2.jpg','dog3.jpg','dog4.jpg','dog5.png','dog6.jpg', 'dog7.jpg','duc.jpg']
-# from fancy up below (but don't do the canvas.show())
-def _draw_pred(df_pred):
-  canvas, pic = matplotlib.pyplot.subplots(1,1, figsize=(6,6))
-  ti = df_pred["breeds"].head(5).values
-  df_pred["pred"].head(5).plot(ax=pic,kind="pie",figsize=(6,6),
-    cmap="Set2",labels=ti)
-  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')
+# 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)
+    if (verbose):
+      self.author = "Duc Haba"
+      self.name = name
+      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>Information:</h3><ul><li>'
+    self.article += 'Author: 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></div>'
+    self.examples = ['dog1.jpg','dog2.jpg','dog3.jpg','dog4.jpg','dog5.png','dog6.jpg', 'dog7.jpg','duc.jpg']
+    return
+  #
+  # pretty print output name-value line
+  def _pp(self, a, b):
+    print("%34s : %s" % (str(a), str(b)))
+    return
   #
-  # 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)
+  # 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)))
   #
-  canvas.legend(ti, loc="lower right",title="120 Dog Breeds: Top 5")
+  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
   #
-  canvas.tight_layout()
-  return canvas
-def predict_donut(img):
-  d = _predict_image(img)
-  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 = _draw_pred(df)
-  return canvas
-art = '<br><ul><li>'
-art += 'Author: Duc Haba, 2022.</li>'
-art += '<li>https://linkedin.com/in/duchaba</li>'
-art += '<li>The training dataset is from the Data Scientist at Department of Health '
-art += 'and Social Care London, England, United Kingdom.</li>'
-art += '<li>https://www.kaggle.com/datasets/amandam1/120-dog-breeds-breed-classification</li>'
-art += '</ul>'
-intf = gradio.Interface(fn=predict_donut, 
-  inputs=image, 
+  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=examples,
+  examples=maxi.examples,
   title="120 Dog Breeds Prediction",
   live=True,
-  article=art)
+  article=maxi.article)
 intf.launch(inline=False,share=True)