Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import fastai
 import fastai.vision
 import PIL
@@ -7,130 +6,121 @@ import matplotlib
 import numpy
 import pandas
 from fastai.vision.all import *
-#
-# create class
+
+# Crear la clase
 class ADA_SKIN(object):
-  #
-  # initialize the object
-  def __init__(self, name="Wallaby",verbose=True,*args, **kwargs):
+  
+  # Inicializar el objeto
+  def __init__(self, name="Wallaby", verbose=True, *args, **kwargs):
     super(ADA_SKIN, self).__init__(*args, **kwargs)
     self.author = "Jey"
     self.name = name
-    if (verbose):
+    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._pp("Hola desde la clase", str(self.__class__) + " Clase: " + str(self.__class__.__name__))
+      self._pp("Nombre del código", self.name)
+      self._pp("Autor", 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 = '<h3>Imágenes de ejemplo: (de izquierda a derecha)</h3><ol>'
+    self.article += '<li>Enfermedad de Bowen (AKIEC)</li>'
+    self.article += '<li>Carcinoma de células basales</li>'
+    self.article += '<li>Lesiones benignas similares a queratosis</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.article += '<li>Lunares melanocíticos</li>'
+    self.article += '<li>Carcinoma de células escamosas</li>'   
+    self.article += '<li>Lesiones vasculares</li>' 
+    self.article += '<li>Benigno</li>' 
+    self.article += '<li>Benigno 2</li></ol>'         
+    self.article += '<h3>Resultado del entrenamiento:</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"
+    self.title = "Diagnóstico de Cáncer de Piel"
     return
-  # 
-  # pretty print output name-value line
+  
+  # Imprimir de manera legible el nombre y valor de una línea
   def _pp(self, a, b):
     print("%34s : %s" % (str(a), str(b)))
     return
-  #
-  # pretty print the header or footer lines
+  
+  # Imprimir la línea de encabezado o pie de página
   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))
+  
+  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),
+      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),
+      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),
+      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),
+      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")
+    
+    t = str(ti[0]) + ": " + str(numpy.round(df_pred.head(1).pred.values[0] * 100, 2)) + "% de certeza"
+    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"
+    pic[0].legend(ti, loc="lower right", title="Cáncer de Piel: 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) + "% de certeza"
     else:
-      t2 = str(ti2[1]) + ": " + str(k1) + "% Certainty"
-    pic[1].set_title(t2,fontsize=14.0, fontweight="bold")
+      t2 = str(ti2[1]) + ": " + str(k1) + "% de certeza"
+    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)
-    #
+    pic[1].legend(ti2, loc="lower right", title="Cáncer de Piel:")
+    
     canvas.tight_layout()
     return canvas
-  #
-  def predict_donut(self,img):
-    d = self._predict_image(img,self.categories)
+  
+  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)
+    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)
+    
+    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"], 
@@ -138,4 +128,6 @@ intf = gradio.Interface(fn=maxi.predict_donut,
   title=maxi.title,
   live=True,
   article=maxi.article)
-intf.launch(inline=False,share=True)
+
+intf.launch(inline=False, share=True)
+