Update app.py

app.py

@@ -1,133 +1,138 @@
 import fastai
-import fastai.vision
 import PIL
-import gradio
-import matplotlib
-import numpy
-import pandas
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
 from fastai.vision.all import *
 
 # Crear la clase
 class ADA_SKIN(object):
   
-  # 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:
-      self._ph()
-      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>Predice las siguientes patologias en piel</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>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></li></ol>'         
-    self.article += '<h3>                                  Prueba Jey(2023)</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 = "Predicción Cáncer de Piel prueba "
-    return
-  
-  # Imprimir de manera legible el nombre y valor de una línea
-  def _pp(self, a, b):
-    print("%34s : %s" % (str(a), str(b)))
-    return
-  
-  # 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))
-    ti = df_pred["vocab"].head(3).values
-    ti2 = df2["vocab"].head(2).values
-    
-    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)) + "% de predicción"
-    pic[0].set_title(t, fontsize=14.0, fontweight="bold")
-    pic[0].axis('off')
-    pic[0].legend(ti, loc="lower right", title="Cáncer de Piel: ")
-    
-    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 predicción"
-    else:
-      t2 = str(ti2[1]) + ": " + str(k1) + "% de predicción"
-    pic[1].set_title(t2, fontsize=14.0, fontweight="bold")
-    pic[1].axis('off')
-    pic[1].legend(ti2, loc="lower right", title="Prediccíon Cáncer de Piel:")
-    
-    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
+    # 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:
+            self._ph()
+            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>Predice las siguientes patologias en piel</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>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></li></ol>'         
+        self.article += '<h3>                                  Prueba Jey(2023)</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 = "Predicción Cáncer de Piel prueba "
+        return
 
+    # Imprimir de manera legible el nombre y valor de una línea
+    def _pp(self, a, b):
+        print("%34s : %s" % (str(a), str(b)))
+        return
+
+    # 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):
+        fig, pic = plt.subplots(1, 2, figsize=(12, 6))
+        ti = df_pred["vocab"].head(3).values
+        ti2 = df2["vocab"].head(2).values
+        
+        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(np.round(df_pred.head(1).pred.values[0] * 100, 2)) + "% de predicción"
+        pic[0].set_title(t, fontsize=14.0, fontweight="bold")
+        pic[0].axis('off')
+        pic[0].legend(ti, loc="lower right", title="Cáncer de Piel: ")
+        
+        k0 = np.round(df2.head(1).pred.values[0] * 100, 2)
+        k1 = np.round(df2.tail(1).pred.values[0] * 100, 2)
+        if k0 > k1:
+            t2 = str(ti2[0]) + ": " + str(k0) + "% de predicción"
+        else:
+            t2 = str(ti2[1]) + ": " + str(k1) + "% de predicción"
+        pic[1].set_title(t2, fontsize=14.0, fontweight="bold")
+        pic[1].axis('off')
+        pic[1].legend(ti2, loc="lower right", title="Predicción Cáncer de Piel:")
+        
+        fig.tight_layout()
+        return fig
+
+    def predict_donut(self, img):
+        d = self._predict_image(img, self.categories)
+        df = pd.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 = pd.DataFrame(d2, index=[0])
+        df2 = df2.transpose().reset_index()
+        df2.columns = ["vocab", "pred"]
+        
+        fig = self._draw_pred(df, df2)
+        return fig
+
+# Inicializar el objeto ADA_SKIN
 maxi = ADA_SKIN(verbose=False)
 
+# Cargar modelos
 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)
+# Crear la interfaz de Gradio
+hf_image = gr.inputs.Image(type='pil', shape=(192, 192))
+hf_plot = gr.outputs.Plot()
+
+intf = gr.Interface(fn=maxi.predict_donut, 
+                    inputs=hf_image, 
+                    outputs=hf_plot, 
+                    examples=maxi.examples,
+                    title=maxi.title,
+                    live=True,
+                    article=maxi.article)
+
+# Lanzar la interfaz
+intf.launch(sh
 
-intf.launch(inline=False, share=True)