import fastai
import fastai.vision
import PIL
import gradio
import matplotlib
import numpy
import pandas
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

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)