diff --git a/Aplicacion/Codigo/predict.py b/Aplicacion/Codigo/predict.py
deleted file mode 100644
index 8ecb2552ff063fc174d0ceb080f1e163977d1888..0000000000000000000000000000000000000000
--- a/Aplicacion/Codigo/predict.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import tensorflow as tf
-from tensorflow import keras
-from keras.models import load_model
-from keras.preprocessing import image
-import numpy as np
-from PIL import Image
-
-# Display
-#from IPython.display import Image, display
-#import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-
-class ModeloCovid:
-
- #Constructor de clase, importa el modelo y guarda un diccionario con las etiquetas de clase
- def __init__(self):
- self.model = load_model("./modeloGrey9M.h5")
- self.num2label = {0:'COVID-19', 1:'NORMAL', 2:'Viral Pneumonia'}
- self.dims = self.model.input_shape[1:3]
-
- #CON UNA IMAGEN Y LAS DIMENSIONES LA LEE DE FORMA ADECUADA
- def _get_img_array(self, img_path, size):
- # `img` is a PIL image of size 299x299
- img = keras.preprocessing.image.load_img(img_path, target_size=size, color_mode="grayscale")
- # `array` is a float32 Numpy array of shape (299, 299, 3)
- array = keras.preprocessing.image.img_to_array(img)
- # We add a dimension to transform our array into a "batch"
- # of size (1, 299, 299, 3)
- array = np.expand_dims(array, axis=0)
- return array
-
- def _make_gradcam_heatmap(self,img_array, model, last_conv_layer_name, pred_index=None):
- # First, we create a model that maps the input image to the activations
- # of the last conv layer as well as the output predictions
- grad_model = tf.keras.models.Model(
- [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
- )
-
- # Then, we compute the gradient of the top predicted class for our input image
- # with respect to the activations of the last conv layer
- with tf.GradientTape() as tape:
- last_conv_layer_output, preds = grad_model(img_array)
- if pred_index is None:
- pred_index = tf.argmax(preds[0])
- class_channel = preds[:, pred_index]
-
- # This is the gradient of the output neuron (top predicted or chosen)
- # with regard to the output feature map of the last conv layer
- grads = tape.gradient(class_channel, last_conv_layer_output)
-
- # This is a vector where each entry is the mean intensity of the gradient
- # over a specific feature map channel
- pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
-
- # We multiply each channel in the feature map array
- # by "how important this channel is" with regard to the top predicted class
- # then sum all the channels to obtain the heatmap class activation
- last_conv_layer_output = last_conv_layer_output[0]
- heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
- heatmap = tf.squeeze(heatmap)
-
- # For visualization purpose, we will also normalize the heatmap between 0 & 1
- heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
- return heatmap.numpy()
-
-
- #A partir de una imagen retorna su clase mas problable tras la prediccion del modelo
- def predict(self,img_path):
- img = image.load_img(img_path,target_size=self.dims, color_mode="grayscale")
- img = np.asarray(img)/255.0
- img = np.expand_dims(img, axis=0)
- img = np.expand_dims(img, axis=3)
-
- output = self.model.predict(img)#Aqui esta el vector de probabilidades
- prediction = np.argmax(output, axis=1).astype(np.int)
-
- return self.num2label[prediction[0]],output
- def gradCam(self, img_path, cam_path="./static/GRADCAM_img/cam.jpg"):
- model_builder = keras.applications.xception.Xception
- preprocess_input = keras.applications.xception.preprocess_input
- decode_predictions = keras.applications.xception.decode_predictions
- last_conv_layer_name = "conv2d_9"
-
- img_array = preprocess_input(self._get_img_array(img_path, size=self.dims))
- # Remove last layer's softmax
- _model=self.model
- _model.layers[-1].activation = None
-
- heatmap = self._make_gradcam_heatmap(img_array, _model, last_conv_layer_name)
- superimposed_img = self._save_and_display_gradcam(img_path,heatmap,cam_path)
- return heatmap, superimposed_img
-
- def _save_and_display_gradcam(self,img_path, heatmap, cam_path, alpha=0.4):
- # Load the original image
-
- img = keras.preprocessing.image.load_img(img_path, color_mode="grayscale")
- img = keras.preprocessing.image.img_to_array(img)
-
- # Rescale heatmap to a range 0-255
- heatmap = np.uint8(255 * heatmap)
-
- # Use jet colormap to colorize heatmap
- jet = cm.get_cmap("jet")
-
- # Use RGB values of the colormap
- jet_colors = jet(np.arange(256))[:, :3]
- jet_heatmap = jet_colors[heatmap]
-
- # Create an image with RGB colorized heatmap
- jet_heatmap = keras.preprocessing.image.array_to_img(jet_heatmap)
- jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))
- jet_heatmap = keras.preprocessing.image.img_to_array(jet_heatmap)
-
- # Superimpose the heatmap on original image
- superimposed_img = jet_heatmap * alpha + img
- superimposed_img = keras.preprocessing.image.array_to_img(superimposed_img)
-
- # Save the superimposed image
- superimposed_img.save(cam_path)
-
- # Display Grad CAM
- return superimposed_img