diff --git a/Aplicacion/Codigo/predict.py b/Aplicacion/Codigo/predict.py
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+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