Update app.py

app.py

@@ -1,14 +1,7 @@
 import gradio as gr
 import pandas as pd
 import numpy as np 
-#import os
-#from tqdm import tqdm
-#import tensorflow as tf
-#from tensorflow import keras
-#from keras.utils import np_utils
-#from keras.preprocessing import image
-#from keras.preprocessing.image import ImageDataGenerator
-#import matplotlib.pyplot as plt
+
 from keras.models import model_from_json
 from keras.preprocessing import image
 from keras.applications.vgg16 import VGG16, preprocess_input
@@ -19,8 +12,7 @@ file.close()
 loaded_model = model_from_json(model_json2)
 loaded_model.load_weights("womanlife.h5")
 
-#new_model = tf.keras.models.load_model('modelo_entrenado.h5')
-objects = ('There is a benign nodule', 'Normal Breast', 'There is a malignant nodule')
+objects = ('There is a benign nodule', 'There is a malignant nodule', 'Normal Breast')
 y_pos = np.arange(len(objects))
 
 
@@ -32,13 +24,6 @@ def predict_image(pic):
     x = np.expand_dims(x, axis=0)
     x = preprocess_input(x)
 
-    #img = image.load_img(pic, grayscale=True, target_size=(48, 48))
-    #x = image.img_to_array(img)
-    
-    #x = np.expand_dims(x, axis = 0)
-    
-    #x /= 255
-
     custom = loaded_model.predict(x)
 
     m=0.000000000000000000001