Update main.py

main.py

@@ -3,36 +3,12 @@ import keras # for network
 import gradio # for interface
 import matplotlib.pyplot # for colormap
 import matplotlib.colors # for color conversion
+import huggingface_hub
 
-# Building the neural network
-# model1 = keras.models.Sequential()
-# model1.add(keras.layers.InputLayer(input_shape=(101, 636, 1)))
-# model1.add(keras.layers.Conv2D(4, (9, 9), activation='relu', padding='same', strides=1))
-# model1.add(keras.layers.Conv2D(4, (9, 9), activation='relu', padding='same'))
-# model1.add(keras.layers.Conv2D(8, (7, 7), activation='relu', padding='same', strides=1))
-# model1.add(keras.layers.Conv2D(8, (7, 7), activation='relu', padding='same'))
-# model1.add(keras.layers.Conv2D(16, (5, 5), activation='relu', padding='same'))
-# model1.add(keras.layers.Conv2D(16, (5, 5), activation='relu', padding='same', strides=1))
-# model1.add(keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same'))
-# model1.add(keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same', strides=1))
-# model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same'))
-# model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same', strides=1))
-# model1.add(keras.layers.UpSampling2D((1, 1)))
-# model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same'))
-# model1.add(keras.layers.UpSampling2D((1, 1)))
-# model1.add(keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same'))
-# model1.add(keras.layers.UpSampling2D((1, 1)))
-# model1.add(keras.layers.Conv2D(4, (7, 7), activation='tanh', padding='same'))
-# model1.add(keras.layers.UpSampling2D((1, 1)))
-# model1.add(keras.layers.Conv2D(3, (9, 9), activation='tanh', padding='same'))
-
-from huggingface_hub import from_pretrained_keras
-
-model1 = from_pretrained_keras("cmudrc/microstructure-colorization")
-
+model1 = huggingface_hub.from_pretrained_keras("cmudrc/microstructure-colorization")
 
 #Loading the weights in the architecture (The file should be stored in the same directory as the code)
-model1.load_weights('modelV13_500trained_1.h5')
+# model1.load_weights('modelV13_500trained_1.h5')
 
 # Get the color map by name:
 cm = matplotlib.pyplot.get_cmap('RdBu')