Update app.py

app.py

@@ -1,39 +1,7 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from tensorflow.keras.datasets import mnist
-from tensorflow import keras
-import keras.backend as K
-from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, Lambda, BatchNormalization, Dropout
- 
-(x_train, y_train), (x_test, y_test) = mnist.load_data()
- 
-x_train = x_train / 255
-x_test = x_test / 255
-
-y_train = keras.utils.to_categorical(y_train, 10)
-
-input_img = Input((28, 28))
-x = Flatten()(input_img)
-x = Dense(256, activation='relu')(x)
-x = Dense(128, activation='relu')(x)
-x = Dense(64, activation='relu')(x)
-Classif = Dense(10, activation='softmax')(x)
-
-model = keras.Model(input_img, Classif)
-
-model.compile(optimizer='adam', loss='categorical_crossentropy')
-
-model.fit(x_train, y_train, epochs=5, batch_size=30, shuffle=True)
-
-
-
-
 import gradio as gr
 import numpy as np
 
-from huggingface_hub import from_pretrained_keras
-
-model = from_pretrained_keras("ISYS/MyNewModel")
+model = keras.models.load_model('my_model')
 
 def greet(img):
     img = np.expand_dims(img, axis=0)