Update app.py

app.py

@@ -1,54 +0,0 @@
-import gradio as gr
-from gradio import Interface
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import datasets, layers, models
-import numpy as np
-
-(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()
-
-X_train = np.concatenate((X_train, X_test))
-y_train = np.concatenate((y_train, y_test))
-
-X_train = X_train / 255
-X_test = X_test / 255
-
-data_augmentation = keras.Sequential([
-    tf.keras.layers.experimental.preprocessing.RandomRotation(0.2, input_shape=(28, 28, 1)),
-])
-
-model = models.Sequential([   
-    data_augmentation,
-                                                   
-    #cnn
-    layers.Conv2D(filters=32, kernel_size=(3,3), padding='same', activation='relu'),
-    layers.MaxPooling2D((2,2)),
-    layers.Conv2D(filters=32, kernel_size=(3,3), padding='same', activation='relu'),
-    layers.MaxPooling2D((2,2)),
-
-    #dense
-
-    layers.Flatten(),
-    layers.Dense(32, activation='relu'),
-    layers.Dense(10, activation='softmax'),
-
-])
-
-model.compile(optimizer='adam',
-            loss='sparse_categorical_crossentropy',
-            metrics=['accuracy'])
-
-model.fit(X_train, y_train, epochs=1)
-
-def predict_image(img):
-  img_3d = img.reshape(-1, 28,28)
-  img_scaled = img_3d/255
-  prediction = model.predict(img_scaled)
-  pred = np.argmax(prediction)
-
-  return pred.item()
-    
-
-iface = gr.Interface(predict_image, inputs='sketchpad', outputs='label', title='Digit Recognition Model By Debamrita Paul', description='Draw a single digit(0 to 9)')
-
-iface.launch()