Delete app.py

app.py

@@ -1,45 +0,0 @@
-import gradio as gr
-
-import tensorflow as tf
-import keras
-from matplotlib import pyplot as plt
-import numpy as np
-
-objects =  tf.keras.datasets.mnist
-(training_images, training_labels), (test_images, test_labels) = objects.load_data()
-
-training_images  = training_images / 255.0
-test_images = test_images / 255.0
-
-from keras.layers import Flatten, Dense
-model = tf.keras.models.Sequential([Flatten(input_shape=(28,28)), 
-                                    Dense(256, activation='relu'),
-                                    Dense(256, activation='relu'),
-                                    Dense(128, activation='relu'), 
-                                    Dense(10, activation=tf.nn.softmax)])
-
-model.compile(optimizer = 'adam',
-              loss = 'sparse_categorical_crossentropy',
-              metrics=['accuracy'])
-
-model.fit(training_images, training_labels, epochs=10)
-
-test=test_images[0].reshape(-1,28,28)
-pred=model.predict(test)
-print(pred)
-
-def predict_image(img):
-  img_3d=img.reshape(-1,28,28)
-  im_resize=img_3d/255.0
-  prediction=model.predict(im_resize)
-  pred=np.argmax(prediction)
-  return pred
-
-iface = gr.Interface(
-        fn= predict_image,
-        inputs= gr.Image(height=28, width=28, image_mode='L', sources='upload'),
-        outputs='label'
-        
-        )
-
-iface.launch(debug='True')