Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+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()
+
+for i in range(9):
+	# define subplot
+	plt.subplot(330 + 1 + i)
+	# plot raw pixel data
+	plt.imshow(training_images[i])
+	
+training_images  = training_images / 255.0
+test_images = test_images / 255.0
+
+
+from tensorflow.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(predict_image, inputs="sketchpad", outputs="label")
+iface.launch(debug='True')