blog article demo

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing.image import img_to_array
+from keras.datasets import mnist
+
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+X_train = X_train/255.0
+X_test = X_test/255.0
+
+model = tf.keras.models.Sequential([
+    tf.keras.layers.
+    tf.keras.layers.Flatten(input_shape=(28, 28)),
+    tf.keras.layers.Dense(units=128, activation='relu'),
+    tf.keras.layers.Dropout(0.5),
+    tf.keras.layers.Dense(units=10,activation="softmax")
+])
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy']
+)
+history = model.fit(X_train,y_train,epochs=10,validation_data=(X_test,y_test))
+
+def predict(img):
+    x = img_to_array(img)
+    x = np.expand_dims(x,axis=0)
+    target = model.predict(x)
+    target = np.argmax(target)
+    return target
+
+demo = gr.Interface(fn=predict, 
+             inputs="sketchpad",
+             outputs="number",
+             live=True, streaming=True)
+
+demo.launch(share=True)