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import gradio as gr
import tensorflow as tf
# Create a Gradio App using Blocks
with gr.Blocks() as demo:
gr.Markdown(
"""
# AI/ML Playground
"""
)
with gr.Accordion("Click for Instructions:"):
gr.Markdown(
"""
* Train/Eval will setup, train, and evaluate the base model
""")
def modelTraining():
print("TensorFlow version:", tf.__version__)
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
predictions = model(x_train[:1]).numpy()
print(predictions)
tf.nn.softmax(predictions).numpy()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
loss_fn(y_train[:1], predictions).numpy()
model.compile(optimizer='adam',
loss=loss_fn,
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test, verbose=2)
return "done"
# Creates the Gradio interface objects
with gr.Row():
with gr.Column(scale=1):
submit_btn = gr.Button(value="Train/Eval")
with gr.Column(scale=2):
model_data = gr.Text(label="Model Results", interactive=False)
submit_btn.click(modelTraining, [], model_data)
# creates a local web server
# if share=True creates a public
# demo on huggingface.co
demo.launch(share=False) |