Spaces:
Runtime error
Runtime error
File size: 2,264 Bytes
ddfcbfd fea1e17 b1334ad 1b8bf98 bc874f6 1b8bf98 bc874f6 1b8bf98 d27ee2d 1b8bf98 a959427 1b8bf98 2c7bae6 1b8bf98 d27ee2d 2c7bae6 39f82d8 d27ee2d 39f82d8 d27ee2d 39f82d8 1b8bf98 d27ee2d 39f82d8 d27ee2d 1b8bf98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
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(img):
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()
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)
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)]
print "Test accuracy: ", test_acc
# Define any necessary preprocessing steps for the image input here
probability_model = tf.keras.Sequential([model,
tf.keras.layers.Softmax()])
# Make a prediction using the model
prediction = probability_model.predict(img)
# Postprocess the prediction and return it
return prediction
# Creates the Gradio interface objects
with gr.Row():
with gr.Column(scale=2):
image_data = gr.Image(label="Upload Image", type="numpy")
with gr.Column(scale=1):
model_performance = gr.Text(label="Model Performance", interactive=False)
model_prediction = gr.Text(label="Model Prediction", interactive=False)
image_data.change(modelTraining, image_data, model_prediction)
# creates a local web server
# if share=True creates a public
# demo on huggingface.co
demo.launch(share=False) |