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import gradio as gr
from load_model import load_model
import matplotlib.pyplot as plt
from tensorflow.keras import layers
from sklearn.datasets import make_moons
import matplotlib.pyplot as plt
import numpy as np
model = load_model()
# Load the Data
data = make_moons(3000, noise=0.05)[0].astype("float32")
norm = layers.experimental.preprocessing.Normalization()
norm.adapt(data)
normalized_data = norm(data)
z, _ = model(normalized_data)
demo = gr.Blocks()
with demo:
gr.Markdown("""# Density estimation using Real NVP <br>
This demo shows a toy example of using Real NVP (real-valued non-volume preserving transformations)
from this [example](https://keras.io/examples/generative/real_nvp/). Below we have two tabs. The first, Inference, shows
our mapping from a data distribution (moons) to a latent space with a known distribution (Gaussian). Click the button to see how a data point from our distribution maps
to our latent space. Our second tab allows you to generate a sample from our latent space, and view the generated data space that is associated with it.
Full credits for this model & example
go to <br>[Mandolini Giorgio Maria](https://www.linkedin.com/in/giorgio-maria-mandolini-a2a1b71b4/),
[Sanna Daniele](https://www.linkedin.com/in/daniele-sanna-338629bb/),
and [Zannini Quirini Giorgio](https://www.linkedin.com/in/giorgio-zannini-quirini-16ab181a0/).<br>
Demo by [Brenden Connors](https://www.linkedin.com/in/brenden-connors-6a0512195).""")
with gr.Tabs():
with gr.TabItem('Inference'):
button = gr.Button(value='Infer Sample Point')
with gr.Row():
fig = plt.figure()
plt.scatter(normalized_data[:, 0], normalized_data[:, 1], color="r")
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.title('Inference Data Space')
fig2 = plt.figure()
plt.scatter(z[:, 0], z[:, 1], color="r")
plt.xlim([-3.5, 4])
plt.ylim([-3.5, 4])
plt.title('Inference Latent Space')
data_space = gr.Plot(value = fig)
latent_space = gr.Plot(value = fig2)
with gr.TabItem('Generation'):
button_generate = gr.Button('Generate')
with gr.Row():
fig3 = plt.figure()
fig4 = plt.figure()
generated_lspace = gr.Plot(fig3)
generated_dspace = gr.Plot(fig4)
def inference_sample():
idx = np.random.choice(normalized_data.shape[0])
new_fig1 = plt.figure()
plt.scatter(normalized_data[:, 0], normalized_data[:, 1], color="r")
plt.scatter(normalized_data[idx, 0], normalized_data[idx, 1], color="b")
plt.title('Inference Data Space')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
output, _ = model(np.array(normalized_data[idx, :]).reshape((1, 2)))
new_fig2 = plt.figure()
plt.scatter(z[:, 0], z[:, 1], color="r")
plt.scatter(output[0,0] , output[0,1], color="b")
plt.xlim([-3.5, 4])
plt.ylim([-3.5, 4])
plt.title('Inference Latent Space')
return new_fig1, new_fig2
def generate():
samples = model.distribution.sample(3000)
x, _ = model.predict(samples)
new_fig1=plt.figure()
plt.scatter(samples[:,0], samples[:,1])
plt.title('Generated Latent Space')
plt.xlim([-3.5, 4])
plt.ylim([-3.5, 4])
new_fig2=plt.figure()
plt.scatter(x[:,0], x[:,1])
plt.title('Generated Data Space')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
return new_fig1, new_fig2
button.click(inference_sample, inputs=[], outputs=[data_space, latent_space])
button_generate.click(generate, inputs=[], outputs=[generated_lspace, generated_dspace])
demo.launch() |