Upload app.py

app.py

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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()