Modifying the figures to be published

app.py

@@ -318,8 +318,24 @@ for column in range(latent_dimensionality):
 latent_matrix = np.array(latent_matrix).T  # Transposes the matrix so that each row can be easily indexed
 ########################################################################################################################
 # Plotting the Interpolation in 2D Using Chosen Points
-if st.button("Generate Interpolation:"):
+if st.button("Generate Interpolation"):
     plt.figure(2)
+
+    linear_interp_latent = np.linspace(latent_point_1, latent_point_2, num_interp)
+    print(len(linear_interp_latent))
+
+    linear_predicted_interps = []
+    figure = np.zeros((28 * num_interp, 28))
+    for i in range(num_interp):
+        generated_image = decoder_model_boxes.predict(np.array([linear_interp_latent[i]]))[0]
+        figure[i * 28:(i + 1) * 28, 0:28, ] = generated_image[:, :, -1]
+        linear_predicted_interps.append(generated_image[:, :, -1])
+
+    plt.figure(figsize=(15, 15))
+    # plt.imshow(figure, cmap='gray')
+    plt.figure(2)
+    st.pyplot(plt.figure(2))
+
     """
     plot_rows = 2
     plot_columns = num_interp + 2
@@ -342,19 +358,6 @@ if st.button("Generate Interpolation:"):
     # plt.title("Second Interpolation Point:\n" + str(box_shape_test[number_2]) + "\nPixel Density: " + str(
     #             box_density_test[number_2]) + "\nAdditional Pixels: " + str(additional_pixels_test[number_2]))  # + "\nPredicted Latent Point 2: " + str(latent_point_2)
     """
-    linear_interp_latent = np.linspace(latent_point_1, latent_point_2, num_interp)
-    print(len(linear_interp_latent))
-
-    linear_predicted_interps = []
-    figure = np.zeros((28 * num_interp, 28))
-    for i in range(num_interp):
-        generated_image = decoder_model_boxes.predict(np.array([linear_interp_latent[i]]))[0]
-        figure[i * 28:(i + 1) * 28, 0:28, ] = generated_image[:, :, -1]
-        linear_predicted_interps.append(generated_image[:, :, -1])
-
-    plt.figure(figsize=(15, 15))
-    plt.imshow(figure, cmap='gray')
-
     '''
     latent_matrix_2 = []  # This will contain the latent points of the interpolation
     for column in range(latent_dimensionality):
@@ -378,9 +381,7 @@ if st.button("Generate Interpolation:"):
             generated_image = generator_model.predict(np.array([mesh[i][j]]))[0]
             figure[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28, ] = generated_image[:, :, -1]
             mesh_predicted_interps.append(generated_image[:, :, -1])
-    
+
     plt.figure(figsize=(15, 15))
     plt.imshow(figure, cmap='gray')
-    '''
-    plt.figure(2)
-    st.pyplot(plt.figure(2))
+    '''