Modifying the figures to be published

app.py

@@ -309,7 +309,7 @@ latent_point_2 = encoder_model_boxes.predict(number_2_expand)[0]
 latent_dimensionality = len(latent_point_1)  # define the dimensionality of the latent space
 ########################################################################################################################
 # Establish the Framework for a LINEAR Interpolation
-number_internal = 8  # the number of interpolations that the model will find between two points
+number_internal = int(interp_length)  # the number of interpolations that the model will find between two points
 num_interp = number_internal + 2  # the number of images to be pictured
 latent_matrix = []  # This will contain the latent points of the interpolation
 for column in range(latent_dimensionality):
@@ -342,7 +342,7 @@ 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, interp_length)
+    linear_interp_latent = np.linspace(latent_point_1, latent_point_2, num_interp)
     print(len(linear_interp_latent))
 
     linear_predicted_interps = []