Update app.py

app.py

@@ -1,25 +1,21 @@
 # For neural networks
 import keras
-# For train-test splits
-import sklearn.model_selection
 # For random calculations
 import numpy
-# For help with saving and opening things
-import os
 
 # Disable eager execution because its bad
 from tensorflow.python.framework.ops import disable_eager_execution
 disable_eager_execution()
 
 # Start a session for checking calculations and stuff
-import tensorflow as tf
-sess = tf.compat.v1.Session()
+#  import tensorflow as tf
+# sess = tf.compat.v1.Session()
 
-from keras import backend as K
-K.set_session(sess)
+# from keras import backend as K
+# K.set_session(sess)
 
 # Do you want it loud? 
-VERBOSE = 1
+# VERBOSE = 1
 
 # This function loads a fuckton of data
 def load_data():
@@ -161,20 +157,15 @@ class Network(object):
       df_pred = pandas.DataFrame(other_data_input.transpose()).rename(columns={0: "Surge", 1: "Heave", 2: "Pitch"})
       table = pandas.concat([fd, df_pred], axis=1)
 
-      # return idx, other_data_input, true_output, predicted_output
       return table
 
-# forward_net = Network("16forward_structure.json", "16forward_weights.h5")
-# inverse_net = Network("16inverse_structure.json", "16inverse_weights.h5")
-
 
 
 import plotly.graph_objects as go
-import numpy as np
 
 
 def plotly_fig(values):
-    X, Y, Z = np.mgrid[0:1:32j, 0:1:32j, 0:1:32j]
+    X, Y, Z = numpy.mgrid[0:1:32j, 0:1:32j, 0:1:32j]
     fig = go.Figure(data=go.Volume(
         x=X.flatten(),
         y=Y.flatten(),
@@ -234,7 +225,7 @@ with gradio.Blocks() as demo:
                 gradio.HTML("Christopher McComb, Carnegie Mellon University")
                 gradio.Markdown("Additive manufacturing is advantageous for producing lightweight components while maintaining function and form. This ability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be necessary to use multiple lattice cell types, also known as multi-lattice structures. In such structures, abrupt transitions between geometries may cause stress concentrations, making the boundary a primary failure point; thus, transition regions should be created between each lattice cell type. Although computational approaches have been proposed, smooth transition regions are still difficult to intuit and design, especially between lattices of drastically different geometries. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells. In particular, the work focuses on identifying the relationships that exist within the latent space produced by the variational autoencoder. Through computational experimentation, it was found that the smoothness of transition regions was higher when the endpoints were located closer together in the latent space.")
             with gradio.Column():
-                download = gradio.HTML("<a href=\"M169970.pdf\" style=\"width: 60%; display: block; margin: auto;\"><img src=\"coverpage.png\"></a>")
+                download = gradio.HTML("<a href=\"https://huggingface.co/spaces/cmudrc/wecnet/resolve/main/McComb2019_Chapter_TowardTheRapidDesignOfEngineer.pdf\" style=\"width: 60%; display: block; margin: auto;\"><img src=\"https://huggingface.co/spaces/cmudrc/wecnet/resolve/main/coverpage.png\"></a>")
     
     with gradio.Tab("Analysis"):
         with gradio.Tab("Spectrum from Dataset"):