Update app.py

app.py

@@ -24,28 +24,9 @@ def load_data():
   G = 32
 
   data = load_dataset("cmudrc/wave-energy", data_files=["data.zip"], split='train')
-  # curvey_curves = [numpy.array(x)/1000000 for x in data['curves']]
-  # round_geometry = [numpy.array(x) for x in data['geometry']]
-  # round_geometry = numpy.array(data['geometry'])
-  curvey_curves = None
-  round_geometry = None
   geometry = numpy.reshape(data['geometry'], (S*N, G*G*G))
   curves = numpy.reshape(data['curves'], (S*N, D*F))/1000000
-  # curves = load_dataset("cmudrc/wave-energy", data_files="curves.zip", split='train').to_pandas().drop(labels="index", axis=1)
-  # geometry = load_dataset("cmudrc/wave-energy", data_files="geometry.zip", split='train').to_pandas().drop(labels="index", axis=1)
-
-  # Open all the files we downloaded at the beginning and take out hte good bits
-  # curves = data.iloc[:, [i for i in range(1, 3*64+1)]]
-  # geometry = data.iloc[:, [i for i in range(1 + 3*64, 1 + 3*64 + 32**3)]]
-
-  # flattened_curves = curves.values / 1000000
-  # curvey_curves = [c.reshape([D, F])/1000000 for c in curves.values]
-
-  # flattened_geometry = geometry.values
-  # round_geometry = [g.reshape([G, G, G]) for g in geometry.values]
-
-  # Return good bits to user
-  return curvey_curves, round_geometry, S, N, D, F, G, curves, geometry
+  return None, None, S, N, D, F, G, curves, geometry
 
 # Disable eager execution because its bad
 from tensorflow.python.framework.ops import disable_eager_execution