Update app.py

app.py

@@ -17,8 +17,14 @@ def load_data():
 
   from datasets import load_dataset
 
-  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)
+
+  data = load_dataset("cmudrc/wave-energy", data_files="data.zip")
+  curvey_curves = [numpy.array(x)/1000000 for x in data['curves']]
+  round_geometry = [numpy.array(x) for x in data['geometry']]
+  geometry = [x.flatten() for x in round_geometry]
+  curves = [x.flatten() for x in curvey_curves]
+  # 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)]]
@@ -30,13 +36,13 @@ def load_data():
   G = 32
 
   # flattened_curves = curves.values / 1000000
-  curvey_curves = [c.reshape([D, F])/1000000 for c in curves.values]
+  # 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]
+  # 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.values/1000000, geometry.values
+  return curvey_curves, round_geometry, S, N, D, F, G, curves, geometry
 
 # Disable eager execution because its bad
 from tensorflow.python.framework.ops import disable_eager_execution