opening with .name

app.py

@@ -118,10 +118,15 @@ def predict_and_analyze(model_name, num_channels, dim, image):
     using
     
     m,n,r = X.shape
-    arr = np.column_stack((np.repeat(np.arange(c),w), 
-                               X.reshape(c*w,-1)))
-    df = pd.DataFrame(arr)
+    X = np.column_stack((np.repeat(np.arange(C), W), 
+                               X.reshape(C * W, -1)))
+    df = pd.DataFrame(X)
     
+    then get the image back with 
+    
+    X = df.values
+    X = X[:, 1:]
+    X = X.reshape((C, W, W))
     
     image = 2d numpy array in shape (C, W*W)
     i.e. take a C,W,W array and reshape into (C, W*W)
@@ -137,7 +142,7 @@ def predict_and_analyze(model_name, num_channels, dim, image):
     #     im = f.readlines()
     # image = np.frombuffer(image)
     
-    image = np.load(image, allow_pickle=True)
+    image = np.load(image.name, allow_pickle=True)
     
     image = image.reshape((num_channels, W, W))