Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-#import os
+import os
 import matplotlib.pyplot as plt
 from scipy.integrate import odeint
 import torch
@@ -107,7 +107,7 @@ class Echo(torchvision.datasets.VisionDataset):
         self.fnames, self.outcome = [], []
 
         if self.split == "EXTERNAL_TEST":
-            # self.fnames = sorted(os.listdir(self.external_test_location))
+            self.fnames = sorted(os.listdir(self.external_test_location))
         else:
             # Load video-level labels
             with open(f"{self.root}FileList.csv") as f:
@@ -119,7 +119,7 @@ class Echo(torchvision.datasets.VisionDataset):
 
             self.header = data.columns.tolist()
             self.fnames = data["FileName"].tolist()
-            # self.fnames = [fn + ".avi" for fn in self.fnames if os.path.splitext(fn)[1] == ""]  # Assume avi if no suffix
+            self.fnames = [fn + ".avi" for fn in self.fnames if os.path.splitext(fn)[1] == ""]  # Assume avi if no suffix
             self.outcome = data.values.tolist()
 
             # Check that files are present
@@ -162,11 +162,11 @@ class Echo(torchvision.datasets.VisionDataset):
     def __getitem__(self, index):
         # Find filename of video
         if self.split == "EXTERNAL_TEST":
-            # video = os.path.join(self.external_test_location, self.fnames[index])
+            video = os.path.join(self.external_test_location, self.fnames[index])
         elif self.split == "CLINICAL_TEST":
-            # video = os.path.join(self.root, "ProcessedStrainStudyA4c", self.fnames[index])
+            video = os.path.join(self.root, "ProcessedStrainStudyA4c", self.fnames[index])
         else:
-            # video = os.path.join(self.root, "Videos", self.fnames[index])
+            video = os.path.join(self.root, "Videos", self.fnames[index])
 
         # Load video into np.array
         video = echonet.utils.loadvideo(video).astype(np.float32)