Update app.py

app.py

@@ -1,12 +1,11 @@
 import gradio as gr
-import os
+#import os
 import matplotlib.pyplot as plt
 from scipy.integrate import odeint
 import torch
 from torch.utils import data
 from torch.utils.data import DataLoader, Dataset
 from torch import nn, optim
-import os
 from skimage.transform import rescale, resize
 from torch import nn, optim
 import torch.nn.functional as F
@@ -36,8 +35,8 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 sequences_all = []
 info_data_all = []
-path = '/Users/FDean/Desktop/Physics_Informed_Transfer_Learning/EchoNet-Dynamic'
-output_path = '/Users/FDean/Desktop/Physics_Informed_Transfer_Learning'
+path = 'EchoNet-Dynamic'
+output_path = ''
 
 class Echo(torchvision.datasets.VisionDataset):
     """EchoNet-Dynamic Dataset.
@@ -120,10 +119,10 @@ 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(os.path.join(self.root, "FileList.csv")) as f:
+            with open(f"{self.root}FileList.csv")) as f:
                 data = pandas.read_csv(f)
             data["Split"].map(lambda x: x.upper())
 
@@ -132,7 +131,7 @@ 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
@@ -149,7 +148,7 @@ class Echo(torchvision.datasets.VisionDataset):
             self.frames = collections.defaultdict(list)
             self.trace = collections.defaultdict(_defaultdict_of_lists)
 
-            with open(os.path.join(self.root, "VolumeTracings.csv")) as f:
+            with open(f"{self.root}VolumeTracings.csv")) as f:
                 header = f.readline().strip().split(",")
                 assert header == ["FileName", "X1", "Y1", "X2", "Y2", "Frame"]
 
@@ -175,11 +174,11 @@ 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)
@@ -379,10 +378,10 @@ class Interpolator(nn.Module):
 
 # Initialize the neural network
 net = Interpolator()
-net.load_state_dict(torch.load('/Users/FDean/Desktop/Physics_Informed_Transfer_Learning/final_model_weights/interp6_7param_weight.pt'))
+net.load_state_dict(torch.load('final_model_weights/interp6_7param_weight.pt'))
 print("Done loading interpolator!")
 
-weights_path = '/Users/FDean/Desktop/Physics_Informed_Transfer_Learning/final_model_weights/202_full_echonet_7param_Vloss_epoch_200_lr_0.001_weight_best_model.pt'
+weights_path = 'final_model_weights/202_full_echonet_7param_Vloss_epoch_200_lr_0.001_weight_best_model.pt'
 model = NEW3DCNN(num_parameters = 7)
 model.load_state_dict(torch.load(weights_path))
 model.to(device)
@@ -492,14 +491,14 @@ def pvloop_simulator_plot_only(Rm, Ra, Emax, Emin, Vd, Tc, start_v):
 
 def generate_example():
         # get random input
-        data_path = '/Users/FDean/Desktop/Physics_Informed_Transfer_Learning/EchoNet-Dynamic'
+        data_path = 'EchoNet-Dynamic'
         image_data = Echo(root = data_path, split = 'all', target_type=['Filename','LargeIndex','SmallIndex'])
         image_loaded_data = DataLoader(image_data, batch_size=1, shuffle=True)
         val_data = next(iter(image_loaded_data))
         #create_echo_clip(val_data,'test')
         val_seq = val_data[0]
         filename = val_data[1][0][0]
-        video = os.path.join(os.getcwd(), f"EchoNet-Dynamic/Videos/{filename}")
+        video = f"EchoNet-Dynamic/Videos/{filename}"
         val_tensor = torch.tensor(val_seq, dtype=torch.float32) 
         results = model(val_tensor)