text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import AlgoTradeDataSet as ds
  import torchvision
  from torchvision import transforms
  from torch.utils.data import Dataset, DataLoader
  import os
  
  MODEL = "algotrade3.pt"
  BATCH_SIZE = 1
  IMAGE_SIZE = ds.IMAGE_SIZE         # MUST MATCH MODEL
  
  
  test = ds.StockDataset(root_dir='\\CoinCurrent', transform=transforms.Compose([
      transforms.ToPILImage(),
      transforms.Grayscale(num_output_channels=3),
      transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)),
      transforms.ToTensor(),
      transforms.Normalize(mean=[0.5], std=[0.5]),
  ]))
  testloader = DataLoader(test, batch_size=BATCH_SIZE, shuffle=False)
  
  files = []
  for file in os.listdir(os.getcwd() + "\\CoinCurrent"):
      if file.endswith('.png'):
          files.append(str(file))
  
  
  if torch.cuda.is_available():
      device = torch.device("cuda:0")
      print(torch.cuda.get_device_name(0))
  else:
      device = torch.device("cpu")
  
  net = torchvision.models.resnet152(pretrained=False).to(device)
  
  net.load_state_dict(torch.load("inferenceDAYTRADE.pt"))
  
  classes = ("Fall", "Stable", "Rise")
  
  correct = 0
  
  net = net.eval()
  criterion = nn.CrossEntropyLoss()
  i = 0
  for data in testloader:
      images, labels = data.get("image").to(device), data.get("solution").to(device)
      outputs = net(images)
      _, predicted = torch.max(outputs.data, 1)
      if predicted == torch.tensor([750]).to(device):
          probabilities = F.softmax(outputs, dim=1)
          if probabilities[0][750] > 0.7:
              print(probabilities[0][750])
              print(files[i])
      i += 1