update model code

app.py

@@ -17,6 +17,28 @@ import cloudinary.uploader
 from a import main
 import numpy as np
 import torchvision.transforms as transforms
+import pandas as pd
+import nibabel as nib
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+import cv2
+from PIL import Image
+from sklearn.model_selection import train_test_split
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+import torchvision.models as models
+from torch.utils.data import Dataset, DataLoader
+import pandas as pd
+from PIL import Image
+import os
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
 # Initialize Flask app
 app = Flask(__name__)
 
@@ -272,6 +294,10 @@ def predict():
 
     # Initialize Model, Loss, and Optimizer
     model_new = ResNetRegression()
+    checkpoint = torch.load(
+        "/home/user/app/a.pth", weights_only=False, map_location=torch.device('cpu'))
+    checkpoint = remove_module_from_checkpoint(checkpoint)
+    model_new.load_state_dict(checkpoint['model_state_dict'])
     image = Image.open(temp_path).convert("RGB")
     output = model_new(transform(image).unsqueeze(0))
     stage = output.item()

requirements.txt

@@ -24,4 +24,4 @@ cloudinary
 PyPDF2
 scikit-learn
 pandas
-gdown
+gdown