added params to app

app.py

@@ -1,13 +1,32 @@
 import torch
+import torch.nn as nn
 import torchvision.transforms as transforms
 from torchvision import models
 from PIL import Image
 import gradio as gr
 
+# Define custom class 'Params' if used
+class Params:
+    def __init__(self):
+        self.batch_size = 128  # You can adjust this based on your GPU memory
+        self.name = "resnet_50_sgd"  # Rename to reflect ResNet-50
+        self.workers = 4  # Number of DataLoader workers
+        self.lr = 0.1  # Learning rate for SGD optimizer
+        self.momentum = 0.9  # Momentum for SGD
+        self.weight_decay = 1e-4  # Weight decay for regularization
+        self.lr_step_size = 30  # Step size for learning rate decay
+        self.lr_gamma = 0.1  # Gamma factor for learning rate decay
+
+    def __repr__(self):
+        return str(self.__dict__)
+
+    def __eq__(self, other):
+        return self.__dict__ == other.__dict__
+
 # Force CPU usage
 device = torch.device('cpu')
 
-# Load your trained ResNet-50 model
+# Load your trained ResNet-50 model (or any custom architecture)
 model = models.resnet50(pretrained=False)  # Load the ResNet-50 architecture
 model.load_state_dict(torch.load("model.pth", map_location=device))  # Load the trained weights (.pth)
 model.to(device)  # Move model to CPU (even if you have a GPU)