Update app.py

app.py

@@ -1,30 +1,32 @@
 import torch
 import gradio as gr
-from model import ALexNet  # Make sure this matches your actual class name
 from torchvision import transforms
 from PIL import Image
 
-# Load model
-model = ALexNet(3, 64, 10)
-model.load_state_dict(torch.load("Modified_ALexnet_for_CIFAR.pth", map_location=torch.device("cpu")))
-model.eval()
+from model import ALexNet  # Make sure this file and class exist
+
+print("App is starting...")
+
+try:
+    model = ALexNet(3, 64, 10)
+    model.load_state_dict(torch.load("Modified_ALexnet_for_CIFAR.pth", map_location=torch.device("cpu")))
+    model.eval()
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Failed to load model: {e}")
 
-# Preprocessing
 transform = transforms.Compose([
-    transforms.Resize((32, 32)),  # Adjust to your model's input size
+    transforms.Resize((32, 32)),
     transforms.ToTensor()
 ])
 
-# Inference function
 def predict(img):
     img = transform(img).unsqueeze(0)
     with torch.no_grad():
         outputs = model(img)
         predicted_class = torch.argmax(outputs, dim=1).item()
         class_names = ["airplane", "automobile", "bird", "cat", "deer",
-               "dog", "frog", "horse", "ship", "truck"]
-
+                       "dog", "frog", "horse", "ship", "truck"]
     return f"Predicted class: {class_names[predicted_class]}"
 
-# Gradio UI
 gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs="text").launch()