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app.py

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+import torch
+import gradio as gr
+import torchvision.transforms as transforms
+from PIL import Image
+import torch.nn as nn
+import torch.nn.functional as F
+
+transform_test = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+class_names = [
+    'Auto Rickshaws', 'Bikes', 'Cars', 'Motorcycles',
+    'Planes', 'Ships', 'Trains'
+]
+class VehicleClassifier(nn.Module):
+    def __init__(self):
+        super(VehicleClassifier, self).__init__()
+        
+        # Convolutional Layers
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
+        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
+        
+        # Pooling Layer
+        self.pool = nn.MaxPool2d(2, 2)
+        
+        # FC Layers
+        self.fc1 = nn.Linear(256 * 14 * 14, 512)
+        self.fc2 = nn.Linear(512, 256)
+        self.fc3 = nn.Linear(256, 7)  # 7 classes for the 7 vehicle categories
+        
+        self.dropout = nn.Dropout(0.5)
+        
+    def forward(self, x):
+        # Apply Convolutional Layers with ReLU activation and Pooling
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = self.pool(F.relu(self.conv3(x)))
+        x = self.pool(F.relu(self.conv4(x)))
+        
+        # Flatten the tensor before feeding into the FCL
+        x = x.view(-1, 256 * 14 * 14)
+        x = F.relu(self.fc1(x))
+        x = self.dropout(x)
+        x = F.relu(self.fc2(x))
+        x = self.dropout(x)
+        x = self.fc3(x)
+        return x
+model = VehicleClassifier().to('cpu') 
+model.load_state_dict(torch.load('vehicle_classifier.pth', map_location=torch.device('cpu')))
+model.eval()  
+
+def predict(image):
+    try:
+        image = Image.open(image).convert('RGB')
+        image = transform_test(image).unsqueeze(0)  # Add batch dimension
+        
+        print(f"Image shape after transformation: {image.shape}")
+        
+        with torch.no_grad():
+            outputs = model(image)
+            print(f"Model output: {outputs}")
+            _, predicted = torch.max(outputs, 1)
+        
+        prediction = class_names[predicted.item()]
+        print(f"Predicted class: {prediction}")
+        
+        return prediction
+    except Exception as e:
+        print(f"Error during prediction: {e}")
+        traceback.print_exc()
+        return "An error occurred during prediction."
+
+
+interface = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type='filepath'),  
+    outputs=gr.Label(num_top_classes=1),  
+    title="Vehicle Classification",
+    description="Upload an image of a vehicle, and the model will predict its type."
+)
+
+# Launch the interface
+interface.launch(share=True)

vehicle_classifier.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f0ed7dbfc5083a96209bad40a0eb4e7889505456a0ffcb999406fff96e14c68d
+size 104853810