Update app.py

app.py

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+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image
+
+# Define the complex CNN model
+class ComplexCNN(nn.Module):
+    def __init__(self, num_classes=4):
+        super(ComplexCNN, self).__init__()
+        self.layer1 = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+        )
+        self.layer2 = nn.Sequential(
+            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(128),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+        )
+        self.layer3 = nn.Sequential(
+            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+        )
+        self.layer4 = nn.Sequential(
+            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(512),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+        )
+        self.fc1 = nn.Linear(512 * 2 * 2, 1024)  # Adjust based on the final feature map size
+        self.fc2 = nn.Linear(1024, 512)
+        self.fc3 = nn.Linear(512, num_classes)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(p=0.5)
+
+    def forward(self, x):
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = x.view(x.size(0), -1)  # Flatten the output
+        x = self.dropout(self.relu(self.fc1(x)))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# Load the trained model
+model = ComplexCNN(num_classes=4)
+model.load_state_dict(torch.load('model_weights.pth', map_location=torch.device('cpu')))
+model.eval()
+
+# Define the transformation
+transform = transforms.Compose([
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+# Define the class labels
+class_labels = ['dog', 'goat', 'lion', 'sheep']
+
+# Function to predict the class of an uploaded image
+def predict(image):
+    image = transform(image).unsqueeze(0)  # Transform and add batch dimension
+    with torch.no_grad():
+        outputs = model(image)
+        _, predicted = torch.max(outputs, 1)
+        predicted_class = class_labels[predicted.item()]
+    return predicted_class
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Textbox(label="Predicted Class")
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()