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()