import gradio as gr
import torch
import torchvision.transforms as transforms
from PIL import Image
import os

class CatDogClassifier:
    def __init__(self, model_path="model.pt"):
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        
        # Load the traced model
        self.model = torch.jit.load(model_path)
        self.model = self.model.to(self.device)
        self.model.eval()

        # Define the same transforms used during training/testing
        self.transform = transforms.Compose([
            transforms.Resize((160, 160)),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225]
            )
        ])

        # Class labels
        self.labels = ['Dog', 'Cat']

    @torch.no_grad()
    def predict(self, image):
        if image is None:
            return None
        
        # Convert to PIL Image if needed
        if not isinstance(image, Image.Image):
            image = Image.fromarray(image).convert('RGB')
        
        # Preprocess image
        img_tensor = self.transform(image).unsqueeze(0).to(self.device)
        
        # Get prediction
        output = self.model(img_tensor)
        probabilities = torch.nn.functional.softmax(output[0], dim=0)
        
        # Create prediction dictionary
        return {
            self.labels[idx]: float(prob)
            for idx, prob in enumerate(probabilities)
        }

# Create classifier instance
classifier = CatDogClassifier()

# Create Gradio interface
demo = gr.Interface(
    fn=classifier.predict,
    inputs=gr.Image(),
    outputs=gr.Label(num_top_classes=2),
    title="Cat vs Dog Classifier",
    description="Upload an image to classify whether it's a cat or a dog",
    examples=[
        ["examples/cat.jpg"],
        ["examples/dog.jpg"]
    ]
)

if __name__ == "__main__":
    demo.launch()