import torch
import torchvision
from torchvision import transforms
import numpy as np
import gradio as gr
from PIL import Image
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from resnet import ResNet18

model = ResNet18()
model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)

inv_normalize = transforms.Normalize(
    mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23],
    std=[1/0.23, 1/0.23, 1/0.23]
)

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

def resize_image_pil(image, new_width, new_height):
    img = Image.fromarray(np.array(image))
    width, height = img.size
    width_scale = new_width / width
    height_scale = new_height / height
    scale = min(width_scale, height_scale)
    resized = img.resize((int(width*scale), int(height*scale)), Image.NEAREST)
    resized = resized.crop((0, 0, new_width, new_height))
    return resized

def inference(input_img, show_gradcam, num_gradcam, target_layer_number, opacity, show_misclassified, num_misclassified, num_top_classes):
    input_img = resize_image_pil(input_img, 32, 32)
    input_img = np.array(input_img)
    org_img = input_img
    input_img = input_img.reshape((32, 32, 3))
    transform = transforms.ToTensor()
    input_img = transform(input_img)
    input_img = input_img.unsqueeze(0)
    
    outputs = model(input_img)
    softmax = torch.nn.Softmax(dim=1)
    probs = softmax(outputs)
    
    top_probs, top_labels = torch.topk(probs, k=min(num_top_classes, 10))
    top_classes = [classes[idx] for idx in top_labels[0]]
    confidences = {cls: float(prob) for cls, prob in zip(top_classes, top_probs[0])}
    
    _, prediction = torch.max(outputs, 1)
    predicted_class = classes[prediction[0].item()]
    
    results = [predicted_class, confidences]
    
    if show_gradcam:
        target_layers = [model.layer2[target_layer_number]]
        cam = GradCAM(model=model, target_layers=target_layers)
        grayscale_cam = cam(input_tensor=input_img, targets=None)
        grayscale_cam = grayscale_cam[0, :]
        visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=opacity)
        results.append(visualization)
    
    if show_misclassified:
        # This part would require a dataset of images to check for misclassifications
        # For demonstration, we'll just return a placeholder message
        results.append("Misclassified images feature would be implemented here")
    
    return results

def launch():
    with gr.Blocks() as demo:
        gr.Markdown("# CIFAR10 ResNet18 Model with GradCAM")
        
        with gr.Row():
            input_image = gr.Image(width=256, height=256, label="Input Image")
            output_image = gr.Image(width=256, height=256, label="GradCAM Output")
        
        with gr.Row():
            prediction = gr.Textbox(label="Predicted Class")
            confidences = gr.Label(label="Top Class Confidences")
        
        with gr.Row():
            show_gradcam = gr.Checkbox(label="Show GradCAM")
            num_gradcam = gr.Slider(1, 5, value=1, step=1, label="Number of GradCAM images")
            target_layer = gr.Slider(-2, -1, value=-2, step=1, label="Target Layer")
            opacity = gr.Slider(0, 1, value=0.5, label="GradCAM Opacity")
        
        with gr.Row():
            show_misclassified = gr.Checkbox(label="Show Misclassified Images")
            num_misclassified = gr.Slider(1, 10, value=5, step=1, label="Number of Misclassified Images")
        
        num_top_classes = gr.Slider(1, 10, value=3, step=1, label="Number of Top Classes to Show")
        
        submit_btn = gr.Button("Submit")
        
        example_images = gr.Dataset(
            components=[input_image],
            samples=[["cat.jpg"], ["dog.jpg"], ["bird.jpg"], ["plane.jpg"], ["car.jpg"],
                     ["deer.jpg"], ["frog.jpg"], ["horse.jpg"], ["ship.jpg"], ["truck.jpg"]]
        )
        
        submit_btn.click(
            inference,
            inputs=[input_image, show_gradcam, num_gradcam, target_layer, opacity, 
                    show_misclassified, num_misclassified, num_top_classes],
            outputs=[prediction, confidences, output_image]
        )
    
    demo.launch()

if __name__ == "__main__":
    launch()