import torch, 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 custom_ResNet
import gradio as gr
import os


model = custom_ResNet()
model.load_state_dict(torch.load("custom_resnet_model.pth", map_location=torch.device('cpu')), strict=False)
model.setup(stage="test")

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 inference(input_img, transparency=0.5, target_layer_number=-1, top_classes=3):
    transform = transforms.ToTensor()
    org_img = input_img
    input_img = transform(input_img)
    input_img = input_img.unsqueeze(0)
    outputs = model(input_img)
    softmax = torch.nn.Softmax(dim=1)  # Use dim=1 to compute softmax along the classes dimension
    o = softmax(outputs)
    confidences = {classes[i]: float(o[0, i]) for i in range(10)}
    _, prediction = torch.max(outputs, 1)
    target_layers = [model.convblock2_l1]
    cam = GradCAM(model=model, target_layers=target_layers, use_cuda=False)
    grayscale_cam = cam(input_tensor=input_img, targets=None)
    grayscale_cam = grayscale_cam[0, :]
    img = input_img.squeeze(0)
    img = inv_normalize(img)
    rgb_img = np.transpose(img, (1, 2, 0))
    rgb_img = rgb_img.numpy()
    visualization = show_cam_on_image(org_img / 255, grayscale_cam, use_rgb=True, image_weight=transparency)

    # Sort the confidences dictionary by values in descending order
    sorted_confidences = {k: v for k, v in sorted(confidences.items(), key=lambda item: item[1], reverse=True)}
    # Take the top `top_classes` elements from the sorted_confidences
    top_classes_confidences = {k: sorted_confidences[k] for k in list(sorted_confidences)[:top_classes]}

    return top_classes_confidences, visualization


# Create a wrapper function for show_misclassified_images()
def show_misclassified_images_wrapper(num_images=10, use_gradcam=False, gradcam_layer=-1, transparency=0.5):
    transparency = float(transparency)
    num_images = int(num_images)
    if use_gradcam == "Yes":
      use_gradcam = True
    else:
      use_gradcam = False

    return model.show_misclassified_images(num_images, use_gradcam, gradcam_layer, transparency)

description1 = "Test the model's prediction. Currently the model only supports the following classes - plane, car, bird, cat, deer, dog, frog, horse, ship, truck."


# Define the full path to the images folder
#images_folder = "examples"

# Define the examples list with full paths
examples = [("plane.jpg", 0.5, -1),
            ("car.jpg", 0.5, -1),
            ("bird.jpg", 0.5, -1),
            ("cat.jpg", 0.5, -1),
            ("deer.jpg", 0.5, -1),
            ("dog.jpg", 0.5, -1),
            ("frog.jpg", 0.5, -1),
            ("horse.jpg", 0.5, -1),
            ("ship.jpg", 0.5, -1),
            ("truck.jpg", 0.5, -1)]


# Create a separate interface for the "Input an image" tab
input_interface = gr.Interface(inference,
                inputs=[gr.Image(shape=(32, 32), label="Input Image"),
                        gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"),
                        gr.Slider(-2, -1, value=-2, step=1, label="Which Layer?"),
                        gr.Slider(1, 10, value=3, step=1, label="How many top confidence classes to be shown?")],
                outputs=[gr.Label(),
                         gr.Image(shape=(32, 32), label="Model Prediction").style(width=300, height=300)],
                description=description1,examples=examples)

description2 = "Displays misclassified image of the model"

# Create a separate interface for the "Misclassified Images" tab
misclassified_interface = gr.Interface(show_misclassified_images_wrapper,
                    inputs=[gr.Number(value=10, label="Number of images to display"),
                            gr.Radio(["Yes", "No"], value="No"  , label="Show GradCAM outputs"),
                            gr.Slider(-2, -1, value=-1, step=1, label="Which layer for GradCAM?"),
                            gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM")],
                    outputs=gr.Plot(), description=description2)

demo = gr.TabbedInterface([input_interface, misclassified_interface], tab_names=["Input an image", "Misclassified Images"], 
                          title="Custom Resnet on CIFAR10 using GradCAM")

demo.launch()