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 ResNet18
import gradio as gr

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 inference(input_img, transparency = 0.5, target_layer_number = -1):
    transform = transforms.ToTensor()
    org_img = input_img
    input_img = transform(input_img)
    input_img = input_img
    input_img = input_img.unsqueeze(0)
    outputs = model(input_img)
    softmax = torch.nn.Softmax(dim=0)
    o = softmax(outputs.flatten())
    confidences = {classes[i]: float(o[i]) for i in range(10)}
    _, prediction = torch.max(outputs, 1)
    target_layers = [model.layer2[target_layer_number]]
    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)
    return confidences, visualization

def inference_confidences(input_img, transparency = 0.5, target_layer_number = -1):
    transform = transforms.ToTensor()
    org_img = input_img
    input_img = transform(input_img)
    input_img = input_img
    input_img = input_img.unsqueeze(0)
    outputs = model(input_img)
    softmax = torch.nn.Softmax(dim=0)
    o = softmax(outputs.flatten())
    confidences = {classes[i]: float(o[i]) for i in range(10)}
    return confidences

def inference_visualization(input_img, transparency = 0.5, target_layer_number = -1):
    transform = transforms.ToTensor()
    org_img = input_img
    input_img = transform(input_img)
    input_img = input_img
    input_img = input_img.unsqueeze(0)
    outputs = model(input_img)
    softmax = torch.nn.Softmax(dim=0)
    o = softmax(outputs.flatten())
    confidences = {classes[i]: float(o[i]) for i in range(10)}
    _, prediction = torch.max(outputs, 1)
    target_layers = [model.layer2[target_layer_number]]
    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)
    return visualization

# Callback function for the Gradio interface
# def gradio_callback(view_gradcam, num_gradcam_images, layer_name, opacity,
#                     view_misclassified, num_misclassified_images,
#                         input_img,submit):
def gradio_callback(view_grad_cam, num_gradcam_images, view_misclassified, num_misclassified_images,
                    input_img, transparency = 0.5, target_layer_number = -1):
    
    confidence = inference_confidences(input_img, transparency = 0.5, target_layer_number = -1)
    if view_grad_cam == "Yes":
        visualization = inference_visualization(input_img, transparency = 0.5, target_layer_number = -1)
        return confidence, visualization
    else:
        return confidence


title = "CIFAR10 trained on ResNet18 Model with GradCAM"
description = "Gradio interface to infer on ResNet18 model, and get GradCAM results"
examples = [["Yes",5,"Yes",5,"cat.jpg", 0.5, -1], ["Yes",5,"Yes",5,"dog.jpg", 0.5, -1]]

demo = 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?")], 
    # outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)],
    # title = title,
    # description = description,
    # examples = examples,
    title = title,
    escription = description,
    # examples = examples,
    fn=gradio_callback,  # We'll add the function later after defining all functions,  # We'll add the function later after defining all functions
    inputs=[
        # gr.Radio(["Yes", "No"], label="View GradCAM images?"),
        # gr.Number(label="Number of GradCAM images to view", default=5, max=10),
        # gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?"),
        # gr.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.5, label="Opacity"),
        # gr.Radio(["Yes", "No"], label="View misclassified images?"),
        # gr.Number(label="Number of misclassified images to view", default=5, min=1, max=10),
        # gr.Image(shape=(32, 32), label="Input Image")
        # gr.Radio(["Yes", "No"], label="View GradCAM images?"),
        gr.Radio(["Yes", "No"], label="GradCAM images", info="View GradCAM images?"),
        gr.Number(label="Number of GradCAM images to view", default=5, max=10),
        gr.Radio(["Yes", "No"], label="View misclassified images?"),
        gr.Number(label="Number of misclassified images to view", default=5, min=1, max=10),
        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?")
                  
    ],
    outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)],
    examples = examples,
    # live=True
)



# Set the callback function to the Gradio interface
# demo.fn = gradio_callback
demo.launch()