import gradio as gr
import torch, torchvision
from torchvision import transforms
from resnet import ResNet18
from resnet import ResBlocks
from PIL import Image
import numpy as np
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from pl_bolts.transforms.dataset_normalizations import cifar10_normalization

model = ResNet18(0.00333)

state_model = torch.load("final_model.pkl", map_location=torch.device('cpu'))
state_dict = state_model.state_dict()

model.load_state_dict(state_dict, strict=False)

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

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 np.array(resized)

def inference(input_img, transparency = 0.5, target_layer_number = -1):

    input_img = resize_image_pil(input_img, 32, 32)    
    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)
    input_img = cifar10_normalization()(input_img)
    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.res_layers[2][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, :]

    img = input_img.squeeze(0)
    img = inv_normalize(img)
    print(transparency)

    visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
    return classes[prediction[0].item()], visualization, confidences

title = "CIFAR10 trained on ResNet18 Model with GradCAM"
description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"

iface = gr.Interface(
    inference, 
    inputs = [
        gr.Image(width=256, height=256, label="Input Image"), 
        gr.Slider(0, 1, value = 0.5, label="Overall Opacity of Image"), 
        gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?")
        ], 
    outputs = [
        "text",
        gr.Image(width=256, height=256, label="Output"),
        gr.Label(num_top_classes=3)
        ],
    title = title,
    description = description,
)

iface.launch()