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("cifar10_saved_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):

    # Convert to PIL image
    img = Image.fromarray(np.array(image))
    
    # Get original size
    width, height = img.size

    # Calculate scale
    width_scale = new_width / width
    height_scale = new_height / height 
    scale = min(width_scale, height_scale)

    # Resize
    resized = img.resize((int(width*scale), int(height*scale)), Image.NEAREST)
    
    # Crop to exact size
    resized = resized.crop((0, 0, new_width, new_height))

    return resized


def inference(input_img, transparency=0.5, target_layer_number=-1, grad_cam_option="Yes",top_classes=3):
    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=0)
    o = softmax(outputs.flatten())
    confidences = {classes[i]: float(o[i]) for i in range(10)}
    confidences = dict(list(confidences.items())[:top_classes])
    _, prediction = torch.max(outputs, 1)
    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, :]
    img = input_img.squeeze(0)
    img = inv_normalize(img)
    print('Confidences ',confidences)
    if grad_cam_option == "Yes":
        visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
        return classes[prediction[0].item()], visualization, confidences
    else:
        return classes[prediction[0].item()], None, confidences

title = "CIFAR10 trained on ResNet18 Model with GradCAM"
description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1], ["bird_1.jpg", 0.5, -1], ["cat_1.jpg", 0.5, -1], ["cat_2.jpg", 0.5, -1],
            ["dog_1.jpg", 0.5, -1], ["dog_2.jpg", 0.5, -1], ["dog_3.jpg", 0.5, -1], ["ship_1.jpg", 0.5, -1], ["ship_2.jpg", 0.5, -1]]

demo = 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?"),
        gr.Dropdown(["Yes", "No"], label="Want to see Grad Cam Images?"),
        gr.Number(value=3, minimum=1,maximum=10)
    ], 
    outputs=[
        "text", 
        gr.Image(width=256, height=256, label="Output"),
        gr.Label()
    ],
    title=title,
    description=description,
    examples=examples,
)

demo.launch()