import gradio as gr
from datetime import datetime
import pytz
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import pandas as pd
from tqdm import tqdm
from collections import defaultdict
import os
from PIL import Image
import shutil
import copy
import numpy as np
import matplotlib.pyplot as plt
from util import transform, train_transform, AnimalDataset
from Nets import CustomResNet18
from deep_translator import GoogleTranslator
from torchcam.methods import SmoothGradCAMpp, XGradCAM, ScoreCAM, GradCAM, GradCAMpp, CAM
import matplotlib.pyplot as plt
from torchcam.utils import overlay_mask
from torchvision.transforms.functional import to_pil_image
from sklearn.preprocessing import LabelEncoder
from functools import lru_cache

IMAGE_PATH = 'src/examples'
RANDOM_IMAGES_TO_SHOW = 20
IMAGES_PER_ROW = 5

CAM_METHODS = {
    "GradCAM": GradCAM,
    "GradCAM++": GradCAMpp,
    "XGradCAM": XGradCAM,
    "SmoothGradCAM++": SmoothGradCAMpp,
}

model = CustomResNet18(90).eval()
model.load_state_dict(torch.load('src/results/models/best_model.pth', map_location=torch.device('cpu')))
cam_model = copy.deepcopy(model)
data_df = pd.read_csv('src/cache/val_df.csv')

def load_random_images():
    random_images = list()
    for i in range(RANDOM_IMAGES_TO_SHOW):
        idx = np.random.randint(0, len(data_df))
        p = os.path.join(IMAGE_PATH, data_df.iloc[idx]['path'])
        animal = data_df.iloc[idx]['target']
        random_images.append((animal, Image.open(p)))
    return random_images

def get_class_name(idx):
    return data_df[data_df['encoded_target'] == idx]['target'].values[0]

@lru_cache(maxsize=100)
def get_translated(to_translate):
    return GoogleTranslator(source="en", target="de").translate(to_translate)
for idx in tqdm(range(90), desc="Translate animals"): get_translated(get_class_name(idx))

def infer_image(image):
    image = transform(image)
    image = image.unsqueeze(0)
    with torch.no_grad():
        output = model(image)
    distribution = torch.nn.functional.softmax(output, dim=1)
    ret = defaultdict(float)
    for idx, prob in enumerate(distribution[0]):
        animal = f'{get_class_name(idx)} ({get_translated(get_class_name(idx))})'
        ret[animal] = prob.item()
    return ret

def gradcam(image, alpha, cam_method, layer):
    if layer == 'layer1': layers = [model.resnet.layer1]
    elif layer == 'layer2': layers = [model.resnet.layer2]
    elif layer == 'layer3': layers = [model.resnet.layer3]
    elif layer == 'layer4': layers = [model.resnet.layer4]
    else: layers = [model.resnet.layer1, model.resnet.layer2, model.resnet.layer3, model.resnet.layer4]
    
    model.eval()
    img_tensor = transform(image).unsqueeze(0)
    cam = CAM_METHODS[cam_method](model, target_layer=layers)
    output = model(img_tensor)
    activation_map = cam(output.squeeze(0).argmax().item(), output)
    result = overlay_mask(image, to_pil_image(activation_map[0].squeeze(0), mode='F'), alpha=alpha)
    cam.remove_hooks()
    
    # height maximal 300px
    if result.size[1] > 300:
        ratio = 300 / result.size[1]
        result = result.resize((int(result.size[0] * ratio), 300))
    return result

with gr.Blocks() as demo:
    with open('src/header.md', 'r') as f:
        markdown_string = f.read()
    header = gr.Markdown(markdown_string)
    
    with gr.Row(variant="panel", equal_height=True):
        user_image = gr.Image(
            type="pil",
            label="Upload Your Own Image",
            info="You can also upload your own image for prediction.",
            scale=1,
        )
        
    with gr.Tab("Predict"):
        with gr.Column():
            output = gr.Label(
                num_top_classes=3,
                label="Output",
                info="Top three predicted classes and their confidences.",
                scale=5,
            )
            predict_mode_button = gr.Button(value="Predict Animal", label="Predict", info="Click to make a prediction.", scale=1)
            predict_mode_button.click(fn=infer_image, inputs=[user_image], outputs=output, queue=True)
    
    with gr.Tab("Explain"):
        with gr.Row():
            with gr.Column():
                cam_method = gr.Radio(
                    list(CAM_METHODS.keys()),
                    label="GradCAM Method",
                    value="GradCAM",
                    interactive=True,
                    scale=2,
                )
                cam_method.description = "Here you can choose the GradCAM method."
                cam_method.description_place = "left"
                
                alpha = gr.Slider(
                    minimum=.1,
                    maximum=.9,
                    value=0.5,
                    interactive=True,
                    step=.1,
                    label="Alpha",
                    scale=1,
                )
                alpha.description = "Here you can choose the alpha value."
                alpha.description_place = "left"
                
                layer = gr.Radio(
                    ["layer1", "layer2", "layer3", "layer4", "all"],
                    label="Layer",
                    value="layer4",
                    interactive=True,
                    scale=2,
                )
                layer.description = "Here you can choose the layer to visualize."
                layer.description_place = "left"
                
            with gr.Column():
                output_cam = gr.Image(
                    type="pil",
                    label="GradCAM",
                    info="GradCAM visualization"
                    
                )
                
                gradcam_mode_button = gr.Button(value="Show GradCAM", label="GradCAM", info="Click to make a prediction.", scale=1)
                gradcam_mode_button.click(fn=gradcam, inputs=[user_image, alpha, cam_method, layer], outputs=output_cam, queue=True)
            
    with gr.Tab("Example Images"):
        with gr.Column():
            placeholder = gr.Markdown("## Example Images")
            showed_images = list()
            loaded_images = load_random_images()
            amount_rows = max(1, (len(loaded_images) // IMAGES_PER_ROW))
            for i in range(amount_rows):
                with gr.Row():
                    for j in range(IMAGES_PER_ROW):
                        animal, image = loaded_images[i * IMAGES_PER_ROW + j]
                        showed_images.append(gr.Image(
                            value=image,
                            label=animal,
                            type="pil",
                            interactive=False,
                        ))
            
            
            
if __name__ == "__main__":
    demo.queue()
    demo.launch()