import gradio as gr
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import hf_hub_download
from torch.autograd import Variable
from PIL import Image


def build_model(hypar, device):
    net = hypar["model"]  # GOSNETINC(3,1)

    # convert to half precision
    if hypar["model_digit"] == "half":
        net.half()
        for layer in net.modules():
            if isinstance(layer, nn.BatchNorm2d):
                layer.float()

    net.to(device)

    if hypar["restore_model"] != "":
        net.load_state_dict(
            torch.load(
                hypar["model_path"] + "/" + hypar["restore_model"],
                map_location=device,
            )
        )
        net.to(device)
    net.eval()
    return net


if not os.path.exists("saved_models"):
    os.mkdir("saved_models")
    os.mkdir("git")
    os.system("git clone https://github.com/xuebinqin/DIS git/xuebinqin/DIS")
    hf_hub_download(
        repo_id="NimaBoscarino/IS-Net_DIS-general-use",
        filename="isnet-general-use.pth",
        local_dir="saved_models",
    )
    os.system("rm -r git/xuebinqin/DIS/IS-Net/__pycache__")
    os.system("mv git/xuebinqin/DIS/IS-Net/* .")

import data_loader_cache
import models

device = "cpu"
ISNetDIS = models.ISNetDIS
normalize = data_loader_cache.normalize
im_preprocess = data_loader_cache.im_preprocess

# Set Parameters
hypar = {}  # paramters for inferencing

# load trained weights from this path
hypar["model_path"] = "./saved_models"
# name of the to-be-loaded weights
hypar["restore_model"] = "isnet-general-use.pth"
# indicate if activate intermediate feature supervision
hypar["interm_sup"] = False

# choose floating point accuracy --
# indicates "half" or "full" accuracy of float number
hypar["model_digit"] = "full"
hypar["seed"] = 0

# cached input spatial resolution, can be configured into different size
hypar["cache_size"] = [1024, 1024]

# data augmentation parameters ---
# mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
hypar["input_size"] = [1024, 1024]
# random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
hypar["crop_size"] = [1024, 1024]

hypar["model"] = ISNetDIS()

# Build Model
net = build_model(hypar, device)


def predict(net, inputs_val, shapes_val, hypar, device):
    """
    Given an Image, predict the mask
    """
    net.eval()

    if hypar["model_digit"] == "full":
        inputs_val = inputs_val.type(torch.FloatTensor)
    else:
        inputs_val = inputs_val.type(torch.HalfTensor)

    inputs_val_v = Variable(inputs_val, requires_grad=False).to(
        device
    )  # wrap inputs in Variable

    ds_val = net(inputs_val_v)[0]  # list of 6 results

    # B x 1 x H x W    # we want the first one which is the most accurate prediction
    pred_val = ds_val[0][0, :, :, :]

    # recover the prediction spatial size to the orignal image size
    pred_val = torch.squeeze(
        F.upsample(
            torch.unsqueeze(pred_val, 0),
            (shapes_val[0][0], shapes_val[0][1]),
            mode="bilinear",
        )
    )

    ma = torch.max(pred_val)
    mi = torch.min(pred_val)
    pred_val = (pred_val - mi) / (ma - mi)  # max = 1

    if device == "cpu":
        torch.cpu.empty_cache()
    # it is the mask we need
    return (pred_val.detach().cpu().numpy() * 255).astype(np.uint8)


def load_image(im_pil, hypar):
    im = np.array(im_pil)
    im, im_shp = im_preprocess(im, hypar["cache_size"])
    im = torch.divide(im, 255.0)
    shape = torch.from_numpy(np.array(im_shp))
    # make a batch of image, shape
    aa = normalize(im, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
    return aa.unsqueeze(0), shape.unsqueeze(0)


def remove_background(image):
    image_tensor, orig_size = load_image(image, hypar)
    mask = predict(net, image_tensor, orig_size, hypar, "cpu")

    mask = Image.fromarray(mask).convert("L")
    im_rgb = image.convert("RGB")

    cropped = im_rgb.copy()
    cropped.putalpha(mask)
    return cropped


inputs = gr.inputs.Image()
outputs = gr.outputs.Image(type="pil")
interface = gr.Interface(
    fn=remove_background,
    inputs=inputs,
    outputs=outputs,
    title="Remove Background",
    description="This App removes the background from an image",
    examples=[
        "examples/input/1.jpeg",
        "examples/input/2.jpeg",
        "examples/input/3.jpeg",
    ],
    cache_examples=True,
)
interface.launch(enable_queue=True)