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
from transformers import pipeline

captioner = pipeline(
    "image-to-text",
    model="Salesforce/blip-image-captioning-base",
    prompt="The main subject of this picture is a"
)

def removeBackground(image):
    image = Image.fromarray(image).convert("RGB")
    caption = captioner(image, max_new_tokens=20)[0]["generated_text"].lower().replace("The main subject of this picture is a".lower(), "").strip()
    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/* .")

    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

    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 == "cuda":
            torch.cuda.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)

    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, caption

inputs = gr.inputs.Image()
outputs = [gr.outputs.Image(type="pil"), "text"]
interface = gr.Interface(
    fn=removeBackground,
    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)