import subprocess

# subprocess.run(["pip", "install", "-e", "./models/GroundingDINO"])
# subprocess.run(["pip", "install", "gradio==4.21.0"])
# subprocess.run(["pip", "install", "fastapi==0.108.0"])



import gradio as gr

from UniVAD.tools import process_image

# subprocess.run(["wget", "-q","https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth"], check=True)
# subprocess.run(["wget", "-q","https://huggingface.co/xinyu1205/recognize-anything-plus-model/resolve/main/ram_plus_swin_large_14m.pth"], check=True)
# subprocess.run(["wget", "-q","https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_h.pth"], check=True)

import torch
import torchvision.transforms as transforms
from UniVAD.univad import UniVAD

from ram.models import ram_plus

from UniVAD.models.segment_anything import (
    sam_hq_model_registry,
    SamPredictor,
)

# Grounding DINO
from UniVAD.models.grounded_sam import (
    load_model,
)



device = "cuda" if torch.cuda.is_available() else "cpu"
image_size = 224
univad_model = UniVAD(image_size=image_size).to(device)


transform = transforms.Compose(
    [
        transforms.Resize((image_size, image_size)),
        transforms.ToTensor(),
    ]
)

ram_model = ram_plus(
    pretrained="./ram_plus_swin_large_14m.pth",
    image_size=384,
    vit="swin_l",
)
ram_model.eval()
ram_model = ram_model.to(device)


grounding_model = load_model(
    "./UniVAD/models/GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py",
    "./groundingdino_swint_ogc.pth",
    "cuda" if torch.cuda.is_available() else "cpu"
)
sam = sam_hq_model_registry["vit_h"]("./sam_hq_vit_h.pth").to(device)
sam_predictor = SamPredictor(sam)


def preprocess_image(img):
    return img.resize((448, 448))



def update_image(image):
    if image is not None:
        return preprocess_image(image)
    
def ad(image_pil, normal_image, box_threshold, text_threshold, text_prompt, background_prompt, cluster_num):
    return process_image(image_pil, normal_image, box_threshold, text_threshold, sam_predictor, grounding_model, univad_model, ram_model, text_prompt, background_prompt, cluster_num, image_size)



with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center" style='margin-top: 30px;'>Demo of UniVAD</h1>""")

    with gr.Row():
        with gr.Column():
            with gr.Row():
                gr.Markdown("### Upload normal image here for reference.")
            with gr.Row():
                normal_img = gr.Image(type="pil", label="Normal Image", value=None, height=475, width=440)
                normal_img.change(fn=update_image, inputs=normal_img, outputs=normal_img)
            with gr.Row():
                normal_mask = gr.Image(type="filepath", label="Normal Component Mask", value=None, height=450, interactive=False)
            with gr.Row():
                clearBtn = gr.Button("Clear", variant="secondary")

        with gr.Column():
            with gr.Row():
                gr.Markdown("### Upload query image here for anomaly detection.")
            with gr.Row():
                query_img = gr.Image(type="pil", label="Query Image", value=None, height=475, width=440)
                query_img.change(fn=update_image, inputs=query_img, outputs=query_img)
            with gr.Row():
                query_mask = gr.Image(type="filepath", label="Query Component Mask", value=None, height=450)
            with gr.Row():
                submitBtn = gr.Button("Submit", variant="primary")

        with gr.Column():

            with gr.Row():
                gr.Markdown("### Settings:")
            with gr.Row():
                box_threshold = gr.Slider(minimum=0.0, maximum=1.0, value=0.25, label="Box Threshold")
            with gr.Row():
                text_threshold = gr.Slider(minimum=0.0, maximum=1.0, value=0.25, label="Text Threshold")
            with gr.Row():
                text_prompt = gr.Textbox(label="Specify what should be in the image.  Separate them with periods (.)", placeholder="(optional)") 
            with gr.Row():
                background_prompt = gr.Textbox(label="Specify what should be IGNORED in the image. Separate them with periods (.)", placeholder="(optional)")
            with gr.Row():
                cluster_num = gr.Textbox(label="Number of Clusters", placeholder="(optional)")                               

            with gr.Row():
                anomaly_map_raw = gr.Image(type="filepath", label="Localizaiton Result", value=None, height=450)

            with gr.Row():
                anomaly_score = gr.HTML(value="<span style='font-size: 30px;'>Detection Result:</span>")    

    gr.State()

    submitBtn.click(
        ad, [
            query_img,
            normal_img,
            box_threshold,
            text_threshold,
            text_prompt,
            background_prompt,
            cluster_num,
        ], [
            query_mask,
            normal_mask,
            anomaly_map_raw,
            anomaly_score
            
        ],
        show_progress=True
    )

    clearBtn.click(
        lambda: (None, None, None, None, None, "<span style='font-size: 30px;'>Detection Result:</span>"), 
        outputs=[query_img, normal_img, query_mask, normal_mask, anomaly_map_raw, anomaly_score]
    )
    

    demo.queue().launch()