import os

import sys
sys.path.append("unilm")

import cv2

from unilm.dit.object_detection.ditod import add_vit_config

import torch

from detectron2.config import CfgNode as CN
from detectron2.config import get_cfg
from detectron2.utils.visualizer import ColorMode, Visualizer
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultPredictor

import gradio as gr
from gradio.themes.utils import sizes


theme = gr.themes.Default(radius_size=sizes.radius_none).set(
    block_label_text_color = '#4D63FF',
    block_title_text_color = '#4D63FF',
    button_primary_text_color = '#4D63FF',
    button_primary_background_fill='#FFFFFF',
    button_primary_border_color='#4D63FF',
    button_primary_background_fill_hover='#EDEFFF',
)


# Step 1: instantiate config
cfg = get_cfg()
add_vit_config(cfg)
cfg.merge_from_file("cascade_dit_base.yml")

# Step 2: add model weights URL to config
cfg.MODEL.WEIGHTS = "https://layoutlm.blob.core.windows.net/dit/dit-fts/publaynet_dit-b_cascade.pth"

# Step 3: set device
cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# Step 4: define model
predictor = DefaultPredictor(cfg)


def analyze_image(img):
    md = MetadataCatalog.get(cfg.DATASETS.TEST[0])
    if cfg.DATASETS.TEST[0]=='icdar2019_test':
        md.set(thing_classes=["table"])
    else:
        md.set(thing_classes=["text","title","list","table","figure"])
    
    output = predictor(img)["instances"]
    v = Visualizer(img[:, :, ::-1],
                    md,
                    scale=1.0,
                    instance_mode=ColorMode.SEGMENTATION)
    result = v.draw_instance_predictions(output.to("cpu"))
    result_image = result.get_image()[:, :, ::-1]
    
    return result_image
    

with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
    gr.Markdown("""
    <div align='center' ><font size='60'>版面分析</font></div>
    """)
    with gr.Row():
        with gr.Column():
            image = gr.Image(label="图片", type="numpy")
            with gr.Row():
                button = gr.Button("提交", variant="primary")
        box2 = gr.Image(label="图片", type="numpy")

    button.click(fn=analyze_image, inputs=[image], outputs=box2)
    examples = gr.Examples(examples=[['publaynet_example.jpeg']],  inputs=[image], label="例子")


demo.launch(server_name = "0.0.0.0")