import os
os.system('git clone https://github.com/facebookresearch/detectron2.git')
os.system('pip install -e detectron2')
os.system("git clone https://github.com/microsoft/unilm.git")
os.system("sed -i 's/from collections import Iterable/from collections.abc import Iterable/' unilm/dit/object_detection/ditod/table_evaluation/data_structure.py")
os.system("curl -LJ -o publaynet_dit-b_cascade.pth 'https://layoutlm.blob.core.windows.net/dit/dit-fts/publaynet_dit-b_cascade.pth?sv=2022-11-02&ss=b&srt=o&sp=r&se=2033-06-08T16:48:15Z&st=2023-06-08T08:48:15Z&spr=https&sig=a9VXrihTzbWyVfaIDlIT1Z0FoR1073VB0RLQUMuudD4%3D'")

import sys
sys.path.append("unilm")
sys.path.append("detectron2")

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

from huggingface_hub import hf_hub_download

import gradio as gr


# 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
filepath = hf_hub_download(repo_id="Sebas6k/DiT_weights", filename="publaynet_dit-b_cascade.pth", repo_type="model")
cfg.MODEL.WEIGHTS = filepath

# 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):
    img = img.astype("float32")
    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
    
title = "Document Layout Analysis"
description = "Demo"
article = ""
# examples =[['publaynet_example.jpeg']]
examples = [
    ['publaynet_example.jpeg'],
    ['PMC1064093_00000.jpg'],
    ['PMC1064139_00005.jpg'],
    ['PMC1079928_00003.jpg'],
    ['PMC1097753_00002.jpg']
]
css = ".output-image, .input-image, .image-preview {height: 600px !important}"

iface = gr.Interface(fn=analyze_image, 
                     inputs=gr.Image(type="numpy", label="document image"), 
                     outputs=gr.Image(type="numpy", label="annotated document"),
                     title=title,
                     description=description,
                     examples=examples,
                     article=article,
                     css=css)
iface.queue(5).launch()