try:
    import detectron2
except:
    import os 
    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')

from matplotlib.pyplot import axis
import gradio as gr
import requests
import numpy as np
from torch import nn
import requests

import torch

from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog


model_path = "https://huggingface.co/dbmdz/detectron2-model/resolve/main/model_final.pth"

cfg = get_cfg()
cfg.merge_from_file("./configs/detectron2/faster_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.8
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2
cfg.MODEL.WEIGHTS = model_path

my_metadata = MetadataCatalog.get("dbmdz_coco_all")
my_metadata.thing_classes = ["Illumination", "Illustration"]

if not torch.cuda.is_available():
    cfg.MODEL.DEVICE='cpu'

predictor = DefaultPredictor(cfg)

def inference(image):
    print(image.height)

    height = image.height

    img = np.array(image)
    outputs = predictor(img)

    v = Visualizer(img, my_metadata, scale=1.2)
    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
    
    return out.get_image()

title = "DBMDZ Detectron2 Model Demo"
description = "This demo introduces an interactive playground for our trained Detectron2 model. <br>The model was trained on manually annotated segments from digitized books to detect Illustration or Illumination segments on a given page. <br>Minimum score for classification is set to 80%."
article = '<p>Detectron model is available from our repository <a href="">here</a> on the Hugging Face Model Hub.</p>'

gr.Interface(
    inference, 
    [gr.inputs.Image(type="pil", label="Input")], 
    gr.outputs.Image(type="numpy", label="Output"),
    title=title,
    description=description,
    article=article,
    examples=[]).launch()