try:
    import detectron2
except:
    import os 
    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
import gradio as gr
import torch
import cv2
import pandas as pd
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.engine import DefaultPredictor
cfg = get_cfg()
cfg.merge_from_file("resnet.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7
cfg.MODEL.DEVICE='cpu'
predictor = DefaultPredictor(cfg)

def inference(image_path):
    # load image
    image = read_image(image_path, format="BGR")

    # Make inference on the image
    outputs = predictor(image)

    metadata = MetadataCatalog.get("custom_dataset_train")
    MetadataCatalog.get("custom_dataset_train").set(thing_classes=['100km/h', '120km/h', '20km/h', '30km/h', '40km/h', '50km/h', '60km/h', '70km/h', '80km/h'])
    v = Visualizer(image, metadata, scale=1)
    out = v.draw_instance_predictions(outputs['instances'])

    # Detection summary table
    cls_idxs = outputs['instances'].pred_classes.numpy()
    thing_classes=['100km/h', '120km/h', '20km/h', '30km/h', '40km/h', '50km/h', '60km/h', '70km/h', '80km/h']
    # get labels from class indices
    labels = [thing_classes[i] for i in cls_idxs]
    scores = outputs['instances'].scores.numpy()
    df = pd.DataFrame({'Detected speed limit': labels, 'Confidence score': scores})
    # Return the visualization as an RGB image
    return out.get_image()[:, :, ::-1], df



examples = ["examples/1.jpg", "examples/2.jpg", "examples/3.jpg"]
with gr.Blocks(theme='gradio/monochrome') as demo:
    gr.Markdown("# Speed Limit Detection demo")
    gr.Markdown("**Author**: *Lu CHEN*")
    gr.Markdown(
        """This interactive demo is based on the Faster R-CNN model for object detection. The model is 
        trained using the [Detectron2](https://github.com/facebookresearch/detectron2) library with a custom
          dataset that I created by combining images from [Tsinghua-Tencent100K](https://cg.cs.tsinghua.edu.cn/traffic-sign/) and [GTSDB](https://benchmark.ini.rub.de/), both of which provide real-world traffic signs captured within the autonomous driving domain.

To use the demo, simply upload an image and click on *"Infer"* to view the following results: 
- **Detection**: outputs of Object Detector
- **Detection summary**: a summary of the detection outputs

You can also select an image from the cached **Examples** to quickly try out. Without clicking *"Infer"*, the cached outputs will be loaded automatically. 
In case the output image seems too small, simply right-click on the image, and choose “Open image in new tab” to visualize it in full size.
    """
    )
    with gr.Row():
        with gr.Column():
            image = gr.Image(type="filepath")
            button = gr.Button("Infer")
            
        with gr.Column():
            detection = gr.Image(label="Output")
            detection_summary = gr.DataFrame(label="Detection summary")
    examples_block = gr.Examples(inputs=image, examples=examples, fn=inference, outputs=[detection, detection_summary], cache_examples=True)
    button.click(fn=inference, inputs=image, outputs=[detection, detection_summary])

demo.launch()