import os
import sys
from importlib.util import find_spec

print("Prepare demo ...")
if not os.path.exists("tcl.pth"):
    print("Download TCL checkpoint ...")
    os.system("wget -q https://github.com/kakaobrain/tcl/releases/download/v1.0.0/tcl.pth")

if not (find_spec("mmcv") and find_spec("mmseg")):
    print("Install mmcv & mmseg ...")
    os.system("mim install mmcv-full==1.6.2 mmsegmentation==0.27.0")

if not find_spec("detectron2"):
    print("Install detectron ...")
    os.system("pip install git+https://github.com/facebookresearch/detectron2.git")

sys.path.insert(0, "./tcl/")

print(" -- done.")

import json
from contextlib import ExitStack
import gradio as gr
import torch
from torch.cuda.amp import autocast

from detectron2.evaluation import inference_context

from predictor import build_demo_model


model = build_demo_model()
if torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")

print(f"device: {device}")
model.to(device)


title = "TCL: Text-grounded Contrastive Learning"
description_head = """
<p style='text-align: center'> <a href='https://arxiv.org/abs/2212.00785' target='_blank'>Paper</a> | <a href='https://github.com/kakaobrain/tcl' target='_blank'>Code</a> </p>
"""

description_body = f"""
Gradio Demo for "Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs".

Explore TCL's capability to perform open-world semantic segmentation **without any mask annotations**. Choose from provided examples or upload your own image. Use the query format `bg; class1; class2; ...`, with `;` as the separator, and the `bg` background query being optional (as in the third example).

This demo highlights the strengths and limitations of unsupervised open-world segmentation methods. Although TCL can handle arbitrary concepts, accurately capturing object boundaries without mask annotation remains a challenge.
"""

if device.type == "cpu":
    description_body += f"\nInference takes about 10 seconds since this demo is running on the free CPU device."

description = description_head + description_body

article = """
<p style='text-align: center'><a href='https://arxiv.org/abs/2212.00785' target='_blank'>Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs</a> | <a href='https://github.com/kakaobrain/tcl' target='_blank'>Github Repo</a></p>
"""

voc_examples = [
    ["examples/voc_59.jpg", "bg; cat; dog"],
    ["examples/voc_97.jpg", "bg; car"],
    ["examples/voc_266.jpg", "bg; dog"],
    ["examples/voc_294.jpg", "bg; bird"],
    ["examples/voc_864.jpg", "bg; cat"],
    ["examples/voc_1029.jpg", "bg; bus"],
]

examples = [
    [
        "examples/dogs.jpg",
        "bg; corgi; shepherd",
    ],
    [
        "examples/dogs.jpg",
        "bg; dog",
    ],
    [
        "examples/dogs.jpg",
        "corgi; shepherd; lawn, trees, and fallen leaves",
    ],
    [
        "examples/banana.jpg",
        "bg; banana",
    ],
    [
        "examples/banana.jpg",
        "bg; red banana; green banana; yellow banana",
    ],
    [
        "examples/frodo_sam_gollum.jpg",
        "bg; frodo; gollum; samwise",
    ],
    [
        "examples/frodo_sam_gollum.jpg",
        "bg; rocks; monster; boys with cape"
    ],
    [
        "examples/mb_mj.jpg",
        "bg; marlon brando; michael jackson",
    ],
]

examples = examples + voc_examples


def inference(img, query):
    query = query.split(";")
    query = [v.strip() for v in query]

    with ExitStack() as stack:
        stack.enter_context(inference_context(model))
        stack.enter_context(torch.no_grad())

        with autocast():
            visualized_output = model.forward_vis(img, query)

        return visualized_output


theme = gr.themes.Soft(text_size=gr.themes.sizes.text_md, primary_hue="teal")
with gr.Blocks(title=title, theme=theme) as demo:
    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>" + title + "</h1>")
    gr.Markdown(description)
    input_components = []
    output_components = []

    with gr.Row():
        with gr.Column(scale=4, variant="panel"):
            output_image_gr = gr.outputs.Image(label="Segmentation", type="pil").style(height=300)
            output_components.append(output_image_gr)

            with gr.Row():
                input_gr = gr.inputs.Image(type="pil")
                query_gr = gr.inputs.Textbox(default="", label="Query")
                input_components.extend([input_gr, query_gr])

            with gr.Row():
                clear_btn = gr.Button("Clear")
                submit_btn = gr.Button("Submit", variant="primary")

        inputs = [c for c in input_components if not isinstance(c, gr.State)]
        outputs = [c for c in output_components if not isinstance(c, gr.State)]
        with gr.Column(scale=2):
            examples_handler = gr.Examples(
                examples=examples,
                inputs=inputs,
                outputs=outputs,
                fn=inference,
                cache_examples=True,
                examples_per_page=7,
            )

    gr.Markdown(article)

    submit_btn.click(
        inference,
        input_components,
        output_components,
        scroll_to_output=True,
    )

    clear_btn.click(
        None,
        [],
        (input_components + output_components),
        _js=f"""() => {json.dumps(
                    [component.cleared_value if hasattr(component, "cleared_value") else None
                     for component in input_components + output_components] + (
                        [gr.Column.update(visible=True)]
                    )
                    + ([gr.Column.update(visible=False)])
                )}
                """,
    )

demo.launch()
#  demo.launch(server_name="0.0.0.0", server_port=9718)