import gradio as gr
import re
from utils import read_pcd, render_point_cloud, render_pcd_file, set_seed
from inference.utils import get_legend
from inference.inference import segment_obj, get_heatmap
from huggingface_hub import login
import os


os.chdir("Pointcept/libs/pointops")
os.system("python setup.py install")
os.chdir("../../../")

login(token=os.getenv('hfkey'))

parts_dict = {
    "fireplug": "bonnet of a fireplug,side cap of a fireplug,barrel of a fireplug,base of a fireplug",
    "mickey": "ear,head,arms,hands,body,legs",
    "motorvehicle": "wheel of a motor vehicle,seat of a motor vehicle,handle of a motor vehicle",
    "teddy": "head,body,arms,legs",
    "lamppost": "lighting of a lamppost,pole of a lamppost",
    "shirt": "sleeve of a shirt,collar of a shirt,body of a shirt",
    "capybara": "hat worn by a capybara,head,body,feet",
    "corgi": "head,leg,body,ear",
    "pushcar": "wheel,body,handle",
    "plant": "pot,plant",
    "chair": "back of chair,leg,seat"
}

source_dict = {
    "fireplug":"objaverse",
    "mickey":"objaverse",
    "motorvehicle":"objaverse",
    "teddy":"objaverse",
    "lamppost":"objaverse",
    "shirt":"objaverse",
    "capybara": "wild",
    "corgi": "wild",
    "pushcar": "wild",
    "plant": "wild",
    "chair": "wild"
}

def predict(pcd_path, inference_mode, part_queries):
    set_seed()
    xyz, rgb, normal = read_pcd(pcd_path)
    if inference_mode == "Segmentation":
        parts = [part.strip(" ") for part in re.split(r'[,;.|]', part_queries)]
        seg_rgb = segment_obj(xyz, rgb, normal, parts).cpu().numpy()
        legend = get_legend(parts)
        return render_point_cloud(xyz, seg_rgb, legend=legend)
    elif inference_mode == "Localization":
        heatmap_rgb = get_heatmap(xyz, rgb, normal, part_queries).cpu().numpy()
        return render_point_cloud(xyz, heatmap_rgb)
    else:
        return None

def on_select(evt: gr.SelectData):
    obj_name = evt.value['image']['orig_name'][:-4]
    src = source_dict[obj_name]
    return [f"examples/{src}/{obj_name}.pcd", parts_dict[obj_name]]


with gr.Blocks(theme=gr.themes.Default(text_size="lg", radius_size="none")) as demo:
    gr.HTML(
        '''<h1 text-align="center">Find Any Part in 3D</h1>
        <p style='font-size: 16px;'>This is a demo for Find3D: Find Any Part in 3D! Two modes are supported: segmentation and localization.
        For segmentation mode, please provide multiple part queries in the "queries" text box, in the format of comma-separated string, such as "part1,part2,part3".
        After hitting "Run", the model will segment the object into the provided parts.
        For localization mode, please only provide one query string in the "queries" text box. After hitting "Run", the model will generate a heatmap for the provided query text.
        Please click on the buttons below "Objaverse" and "In the Wild" for some examples. You can also upload your own .pcd files.</p>
        <p style='font-size: 16px;'>Hint: we provide some part names for the examples below.
        When working with your own point cloud, feel free to rephrase the query (e.g. "part" vs "part of a object") to achieve better performance!</p>
        '''
    )

    with gr.Row(variant="panel"):
        with gr.Column(scale=4):
            file_upload = gr.File(
                label="Upload Point Cloud File",
                type="filepath",
                file_types=[".pcd"],
                value="examples/objaverse/lamppost.pcd"
            )
            inference_mode = gr.Radio(
                choices=["Segmentation", "Localization"],
                label="Inference Mode",
                value="Segmentation",
            )
            part_queries = gr.Textbox(
                label="Part Queries",
                value="lighting of a lamppost,pole of a lamppost",
            )
            run_button = gr.Button(
                value="Run",
                variant="primary",
            )

        with gr.Column(scale=4):
            input_image = gr.Image(label="Input Image", visible=False, type='pil', image_mode='RGBA', height=290)
            input_point_cloud = gr.Plot(label="Input Point Cloud")

        with gr.Column(scale=4):
            output_point_cloud = gr.Plot(label="Output Result")

    with gr.Row(variant="panel"):
        with gr.Column(scale=6):
            title = gr.HTML('''<h1 text-align="center">Objaverse</h1>
        <p style='font-size: 16px;'>Online 3D assets from Objaverse!</p>
        ''')
            gallery_objaverse = gr.Gallery([("examples/objaverse/lamppost.jpg", "lamppost"),
                                  ("examples/objaverse/fireplug.jpg", "fireplug"),
                                  ("examples/objaverse/mickey.jpg", "Mickey"),
                                  ("examples/objaverse/motorvehicle.jpg", "motor vehicle"),
                                  ("examples/objaverse/teddy.jpg", "teddy bear"),
                                  ("examples/objaverse/shirt.jpg", "shirt")],
                                  columns=3,
                                  allow_preview=False)
            gallery_objaverse.select(fn=on_select, 
                           inputs=None, 
                           outputs=[file_upload, part_queries])
        with gr.Column(scale=6):
            title = gr.HTML("""<h1 text-align="center">In the Wild</h1>
        <p style='font-size: 16px;'>Challenging in-the-wild reconstructions from iPhone photos & AI-generated images!</p>
        """)
            gallery_wild = gr.Gallery([("examples/wild/capybara.png", "DALLE-capybara"),
                                  ("examples/wild/corgi.jpg", "DALLE-corgi"),
                                  ("examples/wild/plant.jpg", "iPhone-plant"),
                                  ("examples/wild/pushcar.jpg", "iPhone-pushcar"),
                                  ("examples/wild/chair.jpg", "iPhone-chair")],
                                  columns=3,
                                  allow_preview=False)
            gallery_wild.select(fn=on_select, 
                           inputs=None, 
                           outputs=[file_upload, part_queries])

    file_upload.change(
        fn=render_pcd_file,
        inputs=[file_upload],
        outputs=[input_point_cloud],
    )
    run_button.click(
        fn=predict,
        inputs=[file_upload, inference_mode, part_queries],
        outputs=[output_point_cloud],
    )
    demo.load(
        fn=render_pcd_file,
        inputs=[file_upload],
        outputs=[input_point_cloud]) # initialize

demo.launch()