import torch
import os
import shutil
import tempfile
import gradio as gr
from PIL import Image
from rembg import remove
import sys
import uuid
import subprocess
from glob import glob
import requests
from huggingface_hub import snapshot_download

# Download models
os.makedirs("ckpts", exist_ok=True)

snapshot_download(
    repo_id = "pengHTYX/PSHuman_Unclip_768_6views",
    local_dir = "./ckpts"  
)

os.makedirs("smpl_related", exist_ok=True)
snapshot_download(
    repo_id = "fffiloni/PSHuman-SMPL-related",
    local_dir = "./smpl_related"  
)

# Folder containing example images
examples_folder = "examples"

# Retrieve all file paths in the folder
images_examples = [
    os.path.join(examples_folder, file)
    for file in os.listdir(examples_folder)
    if os.path.isfile(os.path.join(examples_folder, file))
]

def remove_background(input_pil, remove_bg):
    
    # Create a temporary folder for downloaded and processed images
    temp_dir = tempfile.mkdtemp()
    unique_id = str(uuid.uuid4())
    image_path = os.path.join(temp_dir, f'input_image_{unique_id}.png')  
       
    try:
        # Check if input_url is already a PIL Image
        if isinstance(input_pil, Image.Image):
            image = input_pil
        else:
            # Otherwise, assume it's a file path and open it
            image = Image.open(input_pil)
        
        # Flip the image horizontally
        image = image.transpose(Image.FLIP_LEFT_RIGHT)
        
        # Save the resized image
        image.save(image_path)
    except Exception as e:
        shutil.rmtree(temp_dir)
        raise gr.Error(f"Error downloading or saving the image: {str(e)}")

    if remove_bg is True:
        # Run background removal
        removed_bg_path = os.path.join(temp_dir, f'output_image_rmbg_{unique_id}.png')
        try:            
            img = Image.open(image_path)    
            result = remove(img)
            result.save(removed_bg_path)

            # Remove the input image to keep the temp directory clean
            os.remove(image_path)
        except Exception as e:
            shutil.rmtree(temp_dir)
            raise gr.Error(f"Error removing background: {str(e)}")

        return removed_bg_path, temp_dir
    else: 
        return image_path, temp_dir
    
def run_inference(temp_dir, removed_bg_path):
    # Define the inference configuration
    inference_config = "configs/inference-768-6view.yaml"
    pretrained_model = "./ckpts"
    crop_size = 740
    seed = 600
    num_views = 7
    save_mode = "rgb"

    try:
        # Run the inference command
        subprocess.run(
            [
                "python", "inference.py",
                "--config", inference_config,
                f"pretrained_model_name_or_path={pretrained_model}",
                f"validation_dataset.crop_size={crop_size}",
                f"with_smpl=false",
                f"validation_dataset.root_dir={temp_dir}",
                f"seed={seed}",
                f"num_views={num_views}",
                f"save_mode={save_mode}"
            ],
            check=True
        )

        
        # Retrieve the file name without the extension
        removed_bg_file_name = os.path.splitext(os.path.basename(removed_bg_path))[0]

        # List objects in the "out" folder
        out_folder_path = "out"
        out_folder_objects = os.listdir(out_folder_path)
        print(f"Objects in '{out_folder_path}':")
        for obj in out_folder_objects:
            print(f" - {obj}")
        
        # List objects in the "out/{removed_bg_file_name}" folder
        specific_out_folder_path = os.path.join(out_folder_path, removed_bg_file_name)
        if os.path.exists(specific_out_folder_path) and os.path.isdir(specific_out_folder_path):
            specific_out_folder_objects = os.listdir(specific_out_folder_path)
            print(f"\nObjects in '{specific_out_folder_path}':")
            for obj in specific_out_folder_objects:
                print(f" - {obj}")
        else:
            print(f"\nThe folder '{specific_out_folder_path}' does not exist.")
        
        output_video = glob(os.path.join(f"out/{removed_bg_file_name}", "*.mp4"))
        output_objects = glob(os.path.join(f"out/{removed_bg_file_name}", "*.obj"))
        return output_video, output_objects
    
    except subprocess.CalledProcessError as e:
        return f"Error during inference: {str(e)}"

def process_image(input_pil, remove_bg):

    torch.cuda.empty_cache()
    
    # Remove background
    result = remove_background(input_pil, remove_bg)
    
    if isinstance(result, str) and result.startswith("Error"):
        raise gr.Error(f"{result}")  # Return the error message if something went wrong

    removed_bg_path, temp_dir = result  # Unpack only if successful

    # Run inference
    output_video, output_objects = run_inference(temp_dir, removed_bg_path)

    if isinstance(output_video, str) and output_video.startswith("Error"):
        shutil.rmtree(temp_dir)
        raise gr.Error(f"{output_video}")   # Return the error message if inference failed

    
    shutil.rmtree(temp_dir)  # Cleanup temporary folder
    print(output_video)
    torch.cuda.empty_cache()
    return output_video[0], output_objects[0], output_objects[1]

css="""
div#col-container{
    margin: 0 auto;
    max-width: 982px;
}
div#video-out-elm{
    height: 323px;
}
"""
def gradio_interface():
    with gr.Blocks(css=css) as app:
        with gr.Column(elem_id="col-container"):
            gr.Markdown("# PSHuman: Photorealistic Single-image 3D Human Reconstruction using Cross-Scale Multiview Diffusion and Explicit Remeshing")
            gr.HTML("""
            <div style="display:flex;column-gap:4px;">
                <a href="https://github.com/pengHTYX/PSHuman">
                    <img src='https://img.shields.io/badge/GitHub-Repo-blue'>
                </a> 
                <a href="https://penghtyx.github.io/PSHuman/">
                    <img src='https://img.shields.io/badge/Project-Page-green'>
                </a>
    			<a href="https://arxiv.org/pdf/2409.10141">
                    <img src='https://img.shields.io/badge/ArXiv-Paper-red'>
                </a>
                <a href="https://huggingface.co/spaces/fffiloni/PSHuman?duplicate=true">
    				<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-sm.svg" alt="Duplicate this Space">
    			</a>
    			<a href="https://huggingface.co/fffiloni">
    				<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-me-on-HF-sm-dark.svg" alt="Follow me on HF">
    			</a>
            </div>
            """)
            with gr.Group():
                with gr.Row():  
                    with gr.Column(scale=2):
                        
                        input_image = gr.Image(
                            label="Image input", 
                            type="pil",
                            image_mode="RGBA",
                            height=480
                        )
        
                        remove_bg = gr.Checkbox(label="Need to remove BG ?", value=False)
                    
                        submit_button = gr.Button("Process")
                    
                    with gr.Column(scale=4):
                        output_video= gr.Video(label="Output Video", elem_id="video-out-elm")
                        with gr.Row():
                            output_object_mesh = gr.Model3D(label=".OBJ Mesh", height=240)
                            output_object_color = gr.Model3D(label=".OBJ colored", height=240)
    
            gr.Examples(
                examples = examples_folder,
                inputs = [input_image],
                examples_per_page = 11
            )

        submit_button.click(process_image, inputs=[input_image, remove_bg], outputs=[output_video, output_object_mesh, output_object_color])

    return app

# Launch the Gradio app
app = gradio_interface()
app.launch(show_api=False, show_error=True)