import spaces

import subprocess
import re
from typing import List, Tuple, Optional

import gradio as gr
from datetime import datetime
import os
os.environ["TORCH_CUDNN_SDPA_ENABLED"] = "0,1,2,3,4,5,6,7"
import torch
import numpy as np
import cv2
import matplotlib.pyplot as plt
from PIL import Image, ImageFilter
from sam2.build_sam import build_sam2_video_predictor

from moviepy.editor import ImageSequenceClip

# Description
title = "<center><strong><font size='8'>Efficient Track Anything (EfficientTAM)<font></strong></center>"

description_e = """This is a demo of [Efficient Track Anything (EfficientTAM) Model](https://github.com/yformer/EfficientTAM).
              """

description_p = """# Interactive Video Segmentation
                - Built our demo based on [SAM2-Video-Predictor](https://huggingface.co/spaces/fffiloni/SAM2-Video-Predictor). Thanks to Sylvain Filoni.
                - Instruction
                <ol>
                <li> Upload one video or click one example video</li>
                <li> Click 'include' point type, select the object to segment and track</li>
                <li> Click 'exclude' point type (optional), select the area you want to avoid segmenting and tracking</li>
                <li> Click the 'Segment' button, obtain the mask of the first frame </li>
                <li> Click the 'coarse' level and the 'Track' button, segment and track the object every 15 frames </li>
                <li> Click the corresponding frame to add points on the object for mask refining (optional) </li>
                <li> Click the 'fine' level and the 'Track' button, obtain masklet and masked video </li>
                <li> Click the 'Reset' button to restart </li>
                </ol>
                - Github [link](https://github.com/yformer/EfficientTAM)
              """

# examples
examples = [
    ["examples/videos/cat.mp4"],
    ["examples/videos/coffee.mp4"],
    ["examples/videos/car.mp4"],
    ["examples/videos/chick.mp4"],
    ["examples/videos/cups.mp4"],
    ["examples/videos/dog.mp4"],
    ["examples/videos/goat.mp4"],
    ["examples/videos/juggle.mp4"],
    ["examples/videos/street.mp4"],
    ["examples/videos/yacht.mp4"],
]

default_example = examples[0]

def get_video_fps(video_path):
    # Open the video file
    cap = cv2.VideoCapture(video_path)
    
    if not cap.isOpened():
        print("Error: Could not open video.")
        return None
    
    # Get the FPS of the video
    fps = cap.get(cv2.CAP_PROP_FPS)

    return fps

def clear_points(image):
    # we clean all
    return [
        image,   # first_frame_path
        gr.State([]),      # tracking_points
        gr.State([]),      # trackings_input_label
        image,   # points_map
    ]

def preprocess_video_in(video_path):
    if video_path is None:
        return None, gr.State([]), gr.State([]), None, None, None, None, None, None, gr.update(open=True)

    # Generate a unique ID based on the current date and time
    unique_id = datetime.now().strftime('%Y%m%d%H%M%S')
    
    # Set directory with this ID to store video frames 
    extracted_frames_output_dir = f'frames_{unique_id}'
    
    # Create the output directory
    os.makedirs(extracted_frames_output_dir, exist_ok=True)

    ### Process video frames ###
    # Open the video file
    cap = cv2.VideoCapture(video_path)
    
    if not cap.isOpened():
        print("Error: Could not open video.")
        return None

    # Get the frames per second (FPS) of the video
    fps = cap.get(cv2.CAP_PROP_FPS)
    
    # Calculate the number of frames to process (10 seconds of video)
    max_frames = int(fps * 10)
    
    frame_number = 0
    first_frame = None
    
    while True:
        ret, frame = cap.read()
        if not ret or frame_number >= max_frames:
            break
        
        # Format the frame filename as '00000.jpg'
        frame_filename = os.path.join(extracted_frames_output_dir, f'{frame_number:05d}.jpg')
        
        # Save the frame as a JPEG file
        cv2.imwrite(frame_filename, frame)
        
        # Store the first frame
        if frame_number == 0:
            first_frame = frame_filename
        
        frame_number += 1
    
    # Release the video capture object
    cap.release()
    
    # scan all the JPEG frame names in this directory
    scanned_frames = [
        p for p in os.listdir(extracted_frames_output_dir)
        if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
    ]
    scanned_frames.sort(key=lambda p: int(os.path.splitext(p)[0]))
    # print(f"SCANNED_FRAMES: {scanned_frames}")
    
    return [
        first_frame,           # first_frame_path
        gr.State([]),          # tracking_points
        gr.State([]),          # trackings_input_label
        first_frame,           # input_first_frame_image
        first_frame,           # points_map
        extracted_frames_output_dir,            # video_frames_dir
        scanned_frames,        # scanned_frames
        None,                  # stored_inference_state
        None,                  # stored_frame_names
        gr.update(open=False)  # video_in_drawer
    ]


def get_point(point_type, tracking_points, trackings_input_label, input_first_frame_image, evt: gr.SelectData):
    if input_first_frame_image is None:
        return gr.State([]), gr.State([]), None
    print(f"You selected {evt.value} at {evt.index} from {evt.target}")

    tracking_points.value.append(evt.index)
    print(f"TRACKING POINT: {tracking_points.value}")

    if point_type == "include":
        trackings_input_label.value.append(1)
    elif point_type == "exclude":
        trackings_input_label.value.append(0)
    print(f"TRACKING INPUT LABEL: {trackings_input_label.value}")
    
    # Open the image and get its dimensions
    transparent_background = Image.open(input_first_frame_image).convert('RGBA')
    w, h = transparent_background.size
    
    # Define the circle radius as a fraction of the smaller dimension
    fraction = 0.02  # You can adjust this value as needed
    radius = int(fraction * min(w, h))
    
    # Create a transparent layer to draw on
    transparent_layer = np.zeros((h, w, 4), dtype=np.uint8)
    
    for index, track in enumerate(tracking_points.value):
        if trackings_input_label.value[index] == 1:
            cv2.circle(transparent_layer, track, radius, (0, 255, 0, 255), -1)
        else:
            cv2.circle(transparent_layer, track, radius, (255, 0, 0, 255), -1)

    # Convert the transparent layer back to an image
    transparent_layer = Image.fromarray(transparent_layer, 'RGBA')
    selected_point_map = Image.alpha_composite(transparent_background, transparent_layer)
    
    return tracking_points, trackings_input_label, selected_point_map

DEVICE = 'cuda'
# use bfloat16 for the entire notebook
torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
if torch.cuda.get_device_properties(0).major >= 8:
    # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True
    
def show_mask(mask, ax, obj_id=None, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        cmap = plt.get_cmap("tab10")
        cmap_idx = 0 if obj_id is None else obj_id
        color = np.array([*cmap(cmap_idx)[:3], 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.axis('off')
    ax.imshow(mask_image)


def show_points(coords, labels, ax, marker_size=200):
    pos_points = coords[labels==1]
    neg_points = coords[labels==0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)

def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2))    


def load_model(checkpoint):
    # Load model accordingly to user's choice
    if checkpoint == "efficienttam_s":
        efficienttam_checkpoint = "./checkpoints/efficienttam_s.pt"
        model_cfg = "efficienttam_s.yaml"
        return [efficienttam_checkpoint, model_cfg]
    elif checkpoint == "efficienttam_ti":
        efficienttam_checkpoint = "./checkpoints/efficienttam_ti.pt"
        model_cfg = "efficienttam-ti.yaml"
        return [efficienttam_checkpoint, model_cfg]
    elif checkpoint == "efficienttam_s_512x512":
        efficienttam_checkpoint = "./checkpoints/efficienttam_s_512x512.pt"
        model_cfg = "efficienttam_s_512x512.yaml"
        return [efficienttam_checkpoint, model_cfg]
    elif checkpoint == "efficienttam_ti_512x512":
        efficienttam_checkpoint = "./checkpoints/efficienttam_ti_512x512.pt"
        model_cfg = "efficienttam_ti_512x512.yaml"
        return [efficienttam_checkpoint, model_cfg]
    elif checkpoint == "efficienttam_s_1":
        efficienttam_checkpoint = "./checkpoints/efficienttam_s_1.pt"
        model_cfg = "efficienttam_s_1.yaml"
        return [efficienttam_checkpoint, model_cfg]
    elif checkpoint == "efficienttam_s_2":
        efficienttam_checkpoint = "./checkpoints/efficienttam_s_2.pt"
        model_cfg = "efficienttam_s_2.yaml"
        return [efficienttam_checkpoint, model_cfg]
    elif checkpoint == "efficienttam_ti_1":
        efficienttam_checkpoint = "./checkpoints/efficienttam_ti_1.pt"
        model_cfg = "efficienttam_ti_1.yaml"
        return [efficienttam_checkpoint, model_cfg]
    elif checkpoint == "efficienttam_ti_2":
        efficienttam_checkpoint = "./checkpoints/efficienttam_ti_2.pt"
        model_cfg = "efficienttam_ti_2.yaml"
        return [efficienttam_checkpoint, model_cfg]
    else:
        efficienttam_checkpoint = "./checkpoints/demo/efficienttam_s.pt"
        model_cfg = "efficienttam_s.yaml"
        return [efficienttam_checkpoint, model_cfg]

@spaces.GPU
@torch.inference_mode()
@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
def get_mask_sam_process(
    stored_inference_state,
    input_first_frame_image, 
    checkpoint, 
    tracking_points, 
    trackings_input_label, 
    video_frames_dir, # extracted_frames_output_dir defined in 'preprocess_video_in' function
    scanned_frames, 
    working_frame: str = None, # current frame being added points
    available_frames_to_check: List[str] = [],
):

    if len(tracking_points.value) == 0:
        return gr.update(visible=False), None, gr.State(), None, stored_inference_state, working_frame
    # get model and model config paths
    print(f"USER CHOSEN CHECKPOINT: {checkpoint}")
    sam2_checkpoint, model_cfg = load_model(checkpoint)
    print("MODEL LOADED")

    # set predictor 
    predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device="cuda")
    print("PREDICTOR READY")

    # `video_dir` a directory of JPEG frames with filenames like `<frame_index>.jpg`
    # print(f"STATE FRAME OUTPUT DIRECTORY: {video_frames_dir}")
    video_dir = video_frames_dir
    
    # scan all the JPEG frame names in this directory
    frame_names = scanned_frames

    # print(f"STORED INFERENCE STEP: {stored_inference_state}")
    if stored_inference_state is None:
        # Init SAM2 inference_state
        inference_state = predictor.init_state(video_path=video_dir, device="cuda")
        print("NEW INFERENCE_STATE INITIATED")
    else:
        inference_state = stored_inference_state

    # segment and track one object
    # predictor.reset_state(inference_state) # if any previous tracking, reset

    ### HANDLING WORKING FRAME
    # new_working_frame = None
    # Add new point
    if working_frame is None:
        ann_frame_idx = 0  # the frame index we interact with, 0 if it is the first frame
        working_frame = "frame_0.jpg"
    else:
        # Use a regular expression to find the integer
        match = re.search(r'frame_(\d+)', working_frame)
        if match:
            # Extract the integer from the match
            frame_number = int(match.group(1))
            ann_frame_idx = frame_number
            
    print(f"NEW_WORKING_FRAME PATH: {working_frame}")
    
    ann_obj_id = 1  # give a unique id to each object we interact with (it can be any integers)
    
    # Let's add a positive click at (x, y) = (210, 350) to get started
    points = np.array(tracking_points.value, dtype=np.float32)
    # for labels, `1` means positive click and `0` means negative click
    labels = np.array(trackings_input_label.value, np.int32)
    _, out_obj_ids, out_mask_logits = predictor.add_new_points(
        inference_state=inference_state,
        frame_idx=ann_frame_idx,
        obj_id=ann_obj_id,
        points=points,
        labels=labels,
    )

    # Create the plot
    plt.figure(figsize=(12, 8))
    plt.title(f"frame {ann_frame_idx}")
    plt.imshow(Image.open(os.path.join(video_dir, frame_names[ann_frame_idx])))
    show_points(points, labels, plt.gca())
    show_mask((out_mask_logits[0] > 0.0).cpu().numpy(), plt.gca(), obj_id=out_obj_ids[0])
    
    # Save the plot as a JPG file
    first_frame_output_filename = "output_first_frame.jpg"
    plt.savefig(first_frame_output_filename, format='jpg')
    plt.close()
    torch.cuda.empty_cache()

    # Assuming available_frames_to_check.value is a list
    if working_frame not in available_frames_to_check:
        available_frames_to_check.append(working_frame)
        print(available_frames_to_check)
    
    return gr.update(visible=True), "output_first_frame.jpg", frame_names, predictor, inference_state, gr.update(choices=available_frames_to_check, value=working_frame, visible=True)

@spaces.GPU
@torch.inference_mode()
@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
def propagate_to_all(tracking_points, video_in, checkpoint, stored_inference_state, stored_frame_names, video_frames_dir, vis_frame_type, available_frames_to_check, working_frame):
    if tracking_points is None or video_in is None or checkpoint is None or stored_inference_state is None:
        return gr.update(value=None), gr.update(value=None), gr.update(value=None), available_frames_to_check, gr.update(visible=False)
    #### PROPAGATION ####
    sam2_checkpoint, model_cfg = load_model(checkpoint)
    predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device="cuda")
    
    inference_state = stored_inference_state
    frame_names = stored_frame_names
    video_dir = video_frames_dir
    
    # Define a directory to save the JPEG images
    frames_output_dir = "frames_output_images"
    os.makedirs(frames_output_dir, exist_ok=True)
    
    # Initialize a list to store file paths of saved images
    jpeg_images = []

    # run propagation throughout the video and collect the results in a dict
    video_segments = {}  # video_segments contains the per-frame segmentation results
    print("starting propagate_in_video")
    for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(inference_state):
        video_segments[out_frame_idx] = {
            out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy()
            for i, out_obj_id in enumerate(out_obj_ids)
        }    
    
    # obtain the segmentation results every few frames
    if vis_frame_type == "coarse":
        vis_frame_stride = 15
    elif vis_frame_type == "fine":
        vis_frame_stride = 1
    
    plt.close("all")
    for out_frame_idx in range(0, len(frame_names), vis_frame_stride):
        plt.figure(figsize=(6, 4))
        plt.title(f"frame {out_frame_idx}")
        plt.imshow(Image.open(os.path.join(video_dir, frame_names[out_frame_idx])))
        for out_obj_id, out_mask in video_segments[out_frame_idx].items():
            show_mask(out_mask, plt.gca(), obj_id=out_obj_id)

        # Define the output filename and save the figure as a JPEG file
        output_filename = os.path.join(frames_output_dir, f"frame_{out_frame_idx}.jpg")
        plt.savefig(output_filename, format='jpg')
    
        # Close the plot
        plt.close()

        # Append the file path to the list
        jpeg_images.append(output_filename)

        if f"frame_{out_frame_idx}.jpg" not in available_frames_to_check:
            available_frames_to_check.append(f"frame_{out_frame_idx}.jpg")

    torch.cuda.empty_cache()
    print(f"JPEG_IMAGES: {jpeg_images}")

    if vis_frame_type == "coarse":
        return gr.update(value=jpeg_images), gr.update(value=None), gr.update(choices=available_frames_to_check, value=working_frame, visible=True), available_frames_to_check, gr.update(visible=True)
    elif vis_frame_type == "fine":
        # Create a video clip from the image sequence
        original_fps = get_video_fps(video_in)
        fps = original_fps  # Frames per second
        total_frames = len(jpeg_images)
        clip = ImageSequenceClip(jpeg_images, fps=fps)
        # Write the result to a file
        final_vid_output_path = "output_video.mp4"
        
        # Write the result to a file
        clip.write_videofile(
            final_vid_output_path,
            codec='libx264'
        )
        
        return gr.update(value=None), gr.update(value=final_vid_output_path), working_frame, available_frames_to_check, gr.update(visible=True)

def update_ui(vis_frame_type):
    if vis_frame_type == "coarse":
        return gr.update(visible=True), gr.update(visible=False)
    elif vis_frame_type == "fine":
        return gr.update(visible=False), gr.update(visible=True)

def switch_working_frame(working_frame, scanned_frames, video_frames_dir):
    new_working_frame = None
    if working_frame == None:
        new_working_frame = os.path.join(video_frames_dir, scanned_frames[0])
        
    else:
        # Use a regular expression to find the integer
        match = re.search(r'frame_(\d+)', working_frame)
        if match:
            # Extract the integer from the match
            frame_number = int(match.group(1))
            ann_frame_idx = frame_number
            new_working_frame = os.path.join(video_frames_dir, scanned_frames[ann_frame_idx])
    return gr.State([]), gr.State([]), new_working_frame, new_working_frame

def reset_propagation(first_frame_path, predictor, stored_inference_state):
    predictor.reset_state(stored_inference_state)
    # print(f"RESET State: {stored_inference_state} ")
    return first_frame_path, gr.State([]), gr.State([]), gr.update(value=None, visible=False), stored_inference_state, None, ["frame_0.jpg"], first_frame_path, "frame_0.jpg", gr.update(visible=False)
    
with gr.Blocks() as demo:
    first_frame_path = gr.State()
    tracking_points = gr.State([])
    trackings_input_label = gr.State([])
    video_frames_dir = gr.State()
    scanned_frames = gr.State()
    loaded_predictor = gr.State()
    stored_inference_state = gr.State()
    stored_frame_names = gr.State()
    available_frames_to_check = gr.State([])
    with gr.Column():
        # Title
        gr.Markdown(title)
        with gr.Row():
            
            with gr.Column():
                # Instructions
                gr.Markdown(description_p)

                # video_exp = gr.Video(label="Input Example", format="mp4", visible=False)
                with gr.Accordion("Input Video", open=True) as video_in_drawer:
                    video_in = gr.Video(label="Input Video", format="mp4")
                
                with gr.Row():
                    point_type = gr.Radio(label="point type", choices=["include", "exclude"], value="include", scale=2)
                    clear_points_btn = gr.Button("Clear Points", scale=1)
                
                input_first_frame_image = gr.Image(label="input image", interactive=False, type="filepath", visible=False)                 
                
                points_map = gr.Image(
                    label="Frame with Point Prompt", 
                    type="filepath",
                    interactive=False
                )

                with gr.Row():
                    checkpoint = gr.Dropdown(label="Checkpoint", choices=["efficienttam_s", "efficienttam_ti", "efficienttam_s_512x512", "efficienttam_ti_512x512", "efficienttam_s_1", "efficienttam_s_2", "efficienttam_ti_1", "efficienttam_ti_2"], value="efficienttam_s")
                    submit_btn = gr.Button("Segment", size="lg")
                
            
            with gr.Column():
                gr.Markdown("# Try some of the examples below ⬇️")
                gr.Examples(
                    examples=examples,
                    inputs=[video_in,],
                )
                gr.Markdown('\n\n\n\n\n\n\n\n\n\n\n')
                gr.Markdown('\n\n\n\n\n\n\n\n\n\n\n')
                gr.Markdown('\n\n\n\n\n\n\n\n\n\n\n')
                with gr.Row():
                    working_frame = gr.Dropdown(label="Frame ID", choices=[""], value=None, visible=False, allow_custom_value=False, interactive=True)
                    change_current = gr.Button("change current", visible=False)
                output_result = gr.Image(label="Reference Mask")
                with gr.Row():
                    vis_frame_type = gr.Radio(label="Track level", choices=["coarse", "fine"], value="coarse", scale=2)
                    propagate_btn = gr.Button("Track", scale=1)
                reset_prpgt_brn = gr.Button("Reset", visible=False)
                output_propagated = gr.Gallery(label="Masklets", columns=4, visible=False)
                output_video = gr.Video(visible=False)
    
    

    # When new video is uploaded
    video_in.upload(
        fn = preprocess_video_in, 
        inputs = [video_in], 
        outputs = [
            first_frame_path, 
            tracking_points, # update Tracking Points in the gr.State([]) object
            trackings_input_label, # update Tracking Labels in the gr.State([]) object
            input_first_frame_image, # hidden component used as ref when clearing points
            points_map, # Image component where we add new tracking points
            video_frames_dir, # Array where frames from video_in are deep stored
            scanned_frames, # Scanned frames by EfficientTAM
            stored_inference_state, # EfficientTAM inference state
            stored_frame_names, # 
            video_in_drawer, # Accordion to hide uploaded video player
        ],
        queue = False
    )

    video_in.change(
        fn = preprocess_video_in, 
        inputs = [video_in], 
        outputs = [
            first_frame_path, 
            tracking_points, # update Tracking Points in the gr.State([]) object
            trackings_input_label, # update Tracking Labels in the gr.State([]) object
            input_first_frame_image, # hidden component used as ref when clearing points
            points_map, # Image component where we add new tracking points
            video_frames_dir, # Array where frames from video_in are deep stored
            scanned_frames, # Scanned frames by EfficientTAM
            stored_inference_state, # EfficientTAM inference state
            stored_frame_names, # 
            video_in_drawer, # Accordion to hide uploaded video player
        ],
        queue = False
    )

    
    # triggered when we click on image to add new points
    points_map.select(
        fn = get_point, 
        inputs = [
            point_type, # "include" or "exclude"
            tracking_points, # get tracking_points values
            trackings_input_label, # get tracking label values
            input_first_frame_image, # gr.State() first frame path
        ], 
        outputs = [
            tracking_points, # updated with new points
            trackings_input_label, # updated with corresponding labels
            points_map, # updated image with points
        ], 
        queue = False
    )

    # Clear every points clicked and added to the map
    clear_points_btn.click(
        fn = clear_points,
        inputs = input_first_frame_image, # we get the untouched hidden image
        outputs = [
            first_frame_path, 
            tracking_points, 
            trackings_input_label, 
            points_map, 
        ],
        queue=False
    )

    
    change_current.click(
        fn = switch_working_frame,
        inputs = [working_frame, scanned_frames, video_frames_dir],
        outputs = [tracking_points, trackings_input_label, input_first_frame_image, points_map],
        queue=False
    )
    

    submit_btn.click(
        fn = get_mask_sam_process,
        inputs = [
            stored_inference_state,
            input_first_frame_image, 
            checkpoint, 
            tracking_points, 
            trackings_input_label, 
            video_frames_dir, 
            scanned_frames, 
            working_frame,
            available_frames_to_check,
        ],
        outputs = [
            change_current,
            output_result, 
            stored_frame_names, 
            loaded_predictor,
            stored_inference_state,
            working_frame,
        ],
        concurrency_limit=10,
        queue=False
    )

    reset_prpgt_brn.click(
        fn = reset_propagation,
        inputs = [first_frame_path, loaded_predictor, stored_inference_state],
        outputs = [points_map, tracking_points, trackings_input_label, output_propagated, stored_inference_state, output_result, available_frames_to_check, input_first_frame_image, working_frame, reset_prpgt_brn],
        queue=False
    )

    propagate_btn.click(
        fn = update_ui,
        inputs = [vis_frame_type],
        outputs = [output_propagated, output_video],
        queue=False
    ).then(
        fn = propagate_to_all,
        inputs = [tracking_points, video_in, checkpoint, stored_inference_state, stored_frame_names, video_frames_dir, vis_frame_type, available_frames_to_check, working_frame],
        outputs = [output_propagated, output_video, working_frame, available_frames_to_check, reset_prpgt_brn],
        concurrency_limit=10,
        queue=False
    )

demo.queue()
demo.launch()