import argparse
import os
import random
from collections import defaultdict

import cv2
import re

import numpy as np
from PIL import Image
import torch
import html
import gradio as gr

import torchvision.transforms as T
import torch.backends.cudnn as cudnn

from minigpt4.common.config import Config

from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Conversation, SeparatorStyle, Chat

# imports modules for registration
from minigpt4.datasets.builders import *
from minigpt4.models import *
from minigpt4.processors import *
from minigpt4.runners import *
from minigpt4.tasks import *


def parse_args():
    parser = argparse.ArgumentParser(description="Demo")
    parser.add_argument("--cfg-path", default='eval_configs/demo.yaml',
                        help="path to configuration file.")
    parser.add_argument(
        "--options",
        nargs="+",
        help="override some settings in the used config, the key-value pair "
             "in xxx=yyy format will be merged into config file (deprecate), "
             "change to --cfg-options instead.",
    )
    args = parser.parse_args()
    return args


random.seed(42)
np.random.seed(42)
torch.manual_seed(42)

cudnn.benchmark = False
cudnn.deterministic = True

print('Initializing Chat')
args = parse_args()
cfg = Config(args)

device = 'cuda'

model_config = cfg.model_cfg

print("model_config:", model_config)
model_cls = registry.get_model_class(model_config.arch)
model = model_cls.from_config(model_config).to(device)
bounding_box_size = 100

vis_processor_cfg = cfg.datasets_cfg.feature_face_caption.vis_processor.train
vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)

model = model.eval()

CONV_VISION = Conversation(
    system="",
    roles=(r"<s>[INST] ", r" [/INST]"),
    messages=[],
    offset=2,
    sep_style=SeparatorStyle.SINGLE,
    sep="",
)


def extract_substrings(string):
    # first check if there is no-finished bracket
    index = string.rfind('}')
    if index != -1:
        string = string[:index + 1]

    pattern = r'<p>(.*?)\}(?!<)'
    matches = re.findall(pattern, string)
    substrings = [match for match in matches]

    return substrings


def is_overlapping(rect1, rect2):
    x1, y1, x2, y2 = rect1
    x3, y3, x4, y4 = rect2
    return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)


def computeIoU(bbox1, bbox2):
    x1, y1, x2, y2 = bbox1
    x3, y3, x4, y4 = bbox2
    intersection_x1 = max(x1, x3)
    intersection_y1 = max(y1, y3)
    intersection_x2 = min(x2, x4)
    intersection_y2 = min(y2, y4)
    intersection_area = max(0, intersection_x2 - intersection_x1 + 1) * max(0, intersection_y2 - intersection_y1 + 1)
    bbox1_area = (x2 - x1 + 1) * (y2 - y1 + 1)
    bbox2_area = (x4 - x3 + 1) * (y4 - y3 + 1)
    union_area = bbox1_area + bbox2_area - intersection_area
    iou = intersection_area / union_area
    return iou


def save_tmp_img(visual_img):
    file_name = "".join([str(random.randint(0, 9)) for _ in range(5)]) + ".jpg"
    file_path = "/tmp/gradio" + file_name
    visual_img.save(file_path)
    return file_path


def mask2bbox(mask):
    if mask is None:
        return ''
    mask = mask.resize([100, 100], resample=Image.NEAREST)
    mask = np.array(mask)[:, :, 0]

    rows = np.any(mask, axis=1)
    cols = np.any(mask, axis=0)

    if rows.sum():
        # Get the top, bottom, left, and right boundaries
        rmin, rmax = np.where(rows)[0][[0, -1]]
        cmin, cmax = np.where(cols)[0][[0, -1]]
        bbox = '{{<{}><{}><{}><{}>}}'.format(cmin, rmin, cmax, rmax)
    else:
        bbox = ''

    return bbox


def escape_markdown(text):
    # List of Markdown special characters that need to be escaped
    md_chars = ['<', '>']

    # Escape each special character
    for char in md_chars:
        text = text.replace(char, '\\' + char)

    return text


def reverse_escape(text):
    md_chars = ['\\<', '\\>']

    for char in md_chars:
        text = text.replace(char, char[1:])

    return text


colors = [
    (255, 0, 0),
    (0, 255, 0),
    (0, 0, 255),
    (210, 210, 0),
    (255, 0, 255),
    (0, 255, 255),
    (114, 128, 250),
    (0, 165, 255),
    (0, 128, 0),
    (144, 238, 144),
    (238, 238, 175),
    (255, 191, 0),
    (0, 128, 0),
    (226, 43, 138),
    (255, 0, 255),
    (0, 215, 255),
]

color_map = {
    f"{color_id}": f"#{hex(color[2])[2:].zfill(2)}{hex(color[1])[2:].zfill(2)}{hex(color[0])[2:].zfill(2)}" for
    color_id, color in enumerate(colors)
}

used_colors = colors

def get_first_frame(video_path):
    cap = cv2.VideoCapture(video_path)
    
    if not cap.isOpened():
        print("Error: Cannot open video.")
        return None

    ret, frame = cap.read()
    cap.release()

    if ret:
        return frame
    else:
        print("Error: Cannot read frame from video.")
        return None

def visualize_all_bbox_together(image, generation):
    if image is None:
        return None, ''
    
    if isinstance(image, str):  # is a image path
        raw_image = get_first_frame(image)
        frame_rgb = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB)
        image = Image.fromarray(frame_rgb)

    generation = html.unescape(generation)

    image_width, image_height = image.size
    image = image.resize([500, int(500 / image_width * image_height)])
    image_width, image_height = image.size

    string_list = extract_substrings(generation)
    if string_list:  # it is grounding or detection
        mode = 'all'
        entities = defaultdict(list)
        i = 0
        j = 0
        for string in string_list:
            try:
                obj, string = string.split('</p>')
            except ValueError:
                print('wrong string: ', string)
                continue
            bbox_list = string.split('<delim>')
            flag = False
            for bbox_string in bbox_list:
                integers = re.findall(r'-?\d+', bbox_string)
                if len(integers) == 4:
                    x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
                    left = x0 / bounding_box_size * image_width
                    bottom = y0 / bounding_box_size * image_height
                    right = x1 / bounding_box_size * image_width
                    top = y1 / bounding_box_size * image_height

                    entities[obj].append([left, bottom, right, top])

                    j += 1
                    flag = True
            if flag:
                i += 1
    else:
        integers = re.findall(r'-?\d+', generation)

        if len(integers) == 4:  # it is refer
            mode = 'single'

            entities = list()
            x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
            left = x0 / bounding_box_size * image_width
            bottom = y0 / bounding_box_size * image_height
            right = x1 / bounding_box_size * image_width
            top = y1 / bounding_box_size * image_height
            entities.append([left, bottom, right, top])
        else:
            # don't detect any valid bbox to visualize
            return None, ''

    if len(entities) == 0:
        return None, ''

    if isinstance(image, Image.Image):
        image_h = image.height
        image_w = image.width
        image = np.array(image)

    elif isinstance(image, str):
        if os.path.exists(image):
            pil_img = Image.open(image).convert("RGB")
            image = np.array(pil_img)[:, :, [2, 1, 0]]
            image_h = pil_img.height
            image_w = pil_img.width
        else:
            raise ValueError(f"invaild image path, {image}")
    elif isinstance(image, torch.Tensor):

        image_tensor = image.cpu()
        reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None]
        reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None]
        image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
        pil_img = T.ToPILImage()(image_tensor)
        image_h = pil_img.height
        image_w = pil_img.width
        image = np.array(pil_img)[:, :, [2, 1, 0]]
    else:
        raise ValueError(f"invaild image format, {type(image)} for {image}")

    indices = list(range(len(entities)))

    new_image = image.copy()

    previous_bboxes = []
    # size of text
    text_size = 0.5
    # thickness of text
    text_line = 1  # int(max(1 * min(image_h, image_w) / 512, 1))
    box_line = 2
    (c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
    base_height = int(text_height * 0.675)
    text_offset_original = text_height - base_height
    text_spaces = 2

    # num_bboxes = sum(len(x[-1]) for x in entities)
    used_colors = colors  # random.sample(colors, k=num_bboxes)

    color_id = -1
    for entity_idx, entity_name in enumerate(entities):
        if mode == 'single' or mode == 'identify':
            bboxes = entity_name
            bboxes = [bboxes]
        else:
            bboxes = entities[entity_name]
        color_id += 1
        for bbox_id, (x1_norm, y1_norm, x2_norm, y2_norm) in enumerate(bboxes):
            skip_flag = False
            orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm), int(y1_norm), int(x2_norm), int(y2_norm)

            color = used_colors[entity_idx % len(used_colors)]  # tuple(np.random.randint(0, 255, size=3).tolist())
            new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line)

            if mode == 'all':
                l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1

                x1 = orig_x1 - l_o
                y1 = orig_y1 - l_o

                if y1 < text_height + text_offset_original + 2 * text_spaces:
                    y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces
                    x1 = orig_x1 + r_o

                # add text background
                (text_width, text_height), _ = cv2.getTextSize(f"  {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size,
                                                               text_line)
                text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (
                            text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1

                for prev_bbox in previous_bboxes:
                    if computeIoU((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']) > 0.95 and \
                            prev_bbox['phrase'] == entity_name:
                        skip_flag = True
                        break
                    while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']):
                        text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
                        text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
                        y1 += (text_height + text_offset_original + 2 * text_spaces)

                        if text_bg_y2 >= image_h:
                            text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
                            text_bg_y2 = image_h
                            y1 = image_h
                            break
                if not skip_flag:
                    alpha = 0.5
                    for i in range(text_bg_y1, text_bg_y2):
                        for j in range(text_bg_x1, text_bg_x2):
                            if i < image_h and j < image_w:
                                if j < text_bg_x1 + 1.35 * c_width:
                                    # original color
                                    bg_color = color
                                else:
                                    # white
                                    bg_color = [255, 255, 255]
                                new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(
                                    np.uint8)

                    cv2.putText(
                        new_image, f"  {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces),
                        cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
                    )

                    previous_bboxes.append(
                        {'bbox': (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), 'phrase': entity_name})

    if mode == 'all':
        def color_iterator(colors):
            while True:
                for color in colors:
                    yield color

        color_gen = color_iterator(colors)

        # Add colors to phrases and remove <p></p>
        def colored_phrases(match):
            phrase = match.group(1)
            color = next(color_gen)
            return f'<span style="color:rgb{color}">{phrase}</span>'

        generation = re.sub(r'{<\d+><\d+><\d+><\d+>}|<delim>', '', generation)
        generation_colored = re.sub(r'<p>(.*?)</p>', colored_phrases, generation)
    else:
        generation_colored = ''

    pil_image = Image.fromarray(new_image)
    return pil_image, generation_colored


def gradio_reset(chat_state, img_list):
    if chat_state is not None:
        chat_state.messages = []
    if img_list is not None:
        img_list = []
    return None, gr.update(value=None, interactive=True), gr.update(placeholder='Upload your image and chat',
                                                                    interactive=True), chat_state, img_list


def image_upload_trigger(upload_flag, replace_flag, img_list):
    # set the upload flag to true when receive a new image.
    # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
    upload_flag = 1
    if img_list:
        replace_flag = 1
    return upload_flag, replace_flag


def example_trigger(text_input, image, upload_flag, replace_flag, img_list):
    # set the upload flag to true when receive a new image.
    # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
    upload_flag = 1
    if img_list or replace_flag == 1:
        replace_flag = 1

    return upload_flag, replace_flag


def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag, replace_flag):    
    print("+++gradio_ask+++")
    
    if len(user_message) == 0:
        text_box_show = 'Input should not be empty!'
    else:
        text_box_show = ''

    print('user_message:', user_message)
    print('chatbot:', chatbot)
    print('chat_state:', chat_state)


    if isinstance(gr_img, dict):
        gr_img, mask = gr_img['image'], gr_img['mask']
    else:
        mask = None

    if '[identify]' in user_message:
        # check if user provide bbox in the text input
        integers = re.findall(r'-?\d+', user_message)
        if len(integers) != 4:  # no bbox in text
            bbox = mask2bbox(mask)
            user_message = user_message + bbox

    if chat_state is None:
        chat_state = CONV_VISION.copy()

    if upload_flag:
        if replace_flag:
            chat_state = CONV_VISION.copy()  # new image, reset everything
            replace_flag = 0
            chatbot = []
        img_list = []
        llm_message = chat.upload_img(gr_img, chat_state, img_list)
        upload_flag = 0

    chat.ask(user_message, chat_state)
    print('user_message: ', user_message)
    print('chat_state: ', chat_state)

    chatbot = chatbot + [[user_message, None]]

    if '[identify]' in user_message:
        visual_img, _ = visualize_all_bbox_together(gr_img, user_message)
        if visual_img is not None:
            file_path = save_tmp_img(visual_img)
            chatbot = chatbot + [[(file_path,), None]]

    return text_box_show, chatbot, chat_state, img_list, upload_flag, replace_flag


def gradio_answer(chatbot, chat_state, img_list, temperature):
    print("--gradio_answer--")
    # print('img_list: ', img_list)
    llm_message = chat.answer(conv=chat_state,
                              img_list=img_list,
                              temperature=temperature,
                              max_new_tokens=500,
                              max_length=2000)[0]
    chatbot[-1][1] = llm_message
    print('gradio_answer: ', llm_message)

    return chatbot, chat_state

def process_english_text(text):
    if len(text) < 2:
        return text
    text = text[0].upper() + text[1:]

    sentences = text.split('. ')
    corrected_sentences = [s.capitalize() for s in sentences]
    text = '. '.join(corrected_sentences)

    if text.endswith(','):
        text = text[:-1]
    if not text.endswith('.'):
        text += '.'

    return text


def gradio_stream_answer(chatbot, chat_state, img_list, temperature):
    print('---gradio_stream_answer---')
    if len(img_list) > 0:
        if not isinstance(img_list[0], torch.Tensor):
            chat.encode_img(img_list)
    print(chat)
    streamer = chat.stream_answer(conv=chat_state,
                                  img_list=img_list,
                                  temperature=temperature,
                                  max_new_tokens=500,
                                  max_length=2000)
    output = ''
    print('streamer:', streamer)
    for new_output in streamer:
        escapped = escape_markdown(new_output)
        output += escapped
        chatbot[-1][1] = output
        chatbot[-1][1] = process_english_text(chatbot[-1][1])
        yield chatbot, chat_state
    chat_state.messages[-1][1] = '</s>'
    print('output:', output)
    return chatbot, chat_state


def gradio_visualize(chatbot, gr_img):
    if isinstance(gr_img, dict):
        gr_img, mask = gr_img['image'], gr_img['mask']

    unescaped = reverse_escape(chatbot[-1][1])
    visual_img, generation_color = visualize_all_bbox_together(gr_img, unescaped)
    if visual_img is not None:
        if len(generation_color):
            chatbot[-1][1] = generation_color
        file_path = save_tmp_img(visual_img)
        chatbot = chatbot + [[None, (file_path,)]]

    return chatbot


def gradio_taskselect(idx):
    prompt_list = [
        '',
        '[reason] ',
        '[emotion] ',
        '[visual] ',
        '[audio] '
    ]
    instruct_list = [
        '**Hint:** Type in whatever you want',
        '**Hint:** Send the command to multimodal emotion reasoning',
        '**Hint:** Send the command to multimodal emotion recognition',
        '**Hint:** Send the command to generate visual description',
        '**Hint:** Send the command to generate audio description'
    ]
    return prompt_list[idx], instruct_list[idx]




chat = Chat(model, vis_processor, device=device)

title = """<h1 align="center">Emotion-LLaMA Demo</h1>"""
description = 'Welcome to Our Emotion-LLaMA Chatbot Demo!'
article = """<p><a href='https://anonymous.4open.science/r/Emotion-LLaMA'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p>"""

introduction = '''
For Abilities Involging Multimodal Emotion Understanding:
1. Reason: Click **Send** to generate a multimodal emotion description.
2. Emotion: Click **Send** to generate an emotion label.
3. Visual: Click **Send** to generate a visual description.
4. Audio: Click **Send** to generate an audio description.
5. No Tag: Input whatever you want and click **Send** without any tagging.

You can also simply chat in free form!
'''

text_input = gr.Textbox(placeholder='Upload your image and chat', interactive=True, show_label=False, container=False, scale=8)
with gr.Blocks() as demo:
    gr.Markdown(title)
    # gr.Markdown(description)
    gr.Markdown(article)

    with gr.Row():
        with gr.Column(scale=0.5):
            # image = gr.Image(type="pil", tool='sketch', brush_radius=20)
            image = gr.Video(sources=["upload", "webcam"])

            temperature = gr.Slider(
                minimum=0.1,
                maximum=1.5,
                value=0.2,
                step=0.1,
                interactive=True,
                label="Temperature",
            )

            clear = gr.Button("Restart")

            gr.Markdown(introduction)

        with gr.Column():
            chat_state = gr.State(value=None)
            img_list = gr.State(value=[])
            chatbot = gr.Chatbot(label='Emotion-LLaMA')

            dataset = gr.Dataset(
                components=[gr.Textbox(visible=False)],
                samples=[['No Tag'], ['reason'], ['emotion'], ['visual'], ['audio']],
                type="index",
                label='Task Shortcuts',
            )
            task_inst = gr.Markdown('**Hint:** Upload your video and chat')
            with gr.Row():
                text_input.render()
                send = gr.Button("Send", variant='primary', size='sm', scale=1)

    upload_flag = gr.State(value=0)
    replace_flag = gr.State(value=0)
    image.upload(image_upload_trigger, [upload_flag, replace_flag, img_list], [upload_flag, replace_flag])

    with gr.Row():
        with gr.Column():
            gr.Examples(examples=[
                ["examples/samplenew_00004251.mp4", "[detection] face", upload_flag, replace_flag, img_list],
                ["examples/sample_00000338.mp4", "The person in video says: Oh no, my phone and wallet are all in my bag. [emotion] Please determine which emotion label in the video represents: happy, sad, neutral, angry, worried, surprise.", upload_flag, replace_flag, img_list],
                ["examples/sample_00000669.mp4", "The person in video says: Why are you looking at me like this? It's just a woman, so you have to have something to do with me.  [emotion] Determine the emotional state shown in the video, choosing from happy, sad, neutral, angry, worried, or surprise.", upload_flag, replace_flag, img_list],
                ["examples/sample_00003462.mp4", "The person in video says: Do you believe that you push me around?  [emotion] Assess and label the emotion evident in the video: could it be happy, sad, neutral, angry, worried, surprise?", upload_flag, replace_flag, img_list],
                ["examples/sample_00000727.mp4", "The person in video says: No, this, I have to get up! You, I'm sorry, everyone. I'm sorry, it's from the German side.  [emotion] Identify the displayed emotion in the video: is it happy, sad, neutral, angry, worried, or surprise?", upload_flag, replace_flag, img_list],
                ["examples/samplenew_00061200.mp4", "The person in video says: Me: I'm not going in anymore, scared.  [emotion] Identify the displayed emotion in the video: is it happy, sad, neutral, angry, fear, contempt, doubt, worried, or surprise?", upload_flag, replace_flag, img_list],
            ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
                outputs=[upload_flag, replace_flag])
        with gr.Column():
            gr.Examples(examples=[
                ["examples/samplenew_00051251.mp4", "In what state is the person in the video, say the following: \"Do you really think so?\"", upload_flag, replace_flag, img_list],
                ["examples/sample_00004735.mp4", "[visual] What are the emotions of the woman in the video?", upload_flag, replace_flag, img_list],
                ["examples/sample_00002422.mp4", "[audio] Analyze the speaker's voice in the video.", upload_flag, replace_flag, img_list],
                ["examples/sample_00001073.mp4", "The person in video says: Make him different from before. I like the way you are now.  [reason] Please analyze all the clues in the video and reason out the emotional label of the person in the video.", upload_flag, replace_flag, img_list],
                ["examples/sample_00004671.mp4", "The person in video says: Won't you? Impossible! Fan Xiaomei is not such a person.  [reason] What are the facial expressions and vocal tone used in the video? What is the intended meaning behind his words? Which emotion does this reflect?", upload_flag, replace_flag, img_list],
                ["examples/sample_00005854.mp4", "The person in video says: Bastard! Boss, you don't choose, you prefer.  [reason] Please integrate information from various modalities to infer the emotional category of the person in the video.", upload_flag, replace_flag, img_list],
            ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
                outputs=[upload_flag, replace_flag])

    dataset.click(
        gradio_taskselect,
        inputs=[dataset],
        outputs=[text_input, task_inst],
        show_progress="hidden",
        postprocess=False,
        queue=False,
    )

    text_input.submit(
        gradio_ask,
        [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
        [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
    ).success(
        gradio_stream_answer,
        [chatbot, chat_state, img_list, temperature],
        [chatbot, chat_state]
    ).success(
        gradio_visualize,
        [chatbot, image],
        [chatbot],
        queue=False,
    )

    send.click(
        gradio_ask,
        [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
        [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
    ).success(
        gradio_stream_answer,
        [chatbot, chat_state, img_list, temperature],
        [chatbot, chat_state]
    ).success(
        gradio_visualize,
        [chatbot, image],
        [chatbot],
        queue=False,
    )

    clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, chat_state, img_list], queue=False)

demo.launch(share=True, enable_queue=True)