"""
Multimodal Chatbot Arena (side-by-side) tab.
Users chat with two chosen models.
"""

import json
import os
import time

import gradio as gr
import numpy as np

from src.constants import (
    TEXT_MODERATION_MSG,
    IMAGE_MODERATION_MSG,
    MODERATION_MSG,
    CONVERSATION_LIMIT_MSG,
    SLOW_MODEL_MSG,
    INPUT_CHAR_LEN_LIMIT,
    CONVERSATION_TURN_LIMIT,
)
from src.model.model_adapter import get_conversation_template
from src.serve.gradio_block_arena_named import (
    flash_buttons,
    share_click,
    bot_response_multi,
)
from src.serve.gradio_block_arena_vision import (
    get_vqa_sample,
    set_invisible_image,
    set_visible_image,
    add_image,
    moderate_input,
)
from src.serve.gradio_web_server import (
    State,
    bot_response,
    get_conv_log_filename,
    no_change_btn,
    enable_btn,
    disable_btn,
    invisible_btn,
    acknowledgment_md,
    get_ip,
    get_model_description_md,
    _prepare_text_with_image,
)
from src.serve.remote_logger import get_remote_logger
from src.utils import (
    build_logger,
    moderation_filter,
    image_moderation_filter,
)


logger = build_logger("gradio_web_server_multi", "gradio_web_server_multi.log")

num_sides = 2
enable_moderation = False


def clear_history_example(request: gr.Request):
    logger.info(f"clear_history_example (named). ip: {get_ip(request)}")
    return (
        [None] * num_sides
        + [None] * num_sides
        + [invisible_btn] * 4
        + [disable_btn] * 2
    )


def vote_last_response(states, vote_type, model_selectors, request: gr.Request):
    filename = get_conv_log_filename(states[0].is_vision, states[0].has_csam_image)
    with open(filename, "a") as fout:
        data = {
            "tstamp": round(time.time(), 4),
            "type": vote_type,
            "models": [x for x in model_selectors],
            "states": [x.dict() for x in states],
            "ip": get_ip(request),
        }
        fout.write(json.dumps(data) + "\n")
    get_remote_logger().log(data)


def leftvote_last_response(
    state0, state1, model_selector0, model_selector1, request: gr.Request
):
    logger.info(f"leftvote (named). ip: {get_ip(request)}")
    vote_last_response(
        [state0, state1], "leftvote", [model_selector0, model_selector1], request
    )
    return (None,) + (disable_btn,) * 4


def rightvote_last_response(
    state0, state1, model_selector0, model_selector1, request: gr.Request
):
    logger.info(f"rightvote (named). ip: {get_ip(request)}")
    vote_last_response(
        [state0, state1], "rightvote", [model_selector0, model_selector1], request
    )
    return (None,) + (disable_btn,) * 4


def tievote_last_response(
    state0, state1, model_selector0, model_selector1, request: gr.Request
):
    logger.info(f"tievote (named). ip: {get_ip(request)}")
    vote_last_response(
        [state0, state1], "tievote", [model_selector0, model_selector1], request
    )
    return (None,) + (disable_btn,) * 4


def bothbad_vote_last_response(
    state0, state1, model_selector0, model_selector1, request: gr.Request
):
    logger.info(f"bothbad_vote (named). ip: {get_ip(request)}")
    vote_last_response(
        [state0, state1], "bothbad_vote", [model_selector0, model_selector1], request
    )
    return (None,) + (disable_btn,) * 4


def regenerate(state0, state1, request: gr.Request):
    logger.info(f"regenerate (named). ip: {get_ip(request)}")
    states = [state0, state1]
    if state0.regen_support and state1.regen_support:
        for i in range(num_sides):
            states[i].conv.update_last_message(None)
        return (
            states
            + [x.to_gradio_chatbot() for x in states]
            + [None]
            + [disable_btn] * 6
        )
    states[0].skip_next = True
    states[1].skip_next = True
    return (
        states + [x.to_gradio_chatbot() for x in states] + [None] + [no_change_btn] * 6
    )


def clear_history(request: gr.Request):
    logger.info(f"clear_history (named). ip: {get_ip(request)}")
    return (
        [None] * num_sides
        + [None] * num_sides
        + [None]
        + [invisible_btn] * 4
        + [disable_btn] * 2
    )


def add_text(
    state0, state1, model_selector0, model_selector1, chat_input, request: gr.Request
):
    text, images = chat_input["text"], chat_input["files"]
    ip = get_ip(request)
    logger.info(f"add_text (named). ip: {ip}. len: {len(text)}")
    states = [state0, state1]
    model_selectors = [model_selector0, model_selector1]

    # Init states if necessary
    for i in range(num_sides):
        if states[i] is None:
            states[i] = State(model_selectors[i], is_vision=True)

    if len(text) <= 0:
        for i in range(num_sides):
            states[i].skip_next = True
        return (
            states
            + [x.to_gradio_chatbot() for x in states]
            + [None]
            + [
                no_change_btn,
            ]
            * 6
        )

    model_list = [states[i].model_name for i in range(num_sides)]
    all_conv_text_left = states[0].conv.get_prompt()
    all_conv_text_right = states[0].conv.get_prompt()
    all_conv_text = (
        all_conv_text_left[-1000:] + all_conv_text_right[-1000:] + "\nuser: " + text
    )

    text, image_flagged, csam_flag = moderate_input(
        text, all_conv_text, model_list, images, ip
    )

    conv = states[0].conv
    if (len(conv.messages) - conv.offset) // 2 >= CONVERSATION_TURN_LIMIT:
        logger.info(f"conversation turn limit. ip: {ip}. text: {text}")
        for i in range(num_sides):
            states[i].skip_next = True
        return (
            states
            + [x.to_gradio_chatbot() for x in states]
            + [{"text": CONVERSATION_LIMIT_MSG}]
            + [
                no_change_btn,
            ]
            * 6
        )

    if image_flagged:
        logger.info(f"image flagged. ip: {ip}. text: {text}")
        for i in range(num_sides):
            states[i].skip_next = True
        return (
            states
            + [x.to_gradio_chatbot() for x in states]
            + [{"text": IMAGE_MODERATION_MSG}]
            + [
                no_change_btn,
            ]
            * 6
        )

    text = text[:INPUT_CHAR_LEN_LIMIT]  # Hard cut-off
    for i in range(num_sides):
        post_processed_text = _prepare_text_with_image(
            states[i], text, images, csam_flag=csam_flag
        )
        logger.info(f"msg={post_processed_text}")
        states[i].conv.append_message(states[i].conv.roles[0], post_processed_text)
        states[i].conv.append_message(states[i].conv.roles[1], None)
        states[i].skip_next = False

    return (
        states
        + [x.to_gradio_chatbot() for x in states]
        + [None]
        + [
            disable_btn,
        ]
        * 6
    )


def build_side_by_side_vision_ui_named(models, random_questions=None):
    notice_markdown = """
# ⚔️  Vision Arena ⚔️ : Benchmarking FIRE-LLaVA VS. LLaVA-NeXT

## 📜 Rules
- Chat with any two models side-by-side and vote!
- You can continue chatting for multiple rounds.
- Click "Clear history" to start a new round.
- You can only chat with <span style='color: #DE3163; font-weight: bold'>one image per conversation</span>. You can upload images less than 15MB.

**❗️ For research purposes, we log user prompts and images, and may release this data to the public in the future. Please do not upload any confidential or personal information.**

## 🤖 Choose two models to compare
"""

    states = [gr.State() for _ in range(num_sides)]
    model_selectors = [None] * num_sides
    chatbots = [None] * num_sides

    notice = gr.Markdown(notice_markdown, elem_id="notice_markdown")

    with gr.Row():
        with gr.Column(scale=2, visible=False) as image_column:
            imagebox = gr.Image(
                type="pil",
                show_label=False,
                interactive=False,
            )

        with gr.Column(scale=5):
            with gr.Group(elem_id="share-region-anony"):
                with gr.Accordion(
                    f"🔍 Expand to see the descriptions of {len(models)} models",
                    open=False,
                ):
                    model_description_md = get_model_description_md(models)
                    gr.Markdown(
                        model_description_md, elem_id="model_description_markdown"
                    )

                with gr.Row():
                    for i in range(num_sides):
                        with gr.Column():
                            model_names_dict = {
                                "llava-fire": 'FIRE-LLaVA',
                                "llava-original": "LLaVA-Next-LLaMA-3-8B"
                            }
                            model_choices = []
                            for model_value in models:
                                if model_value in model_names_dict:
                                    model_choices.append((model_names_dict[model_value], model_value))
                                else:
                                    model_choices.append((model_value, model_value))
                            model_selectors[i] = gr.Dropdown(
                                choices=model_choices,
                                value=models[i] if len(models) > i else "",
                                interactive=True,
                                show_label=False,
                                container=False,
                            )

                with gr.Row():
                    for i in range(num_sides):
                        label = "Model A" if i == 0 else "Model B"
                        with gr.Column():
                            chatbots[i] = gr.Chatbot(
                                label=label,
                                elem_id=f"chatbot",
                                height=550,
                                show_copy_button=True,
                            )

    with gr.Row():
        leftvote_btn = gr.Button(
            value="👈  A is better", visible=False, interactive=False
        )
        rightvote_btn = gr.Button(
            value="👉  B is better", visible=False, interactive=False
        )
        tie_btn = gr.Button(value="🤝  Tie", visible=False, interactive=False)
        bothbad_btn = gr.Button(
            value="👎  Both are bad", visible=False, interactive=False
        )
    with gr.Row():
        recommendation = gr.Textbox(
            visible=False
        )
    with gr.Row():
        textbox = gr.MultimodalTextbox(
            file_types=["image"],
            show_label=False,
            placeholder="Click add or drop your image here",
            container=True,
            elem_id="input_box",
        )

    with gr.Row() as button_row:
        if random_questions:
            global vqa_samples
            with open(random_questions, "r") as f:
                vqa_samples = json.load(f)
            random_btn = gr.Button(value="🎲 Random Example", interactive=True)
        clear_btn = gr.Button(value="🗑️  Clear history", interactive=False)
        regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=False)
        share_btn = gr.Button(value="📷  Share")
    with gr.Row():
        gr.Examples(examples=[
            [
                {
                    "files": ["assets/image_50.png"],
                    "text": "Please directly answer the question and provide the correct option letter, e.g., A, B, C, D.\nQuestion: As shown in the figure, then angle COE = ()\nChoices:\nA:30°\nB:140°\nC:50°\nD:60°"
                },
                {
                    "files": ["assets/test_11407.png"],
                    "text": """Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end. 
Question: 如图，△ABC中，AD为中线，AD⊥AC，∠BAD＝30°，AB＝3，则AC长（） 
Choices: 
A. 2.5 
B. 2 
C. 1 
D. 1.5"""
                },
                {
                    "files": ["assets/magnetic.png"],
                    "text": """Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end. 
Question: Will these magnets attract or repel each other? 
Choices: 
A. repel 
B. attract"""
                },
                {
                    "files": ["assets/fox.png"],
                    "text": """Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end. 
Question: Which of the following organisms is the primary consumer in this food web? 
Choices: 
A. Arctic fox 
B. rough-legged hawk 
C. mushroom"""
                },

            ],
        ],inputs=[textbox])
    with gr.Accordion("Parameters", open=False) as parameter_row:
        temperature = gr.Slider(
            minimum=0.0,
            maximum=1.0,
            value=0.7,
            step=0.1,
            interactive=True,
            label="Temperature",
        )
        top_p = gr.Slider(
            minimum=0.0,
            maximum=1.0,
            value=1.0,
            step=0.1,
            interactive=True,
            label="Top P",
        )
        max_output_tokens = gr.Slider(
            minimum=16,
            maximum=2048,
            value=1024,
            step=64,
            interactive=True,
            label="Max output tokens",
        )

    gr.Markdown(acknowledgment_md, elem_id="ack_markdown")
    
    # Register listeners
    btn_list = [
        leftvote_btn,
        rightvote_btn,
        tie_btn,
        bothbad_btn,
        regenerate_btn,
        clear_btn,
    ]
    leftvote_btn.click(
        leftvote_last_response,
        states + model_selectors,
        [textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn],
    )
    rightvote_btn.click(
        rightvote_last_response,
        states + model_selectors,
        [textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn],
    )
    tie_btn.click(
        tievote_last_response,
        states + model_selectors,
        [textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn],
    )
    bothbad_btn.click(
        bothbad_vote_last_response,
        states + model_selectors,
        [textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn],
    )
    regenerate_btn.click(
        regenerate, states, states + chatbots + [textbox] + btn_list
    ).then(
        bot_response_multi,
        states + [temperature, top_p, max_output_tokens],
        states + chatbots + btn_list,
    ).then(
        flash_buttons, [], btn_list
    )
    clear_btn.click(clear_history, None, states + chatbots + [textbox] + btn_list)
    
    share_js = """
function (a, b, c, d) {
    const captureElement = document.querySelector('#share-region-named');
    html2canvas(captureElement)
        .then(canvas => {
            canvas.style.display = 'none'
            document.body.appendChild(canvas)
            return canvas
        })
        .then(canvas => {
            const image = canvas.toDataURL('image/png')
            const a = document.createElement('a')
            a.setAttribute('download', 'chatbot-arena.png')
            a.setAttribute('href', image)
            a.click()
            canvas.remove()
        });
    return [a, b, c, d];
}
"""
    share_btn.click(share_click, states + model_selectors, [], js=share_js)

    for i in range(num_sides):
        model_selectors[i].change(
            clear_history, None, states + chatbots + [textbox] + btn_list
        ).then(set_visible_image, [textbox], [image_column])

    textbox.input(add_image, [textbox], [imagebox]).then(
        set_visible_image, [textbox], [image_column]
    ).then(clear_history_example, None, states + chatbots + btn_list)
    def get_recommendation(chatbots):

        logger.info(f"chatbots {chatbots}")

        return [gr.Textbox(visible=True, value="Teacher Feedback Recommendation Content")]

    textbox.submit(
        add_text,
        states + model_selectors + [textbox],
        states + chatbots + [textbox] + btn_list,
    ).then(set_invisible_image, [], [image_column]).then(
        bot_response_multi,
        states + [temperature, top_p, max_output_tokens],
        states + chatbots + btn_list,
    ).then(
        flash_buttons, [], btn_list
    ).then(
        get_recommendation, chatbots, [recommendation]
    )

    if random_questions:
        random_btn.click(
            get_vqa_sample,  # First, get the VQA sample
            [],  # Pass the path to the VQA samples
            [textbox, imagebox],  # Outputs are textbox and imagebox
        ).then(set_visible_image, [textbox], [image_column]).then(
            clear_history_example, None, states + chatbots + btn_list
        )

    return states + model_selectors