import spaces
import argparse
from ast import parse
import datetime
import json
import os
import time
import hashlib
import re
import torch
import gradio as gr
import requests
import random
from filelock import FileLock
from io import BytesIO
from PIL import Image, ImageDraw, ImageFont
from models import load_image
from constants import LOGDIR
from utils import (
    build_logger,
    server_error_msg,
    violates_moderation,
    moderation_msg,
    load_image_from_base64,
    get_log_filename,
)
from threading import Thread
import traceback
# import torch
from conversation import Conversation
from transformers import AutoModel, AutoTokenizer, TextIteratorStreamer
import subprocess

subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

torch.set_default_device('cuda')

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

headers = {"User-Agent": "Vintern-1B-3.5-Demo Client"}

no_change_btn = gr.Button()
enable_btn = gr.Button(interactive=True)
disable_btn = gr.Button(interactive=False)


@spaces.GPU(duration=10)
def make_zerogpu_happy():
    pass


def write2file(path, content):
    lock = FileLock(f"{path}.lock")
    with lock:
        with open(path, "a") as fout:
            fout.write(content)


get_window_url_params = """
function() {
    const params = new URLSearchParams(window.location.search);
    url_params = Object.fromEntries(params);
    console.log(url_params);
    return url_params;
    }
"""


def init_state(state=None):
    if state is not None:
        del state
    return Conversation()

def vote_last_response(state, liked, request: gr.Request):
    conv_data = {
        "tstamp": round(time.time(), 4),
        "like": liked,
        "model": 'Vintern-1B-v3',
        "state": state.dict(),
        "ip": request.client.host,
    }
    write2file(get_log_filename(), json.dumps(conv_data) + "\n")


def upvote_last_response(state, request: gr.Request):
    logger.info(f"upvote. ip: {request.client.host}")
    vote_last_response(state, True, request)
    textbox = gr.MultimodalTextbox(value=None, interactive=True)
    return (textbox,) + (disable_btn,) * 3


def downvote_last_response(state, request: gr.Request):
    logger.info(f"downvote. ip: {request.client.host}")
    vote_last_response(state, False, request)
    textbox = gr.MultimodalTextbox(value=None, interactive=True)
    return (textbox,) + (disable_btn,) * 3


def vote_selected_response(
    state, request: gr.Request, data: gr.LikeData
):
    logger.info(
        f"Vote: {data.liked}, index: {data.index}, value: {data.value} , ip: {request.client.host}"
    )
    conv_data = {
        "tstamp": round(time.time(), 4),
        "like": data.liked,
        "index": data.index,
        "model": 'Vintern-1B-v3',
        "state": state.dict(),
        "ip": request.client.host,
    }
    write2file(get_log_filename(), json.dumps(conv_data) + "\n")
    return


def flag_last_response(state, request: gr.Request):
    logger.info(f"flag. ip: {request.client.host}")
    vote_last_response(state, "flag", request)
    textbox = gr.MultimodalTextbox(value=None, interactive=True)
    return (textbox,) + (disable_btn,) * 3


def regenerate(state, image_process_mode, request: gr.Request):
    logger.info(f"regenerate. ip: {request.client.host}")
    # state.messages[-1][-1] = None
    state.update_message(Conversation.ASSISTANT, content='', image=None, idx=-1)
    prev_human_msg = state.messages[-2]
    if type(prev_human_msg[1]) in (tuple, list):
        prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode)
    state.skip_next = False
    textbox = gr.MultimodalTextbox(value=None, interactive=True)
    return (state, state.to_gradio_chatbot(), textbox) + (disable_btn,) * 5


def clear_history(request: gr.Request):
    logger.info(f"clear_history. ip: {request.client.host}")
    state = init_state()
    textbox = gr.MultimodalTextbox(value=None, interactive=True)
    return (state, state.to_gradio_chatbot(), textbox) + (disable_btn,) * 5


def add_text(state, message, system_prompt, request: gr.Request):
    print(f"state: {state}")
    if not state:
        state = init_state()
    images = message.get("files", [])
    text = message.get("text", "").strip()
    logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")
    # import pdb; pdb.set_trace()
    textbox = gr.MultimodalTextbox(value=None, interactive=False)
    if len(text) <= 0 and len(images) == 0:
        state.skip_next = True
        return (state, state.to_gradio_chatbot(), textbox) + (no_change_btn,) * 5
    if args.moderate:
        flagged = violates_moderation(text)
        if flagged:
            state.skip_next = True
            textbox = gr.MultimodalTextbox(
                value={"text": moderation_msg}, interactive=True
            )
            return (state, state.to_gradio_chatbot(), textbox) + (no_change_btn,) * 5
    images = [Image.open(path).convert("RGB") for path in images]

    if len(images) > 0 and len(state.get_images(source=state.USER)) > 0:
        state = init_state(state)
    state.set_system_message(system_prompt)
    state.append_message(Conversation.USER, text, images)
    state.skip_next = False
    return (state, state.to_gradio_chatbot(), textbox) + (
        disable_btn,
    ) * 5

model_name = "5CD-AI/Vintern-1B-v3_5"
model = AutoModel.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)

@spaces.GPU
def predict(message, 
            image_path, 
            history,
            max_input_tiles=6, 
            temperature=1.0,
            max_output_tokens=700,
            top_p=0.7,
            repetition_penalty=2.5):
        pixel_values = load_image(image_path, max_num=max_input_tiles).to(torch.bfloat16).cuda()
        generation_config = dict(temperature=temperature, max_new_tokens= max_output_tokens, top_p=top_p, do_sample=False, num_beams = 3, repetition_penalty=repetition_penalty)
        if pixel_values is not None:
            question = '<image>\n'+message
        else:
            question = message
        response, conv_history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
        return response, conv_history

def http_bot(
    state,
    temperature,
    top_p,
    repetition_penalty,
    max_new_tokens,
    max_input_tiles,
    request: gr.Request,
):
    
    logger.info(f"http_bot. ip: {request.client.host}")
    start_tstamp = time.time()
    if hasattr(state, "skip_next") and state.skip_next:
        # This generate call is skipped due to invalid inputs
        yield (
            state,
            state.to_gradio_chatbot(),
            gr.MultimodalTextbox(interactive=False),
        ) + (no_change_btn,) * 5
        return

    if model is None:
        state.update_message(Conversation.ASSISTANT, server_error_msg)
        yield (
            state,
            state.to_gradio_chatbot(),
            gr.MultimodalTextbox(interactive=False),
            disable_btn,
            disable_btn,
            disable_btn,
            enable_btn,
            enable_btn,
        )
        return

    all_images = state.get_images(source=state.USER)
    all_image_paths = [state.save_image(image) for image in all_images]

    state.append_message(Conversation.ASSISTANT, state.streaming_placeholder)
    yield (
        state,
        state.to_gradio_chatbot(),
        gr.MultimodalTextbox(interactive=False),
    ) + (disable_btn,) * 5

    try:
        # Stream output
        message = state.get_user_message(source=state.USER)
        logger.info(f"==== User message ====\n{message}")
        logger.info(f"==== Image paths ====\n{all_image_paths}")

        logger.info(f"==== History ====\n{state.get_prompt()}")
        response, conv_history = predict(message, all_image_paths[0], max_input_tiles, temperature, max_new_tokens, top_p, repetition_penalty)
        logger.info(f"==== AI history ====\n{conv_history}")


        # response = "This is a test response"
        buffer = ""
        for new_text in response:
            buffer += new_text
                
            state.update_message(Conversation.ASSISTANT, buffer + state.streaming_placeholder, None)
            yield (
                state,
                state.to_gradio_chatbot(),
                gr.MultimodalTextbox(interactive=False),
            ) + (disable_btn,) * 5

    except Exception as e:
        logger.error(f"Error in http_bot: {e} \n{traceback.format_exc()}")
        state.update_message(Conversation.ASSISTANT, server_error_msg, None)
        yield (
            state,
            state.to_gradio_chatbot(),
            gr.MultimodalTextbox(interactive=True),
        ) + (
            disable_btn,
            disable_btn,
            disable_btn,
            enable_btn,
            enable_btn,
        )
        return

    ai_response = state.return_last_message()

    logger.info(f"==== AI response ====\n{ai_response}")
   
    state.end_of_current_turn()

    yield (
        state,
        state.to_gradio_chatbot(),
        gr.MultimodalTextbox(interactive=True),
    ) + (enable_btn,) * 5

    finish_tstamp = time.time()
    logger.info(f"{buffer}")
    data = {
        "tstamp": round(finish_tstamp, 4),
        "like": None,
        "model": model_name,
        "start": round(start_tstamp, 4),
        "finish": round(start_tstamp, 4),
        "state": state.dict(),
        "images": all_image_paths,
        "ip": request.client.host,
    }
    write2file(get_log_filename(), json.dumps(data) + "\n")

# <h1 style="font-size: 28px; font-weight: bold;">Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling</h1>
title_html = """
<div style="text-align: center;">
    <img src="https://lh3.googleusercontent.com/pw/AP1GczMmW-aFQ4dNaR_LCAllh4UZLLx9fTZ1ITHeGVMWx-1bwlIWz4VsWJSGb3_9C7CQfvboqJH41y2Sbc5ToC9ZmKeV4-buf_DEevIMU0HtaLWgHAPOqBiIbG6LaE8CvDqniLZzvB9UX8TR_-YgvYzPFt2z=w1472-h832-s-no-gm?authuser=0" style="height: 100; width: 100%;">
    <p>🔥Vintern-1B-v3_5🔥</p>
    <p>An Efficient Multimodal Large Language Model for Vietnamese</p>
    <a href="https://huggingface.co/papers/2408.12480">[📖 Vintern Paper]</a>
    <a href="https://huggingface.co/5CD-AI">[🤗 Huggingface]</a>
</div>
"""


tos_markdown = """
### Terms of use
By using this service, users are required to agree to the following terms:
It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
"""


# .gradio-container {margin: 5px 10px 0 10px !important};
block_css = """
.gradio-container {margin: 0.1% 1% 0 1% !important; max-width: 98% !important;};
#buttons button {
    min-width: min(120px,100%);
}

.gradient-text {
    font-size: 28px;
    width: auto;
    font-weight: bold;
    background: linear-gradient(45deg, red, orange, yellow, green, blue, indigo, violet);
    background-clip: text;
    -webkit-background-clip: text;
    color: transparent;
}

.plain-text {
    font-size: 22px;
    width: auto;
    font-weight: bold;
}
"""

# js = """
# function createWaveAnimation() {
#     const text = document.getElementById('text');
#     var i = 0;
#     setInterval(function() {
#         const colors = [
#             'red, orange, yellow, green, blue, indigo, violet, purple',
#             'orange, yellow, green, blue, indigo, violet, purple, red',
#             'yellow, green, blue, indigo, violet, purple, red, orange',
#             'green, blue, indigo, violet, purple, red, orange, yellow',
#             'blue, indigo, violet, purple, red, orange, yellow, green',
#             'indigo, violet, purple, red, orange, yellow, green, blue',
#             'violet, purple, red, orange, yellow, green, blue, indigo',
#             'purple, red, orange, yellow, green, blue, indigo, violet',
#         ];
#         const angle = 45;
#         const colorIndex = i % colors.length;
#         text.style.background = `linear-gradient(${angle}deg, ${colors[colorIndex]})`;
#         text.style.webkitBackgroundClip = 'text';
#         text.style.backgroundClip = 'text';
#         text.style.color = 'transparent';
#         text.style.fontSize = '28px';
#         text.style.width = 'auto';
#         text.textContent = 'Vintern-1B';
#         text.style.fontWeight = 'bold';
#         i += 1;
#     }, 200);
#     const params = new URLSearchParams(window.location.search);
#     url_params = Object.fromEntries(params);
#     // console.log(url_params);
#     // console.log('hello world...');
#     // console.log(window.location.search);
#     // console.log('hello world...');
#     // alert(window.location.search)
#     // alert(url_params);
#     return url_params;
# }

# """


def build_demo():
    textbox = gr.MultimodalTextbox(
        interactive=True,
        file_types=["image", "video"],
        placeholder="Enter message or upload file...",
        show_label=False,
    )

    with gr.Blocks(
        title="Vintern-1B-v3_5-Demo",
        theme=gr.themes.Default(),
        css=block_css,
    ) as demo:
        state = gr.State()

        with gr.Row():
            with gr.Column(scale=2):
                # gr.Image('./gallery/logo-47b364d3.jpg')
                gr.HTML(title_html)

                with gr.Accordion("Settings", open=False) as setting_row:
                    system_prompt = gr.Textbox(
                        value="Bạn là một trợ lý AI đa phương thức hữu ích, hãy trả lời câu hỏi người dùng một cách chi tiết.",
                        label="System Prompt",
                        interactive=True,
                    )
                    temperature = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.2,
                        step=0.1,
                        interactive=True,
                        label="Temperature",
                    )
                    top_p = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.7,
                        step=0.1,
                        interactive=True,
                        label="Top P",
                    )
                    repetition_penalty = gr.Slider(
                        minimum=1.0,
                        maximum=1.5,
                        value=1.1,
                        step=0.02,
                        interactive=True,
                        label="Repetition penalty",
                    )
                    max_output_tokens = gr.Slider(
                        minimum=0,
                        maximum=4096,
                        value=1024,
                        step=64,
                        interactive=True,
                        label="Max output tokens",
                    )
                    max_input_tiles = gr.Slider(
                        minimum=1,
                        maximum=32,
                        value=12,
                        step=1,
                        interactive=True,
                        label="Max input tiles (control the image size)",
                    )
                examples = gr.Examples(
                    examples=[
                        [
                            {
                                "files": [
                                    "gallery/14.jfif",
                                ],
                                "text": "Please help me analyze this picture.",
                            }
                        ],
                        [
                            {
                                "files": [
                                    "gallery/1-2.PNG",
                                ],
                                "text": "Implement this flow chart using python",
                            }
                        ],
                        [
                            {
                                "files": [
                                    "gallery/15.PNG",
                                ],
                                "text": "Please help me analyze this picture.",
                            }
                        ],
                    ],
                    inputs=[textbox],
                )

            with gr.Column(scale=8):
                chatbot = gr.Chatbot(
                    elem_id="chatbot",
                    label="Vintern-1B-v3_5-Demo",
                    height=580,
                    show_copy_button=True,
                    show_share_button=True,
                    avatar_images=[
                        "assets/human.png",
                        "assets/assistant.png",
                    ],
                    bubble_full_width=False,
                )
                with gr.Row():
                    with gr.Column(scale=8):
                        textbox.render()
                    with gr.Column(scale=1, min_width=50):
                        submit_btn = gr.Button(value="Send", variant="primary")
                with gr.Row(elem_id="buttons") as button_row:
                    upvote_btn = gr.Button(value="👍  Upvote", interactive=False)
                    downvote_btn = gr.Button(value="👎  Downvote", interactive=False)
                    flag_btn = gr.Button(value="⚠️  Flag", interactive=False)
                    # stop_btn = gr.Button(value="⏹️  Stop Generation", interactive=False)
                    regenerate_btn = gr.Button(
                        value="🔄  Regenerate", interactive=False
                    )
                    clear_btn = gr.Button(value="🗑️  Clear", interactive=False)

        gr.Markdown(tos_markdown)
        url_params = gr.JSON(visible=False)

        # Register listeners
        btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn]
        upvote_btn.click(
            upvote_last_response,
            [state],
            [textbox, upvote_btn, downvote_btn, flag_btn],
        )
        downvote_btn.click(
            downvote_last_response,
            [state],
            [textbox, upvote_btn, downvote_btn, flag_btn],
        )
        chatbot.like(
            vote_selected_response,
            [state],
            [],
        )
        flag_btn.click(
            flag_last_response,
            [state],
            [textbox, upvote_btn, downvote_btn, flag_btn],
        )
        regenerate_btn.click(
            regenerate,
            [state, system_prompt],
            [state, chatbot, textbox] + btn_list,
        ).then(
            http_bot,
            [
                state,
                temperature,
                top_p,
                repetition_penalty,
                max_output_tokens,
                max_input_tiles,
            ],
            [state, chatbot, textbox] + btn_list,
        )
        clear_btn.click(clear_history, None, [state, chatbot, textbox] + btn_list)

        textbox.submit(
            add_text,
            [state, textbox, system_prompt],
            [state, chatbot, textbox] + btn_list,
        ).then(
            http_bot,
            [
                state,
                temperature,
                top_p,
                repetition_penalty,
                max_output_tokens,
                max_input_tiles,
            ],
            [state, chatbot, textbox] + btn_list,
        )
        submit_btn.click(
            add_text,
            [state, textbox, system_prompt],
            [state, chatbot, textbox] + btn_list,
        ).then(
            http_bot,
            [
                state,
                temperature,
                top_p,
                repetition_penalty,
                max_output_tokens,
                max_input_tiles,
            ],
            [state, chatbot, textbox] + btn_list,
        )

    return demo


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="0.0.0.0")
    parser.add_argument("--port", type=int, default=7860)
    parser.add_argument("--concurrency-count", type=int, default=10)
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--moderate", action="store_true")
    args = parser.parse_args()
    logger.info(f"args: {args}")

    logger.info(args)
    demo = build_demo()
    demo.queue(api_open=False).launch(
        server_name=args.host,
        server_port=args.port,
        share=args.share,
        max_threads=args.concurrency_count,
    )