import argparse
import datetime
import hashlib
import json
import os
import subprocess
import sys
import time

import gradio as gr
import requests

from llava.constants import LOGDIR
from llava.conversation import SeparatorStyle, conv_templates, default_conversation
from llava.utils import (
    build_logger,
    moderation_msg,
    server_error_msg,
    violates_moderation,
)

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

headers = {"User-Agent": "LLaVA Client"}

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

priority = {
    "vicuna-13b": "aaaaaaa",
    "koala-13b": "aaaaaab",
}


def get_conv_log_filename():
    t = datetime.datetime.now()
    name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
    return name


def get_model_list():
    ret = requests.post(args.controller_url + "/refresh_all_workers")
    assert ret.status_code == 200
    ret = requests.post(args.controller_url + "/list_models")
    models = ret.json()["models"]
    models.sort(key=lambda x: priority.get(x, x))
    logger.info(f"Models: {models}")
    return models


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 load_demo(url_params, request: gr.Request):
    logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")

    dropdown_update = gr.Dropdown.update(visible=True)
    if "model" in url_params:
        model = url_params["model"]
        if model in models:
            dropdown_update = gr.Dropdown.update(value=model, visible=True)

    state = default_conversation.copy()
    return state, dropdown_update


def load_demo_refresh_model_list(request: gr.Request):
    logger.info(f"load_demo. ip: {request.client.host}")
    models = get_model_list()
    state = default_conversation.copy()

    models_downloaded = True if models else False

    model_dropdown_kwargs = {
        "choices": [],
        "value": "Downloading the models...",
        "interactive": models_downloaded,
    }

    if models_downloaded:
        model_dropdown_kwargs["choices"] = models
        model_dropdown_kwargs["value"] = models[0]

    models_dropdown_update = gr.Dropdown.update(**model_dropdown_kwargs)

    send_button_update = gr.Button.update(
        interactive=models_downloaded,
    )

    return state, models_dropdown_update, send_button_update


def vote_last_response(state, vote_type, model_selector, request: gr.Request):
    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "tstamp": round(time.time(), 4),
            "type": vote_type,
            "model": model_selector,
            "state": state.dict(),
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")


def upvote_last_response(state, model_selector, request: gr.Request):
    logger.info(f"upvote. ip: {request.client.host}")
    vote_last_response(state, "upvote", model_selector, request)
    return ("",) + (disable_btn,) * 3


def downvote_last_response(state, model_selector, request: gr.Request):
    logger.info(f"downvote. ip: {request.client.host}")
    vote_last_response(state, "downvote", model_selector, request)
    return ("",) + (disable_btn,) * 3


def flag_last_response(state, model_selector, request: gr.Request):
    logger.info(f"flag. ip: {request.client.host}")
    vote_last_response(state, "flag", model_selector, request)
    return ("",) + (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
    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
    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5


def clear_history(request: gr.Request):
    logger.info(f"clear_history. ip: {request.client.host}")
    state = default_conversation.copy()
    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5


def add_text(state, text, image, image_process_mode, request: gr.Request):
    logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")
    if len(text) <= 0 and image is None:
        state.skip_next = True
        return (state, state.to_gradio_chatbot(), "", None) + (no_change_btn,) * 5
    if args.moderate:
        flagged = violates_moderation(text)
        if flagged:
            state.skip_next = True
            return (state, state.to_gradio_chatbot(), moderation_msg, None) + (
                no_change_btn,
            ) * 5

    text = text[:1536]  # Hard cut-off
    if image is not None:
        text = text[:1200]  # Hard cut-off for images
        if "<image>" not in text:
            # text = '<Image><image></Image>' + text
            text = text + "\n<image>"
        text = (text, image, image_process_mode)
        if len(state.get_images(return_pil=True)) > 0:
            state = default_conversation.copy()
    state.append_message(state.roles[0], text)
    state.append_message(state.roles[1], None)
    state.skip_next = False
    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5


def http_bot(
    state, model_selector, temperature, top_p, max_new_tokens, request: gr.Request
):
    logger.info(f"http_bot. ip: {request.client.host}")
    start_tstamp = time.time()
    model_name = model_selector

    if state.skip_next:
        # This generate call is skipped due to invalid inputs
        yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
        return

    if len(state.messages) == state.offset + 2:
        # First round of conversation
        if "llava" in model_name.lower():
            if "llama-2" in model_name.lower():
                template_name = "llava_llama_2"
            elif "v1" in model_name.lower():
                if "mmtag" in model_name.lower():
                    template_name = "v1_mmtag"
                elif (
                    "plain" in model_name.lower()
                    and "finetune" not in model_name.lower()
                ):
                    template_name = "v1_mmtag"
                else:
                    template_name = "llava_v1"
            elif "mpt" in model_name.lower():
                template_name = "mpt"
            else:
                if "mmtag" in model_name.lower():
                    template_name = "v0_mmtag"
                elif (
                    "plain" in model_name.lower()
                    and "finetune" not in model_name.lower()
                ):
                    template_name = "v0_mmtag"
                else:
                    template_name = "llava_v0"
        elif "mpt" in model_name:
            template_name = "mpt_text"
        elif "llama-2" in model_name:
            template_name = "llama_2"
        else:
            template_name = "vicuna_v1"
        new_state = conv_templates[template_name].copy()
        new_state.append_message(new_state.roles[0], state.messages[-2][1])
        new_state.append_message(new_state.roles[1], None)
        state = new_state

    # Query worker address
    controller_url = args.controller_url
    ret = requests.post(
        controller_url + "/get_worker_address", json={"model": model_name}
    )
    worker_addr = ret.json()["address"]
    logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}")

    # No available worker
    if worker_addr == "":
        state.messages[-1][-1] = server_error_msg
        yield (
            state,
            state.to_gradio_chatbot(),
            disable_btn,
            disable_btn,
            disable_btn,
            enable_btn,
            enable_btn,
        )
        return

    # Construct prompt
    prompt = state.get_prompt()

    all_images = state.get_images(return_pil=True)
    all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images]
    for image, hash in zip(all_images, all_image_hash):
        t = datetime.datetime.now()
        filename = os.path.join(
            LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{hash}.jpg"
        )
        if not os.path.isfile(filename):
            os.makedirs(os.path.dirname(filename), exist_ok=True)
            image.save(filename)

    # Make requests
    pload = {
        "model": model_name,
        "prompt": prompt,
        "temperature": float(temperature),
        "top_p": float(top_p),
        "max_new_tokens": min(int(max_new_tokens), 1536),
        "stop": state.sep
        if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT]
        else state.sep2,
        "images": f"List of {len(state.get_images())} images: {all_image_hash}",
    }
    logger.info(f"==== request ====\n{pload}")

    pload["images"] = state.get_images()

    state.messages[-1][-1] = "▌"
    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5

    try:
        # Stream output
        response = requests.post(
            worker_addr + "/worker_generate_stream",
            headers=headers,
            json=pload,
            stream=True,
            timeout=10,
        )
        for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
            if chunk:
                data = json.loads(chunk.decode())
                if data["error_code"] == 0:
                    output = data["text"][len(prompt) :].strip()
                    state.messages[-1][-1] = output + "▌"
                    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
                else:
                    output = data["text"] + f" (error_code: {data['error_code']})"
                    state.messages[-1][-1] = output
                    yield (state, state.to_gradio_chatbot()) + (
                        disable_btn,
                        disable_btn,
                        disable_btn,
                        enable_btn,
                        enable_btn,
                    )
                    return
                time.sleep(0.03)
    except requests.exceptions.RequestException as e:
        state.messages[-1][-1] = server_error_msg
        yield (state, state.to_gradio_chatbot()) + (
            disable_btn,
            disable_btn,
            disable_btn,
            enable_btn,
            enable_btn,
        )
        return

    state.messages[-1][-1] = state.messages[-1][-1][:-1]
    yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5

    finish_tstamp = time.time()
    logger.info(f"{output}")

    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "tstamp": round(finish_tstamp, 4),
            "type": "chat",
            "model": model_name,
            "start": round(start_tstamp, 4),
            "finish": round(start_tstamp, 4),
            "state": state.dict(),
            "images": all_image_hash,
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")


title_markdown = """
Image to Text

"""

tos_markdown = """
### Instructions

Upload an image and enter a task such as, "Describe the image in one paragraph".
"""


learn_more_markdown = """

"""

block_css = """

#buttons button {
    min-width: min(120px,100%);
}

"""


def build_demo(embed_mode):
    models = get_model_list()

    textbox = gr.Textbox(
        show_label=False, placeholder="Enter text and press ENTER", container=False
    )
    with gr.Blocks(title="LLaVA", theme=gr.themes.Default(), css=block_css) as demo:
        state = gr.State(default_conversation.copy())

        if not embed_mode:
            gr.Markdown(title_markdown)

        with gr.Row():
            with gr.Column(scale=3):
                with gr.Row(elem_id="model_selector_row"):
                    model_selector = gr.Dropdown(
                        choices=models,
                        value=models[0] if models else "Downloading the models...",
                        interactive=True if models else False,
                        show_label=False,
                        container=False,
                    )

                imagebox = gr.Image(type="pil")
                image_process_mode = gr.Radio(
                    ["Crop", "Resize", "Pad", "Default"],
                    value="Default",
                    label="Preprocess for non-square image",
                    visible=False,
                )

                cur_dir = os.path.dirname(os.path.abspath(__file__))
                gr.Examples(
                    examples=[
                        [
                            f"{cur_dir}/examples/arm_wrestling.png",
                            "Write one or more sentences that describe the image.",
                        ],
                        [
                            f"{cur_dir}/examples/waterview.jpg",
                            "What are the things I should be cautious about when I visit here?",
                        ],
                    ],
                    inputs=[imagebox, textbox],
                )

                with gr.Accordion("Parameters", open=False) as parameter_row:
                    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",
                    )
                    max_output_tokens = gr.Slider(
                        minimum=0,
                        maximum=1024,
                        value=512,
                        step=64,
                        interactive=True,
                        label="Max output tokens",
                    )

            with gr.Column(scale=8):
                chatbot = gr.Chatbot(
                    elem_id="chatbot", label="Image Chatbot", height=550
                )
                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", interactive=False
                        )
                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 history", interactive=False)

        if not embed_mode:
            gr.Markdown(tos_markdown)
            gr.Markdown(learn_more_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, model_selector],
            [textbox, upvote_btn, downvote_btn, flag_btn],
        )
        downvote_btn.click(
            downvote_last_response,
            [state, model_selector],
            [textbox, upvote_btn, downvote_btn, flag_btn],
        )
        flag_btn.click(
            flag_last_response,
            [state, model_selector],
            [textbox, upvote_btn, downvote_btn, flag_btn],
        )
        regenerate_btn.click(
            regenerate,
            [state, image_process_mode],
            [state, chatbot, textbox, imagebox] + btn_list,
        ).then(
            http_bot,
            [state, model_selector, temperature, top_p, max_output_tokens],
            [state, chatbot] + btn_list,
        )
        clear_btn.click(
            clear_history, None, [state, chatbot, textbox, imagebox] + btn_list
        )

        textbox.submit(
            add_text,
            [state, textbox, imagebox, image_process_mode],
            [state, chatbot, textbox, imagebox] + btn_list,
        ).then(
            http_bot,
            [state, model_selector, temperature, top_p, max_output_tokens],
            [state, chatbot] + btn_list,
        )
        submit_btn.click(
            add_text,
            [state, textbox, imagebox, image_process_mode],
            [state, chatbot, textbox, imagebox] + btn_list,
        ).then(
            http_bot,
            [state, model_selector, temperature, top_p, max_output_tokens],
            [state, chatbot] + btn_list,
        )

        if args.model_list_mode == "once":
            demo.load(
                load_demo,
                [url_params],
                [state, model_selector],
                _js=get_window_url_params,
            )
        elif args.model_list_mode == "reload":
            demo.load(
                load_demo_refresh_model_list, None, [state, model_selector, submit_btn]
            )
        else:
            raise ValueError(f"Unknown model list mode: {args.model_list_mode}")

    return demo


def start_controller():
    logger.info("Starting the controller")
    controller_command = [
        "python",
        "-m",
        "llava.serve.controller",
        "--host",
        "0.0.0.0",
        "--port",
        "10000",
    ]
    return subprocess.Popen(controller_command)


def start_worker(model_path: str, bits=16):
    logger.info(f"Starting the model worker for the model {model_path}")
    model_name = model_path.strip("/").split("/")[-1]
    assert bits in [4, 8, 16], "It can be only loaded with 16-bit, 8-bit, and 4-bit."
    if bits != 16:
        model_name += f"-{bits}bit"
    worker_command = [
        "python",
        "-m",
        "llava.serve.model_worker",
        "--host",
        "0.0.0.0",
        "--controller",
        "http://localhost:10000",
        "--model-path",
        model_path,
        "--model-name",
        model_name,
    ]
    if bits != 16:
        worker_command += [f"--load-{bits}bit"]
    return subprocess.Popen(worker_command)


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="0.0.0.0")
    parser.add_argument("--port", type=int)
    parser.add_argument("--controller-url", type=str, default="http://localhost:10000")
    parser.add_argument("--concurrency-count", type=int, default=8)
    parser.add_argument(
        "--model-list-mode", type=str, default="reload", choices=["once", "reload"]
    )
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--moderate", action="store_true")
    parser.add_argument("--embed", action="store_true")

    args = parser.parse_args()

    return args


def start_demo(args):
    demo = build_demo(args.embed)
    demo.queue(
        concurrency_count=args.concurrency_count, status_update_rate=10, api_open=False
    ).launch(auth=("choibu", "cs1117"), server_name=args.host, server_port=args.port, share=args.share)


if __name__ == "__main__":
    args = get_args()
    logger.info(f"args: {args}")

    model_path = "liuhaotian/llava-v1.5-13b"
    bits = int(os.getenv("bits", 8))

    controller_proc = start_controller()
    worker_proc = start_worker(model_path, bits=bits)

    # Wait for worker and controller to start
    time.sleep(10)

    exit_status = 0
    try:
        start_demo(args)
    except Exception as e:
        print(e)
        exit_status = 1
    finally:
        worker_proc.kill()
        controller_proc.kill()

        sys.exit(exit_status)