import argparse
import datetime
import json
import os
import time
import gradio as gr
import requests
import hashlib
import pypandoc
import base64
import sys
import spaces

from io import BytesIO

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

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

#logger = build_#logger("gradio_web_server", "gradio_web_server.log")

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

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

priority = {
    "Bunny": "aaaaaaa",
}

def start_controller():
    print("Starting the controller")
    controller_command = [
        sys.executable,
        "serve/controller.py",
        "--host",
        "0.0.0.0",
        "--port",
        "10000",
    ]
    print(controller_command)
    return subprocess.Popen(controller_command)

@spaces.GPU
def start_worker(model_path: str):
    print(f"Starting the model worker for the model {model_path}")
    model_path = 'qnguyen3/nanoLLaVA'
    worker_command = [
        sys.executable,
        "serve/model_worker.py",
        "--host",
        "0.0.0.0",
        "--controller",
        "http://localhost:10000",
        "--port",
        "40000",
        "worker",
        "http://localhost:40000",
        "--model-path",
        model_path,
        "--model-type",
        "qwen1.5-0.5b",
        "--use-flash-attn",
    ]
    print(worker_command)
    return subprocess.Popen(worker_command)


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.update(visible=True)
    if "model" in url_params:
        model = url_params["model"]
        if model in models:
            dropdown_update = gr.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()
    dropdown_update = gr.update(
        choices=models,
        value=models[0] if len(models) > 0 else ""
    )
    return state, dropdown_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 save_conversation(conversation):
    print("save_conversation_wrapper is called")
    html_content = "<html><body>"

    for role, message in conversation.messages:
        if isinstance(message, str):  # only text
            html_content += f"<p><b>{role}</b>: {message}</p>"
        elif isinstance(message, tuple):  # text+image
            text, image_obj, _ = message

            # add text
            if text:
                html_content += f"<p><b>{role}</b>: {text}</p>"

            # add image
            buffered = BytesIO()
            image_obj.save(buffered, format="PNG")
            encoded_image = base64.b64encode(buffered.getvalue()).decode()
            html_content += f'<img src="data:image/png;base64,{encoded_image}" /><br>'

    html_content += "</body></html>"

    doc_path = "./conversation.docx"
    pypandoc.convert_text(html_content, 'docx', format='html', outputfile=doc_path,
                          extra_args=["-M2GB", "+RTS", "-K64m", "-RTS"])
    return doc_path


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()
    #logger.info(f"Input Text: {text}")
    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:
        template_name = "bunny"
        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

    #logger.info(f"Processed Input Text: {state.messages[-2][1]}")
    # 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(), enable_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": '<|im_end|>', #state.sep if state.sep_style in [SeparatorStyle.PLAIN, ] 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()
    print('=========> get_images')
    state.messages[-1][-1] = "▌"
    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
    print('=========> state', state.messages[-1][-1])

    try:
        # Stream output
        response = requests.post(worker_addr + "/worker_generate_stream",
                                 headers=headers, json=pload, stream=True, timeout=1000)
        print("====> response ok")
        print("====> response dir", dir(response))
        print("====> response", response)
        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()) + (enable_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()) + (enable_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(finish_tstamp, 4),
            "state": state.dict(),
            "images": all_image_hash,
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")


title_markdown = ("""
# 🐰 Bunny: A family of lightweight multimodal models

[📖[Technical report](https://arxiv.org/abs/2402.11530)] | [🏠[Code](https://github.com/BAAI-DCAI/Bunny)] | [🤗[Model](https://huggingface.co/BAAI/Bunny-v1_0-3B)]

""")

tos_markdown = ("""
### Terms of use
By using this service, users are required to agree to the following terms:
The service is a research preview intended for non-commercial use only. 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.
""")

learn_more_markdown = ("""
### License
This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses. The content of this project itself is licensed under the Apache license 2.0.
""")

block_css = """
.centered {
    text-align: center;
}
#buttons button {
    min-width: min(120px,100%);
}
#file-downloader {
    min-width: min(120px,100%);
    height: 50px;
}
"""


def trigger_download(doc_path):
    return doc_path


def build_demo(embed_mode):
    textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False)
    with gr.Blocks(title="Bunny", theme=gr.themes.Default(primary_hue="blue", secondary_hue="lime"),
                   css=block_css) as demo:
        state = gr.State()

        if not embed_mode:
            gr.Markdown(title_markdown)

        with gr.Row():
            with gr.Column(scale=4):
                with gr.Row(elem_id="model_selector_row"):
                    model_selector = gr.Dropdown(
                        choices=models,
                        value=models[0] if len(models) > 0 else "",
                        interactive=True,
                        show_label=False,
                        container=False,
                        allow_custom_value=True
                    )

                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/example_1.png", "What is the astronaut holding in his hand?"],
                    [f"{cur_dir}/examples/example_2.png", "Why is the image funny?"],
                ], 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", )

                file_output = gr.components.File(label="Download Document", visible=True, elem_id="file-downloader")
            with gr.Column(scale=8):
                chatbot = gr.Chatbot(elem_id="chatbot", label="Bunny Chatbot",
                                     avatar_images=[f"{cur_dir}/examples/user.png", f"{cur_dir}/examples/icon.jpg"],
                                     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")

                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)
                    # 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)
                    save_conversation_btn = gr.Button(value="🗃️  Save", 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, regenerate_btn, clear_btn, save_conversation_btn]

        upvote_btn.click(
            upvote_last_response,
            [state, model_selector],
            [textbox, upvote_btn, downvote_btn]
        )
        downvote_btn.click(
            downvote_last_response,
            [state, model_selector],
            [textbox, upvote_btn, downvote_btn]
        )

        regenerate_btn.click(
            regenerate,
            [state, image_process_mode],
            [state, chatbot, textbox, imagebox] + btn_list,
            queue=False
        ).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,
            queue=False
        )

        save_conversation_btn.click(
            save_conversation,
            inputs=[state],
            outputs=file_output
        )

        textbox.submit(
            add_text,
            [state, textbox, imagebox, image_process_mode],
            [state, chatbot, textbox, imagebox] + btn_list,
            queue=False
        ).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,
            queue=False
        ).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,
                queue=False
            )
        elif args.model_list_mode == "reload":
            demo.load(
                load_demo_refresh_model_list,
                None,
                [state, model_selector],
                queue=False
            )
        else:
            raise ValueError(f"Unknown model list mode: {args.model_list_mode}")

    return demo


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="127.0.0.1")
    parser.add_argument("--port", type=int)
    parser.add_argument("--concurrency-count", type=int, default=10)
    parser.add_argument("--model-list-mode", type=str, default="once",
                        choices=["once", "reload"])
    parser.add_argument("--controller-url", type=str, default="http://localhost:10000")
    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()
    #logger.info(f"args: {args}")

    models = ['qnguyen3/nanoLLaVA']
    #logger.info(args)

    concurrency_count = int(os.getenv("concurrency_count", 5))

    controller_proc = start_controller()
    model_path = 'qnguyen3/nanoLLaVA'
    worker_proc = start_worker(model_path)
    time.sleep(10)
    exit_status = 0
    
    demo = build_demo(args.embed)
    demo.launch(
        server_name=args.host,
        server_port=args.port,
        share=args.share,
        debug=True,
        max_threads=10)
    #     )
    # except Exception as e:
    #     print(e)
    #     exit_status = 1
    # finally:
    #     worker_proc.kill()
    #     controller_proc.kill()
    #     sys.exit(exit_status)