# from .demo_modelpart import InferenceDemo
import gradio as gr
import os
from threading import Thread

# import time
import cv2

import datetime
# import copy
import torch

import spaces
import numpy as np

from llava import conversation as conversation_lib
from llava.constants import DEFAULT_IMAGE_TOKEN


from llava.constants import (
    IMAGE_TOKEN_INDEX,
    DEFAULT_IMAGE_TOKEN,
    DEFAULT_IM_START_TOKEN,
    DEFAULT_IM_END_TOKEN,
)
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import (
    tokenizer_image_token,
    process_images,
    get_model_name_from_path,
    KeywordsStoppingCriteria,
)

from serve_constants import html_header, bibtext, learn_more_markdown, tos_markdown

import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer, TextIteratorStreamer

import hashlib
import PIL
import base64
import json

import datetime
import gradio as gr
import gradio_client
import subprocess
import sys

from huggingface_hub import HfApi
from huggingface_hub import login
from huggingface_hub import revision_exists

login(token=os.environ["HF_TOKEN"],
      write_permission=True)

api = HfApi()
repo_name = os.environ["LOG_REPO"]

external_log_dir = "./logs"
LOGDIR = external_log_dir


def install_gradio_4_35_0():
    current_version = gr.__version__
    if current_version != "4.35.0":
        print(f"Current Gradio version: {current_version}")
        print("Installing Gradio 4.35.0...")
        subprocess.check_call([sys.executable, "-m", "pip", "install", "gradio==4.35.0", "--force-reinstall"])
        print("Gradio 4.35.0 installed successfully.")
    else:
        print("Gradio 4.35.0 is already installed.")

# Call the function to install Gradio 4.35.0 if needed
install_gradio_4_35_0()

import gradio as gr
import gradio_client
print(f"Gradio version: {gr.__version__}")
print(f"Gradio-client version: {gradio_client.__version__}")

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

class InferenceDemo(object):
    def __init__(
        self, args, model_path, tokenizer, model, image_processor, context_len
    ) -> None:
        disable_torch_init()

        self.tokenizer, self.model, self.image_processor, self.context_len = (
            tokenizer,
            model,
            image_processor,
            context_len,
        )

        if "llama-2" in model_name.lower():
            conv_mode = "llava_llama_2"
        elif "v1" in model_name.lower() or "pulse" in model_name.lower():
            conv_mode = "llava_v1"
        elif "mpt" in model_name.lower():
            conv_mode = "mpt"
        elif "qwen" in model_name.lower():
            conv_mode = "qwen_1_5"
        else:
            conv_mode = "llava_v0"

        if args.conv_mode is not None and conv_mode != args.conv_mode:
            print(
                "[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format(
                    conv_mode, args.conv_mode, args.conv_mode
                )
            )
        else:
            args.conv_mode = conv_mode
        self.conv_mode = conv_mode
        self.conversation = conv_templates[args.conv_mode].copy()
        self.num_frames = args.num_frames


def is_valid_video_filename(name):
    video_extensions = ["avi", "mp4", "mov", "mkv", "flv", "wmv", "mjpeg"]

    ext = name.split(".")[-1].lower()

    if ext in video_extensions:
        return True
    else:
        return False

def is_valid_image_filename(name):
    image_extensions = ["jpg", "jpeg", "png", "bmp", "gif", "tiff", "webp", "heic", "heif", "jfif", "svg", "eps", "raw"] 

    ext = name.split(".")[-1].lower()

    if ext in image_extensions:
        return True
    else:
        return False


def sample_frames(video_file, num_frames):
    video = cv2.VideoCapture(video_file)
    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
    interval = total_frames // num_frames
    frames = []
    for i in range(total_frames):
        ret, frame = video.read()
        pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        if not ret:
            continue
        if i % interval == 0:
            frames.append(pil_img)
    video.release()
    return frames


def load_image(image_file):
    if image_file.startswith("http") or image_file.startswith("https"):
        response = requests.get(image_file)
        if response.status_code == 200:
            image = Image.open(BytesIO(response.content)).convert("RGB")
        else:
            print("failed to load the image")
    else:
        print("Load image from local file")
        print(image_file)
        image = Image.open(image_file).convert("RGB")

    return image


def clear_history(history):

    our_chatbot.conversation = conv_templates[our_chatbot.conv_mode].copy()

    return None


def clear_response(history):
    for index_conv in range(1, len(history)):
        # loop until get a text response from our model.
        conv = history[-index_conv]
        if not (conv[0] is None):
            break
    question = history[-index_conv][0]
    history = history[:-index_conv]
    return history, question


# def print_like_dislike(x: gr.LikeData):
#     print(x.index, x.value, x.liked)


def add_message(history, message):
    # history=[]
    global our_chatbot
    if len(history) == 0:
        our_chatbot = InferenceDemo(
            args, model_path, tokenizer, model, image_processor, context_len
        )

    for x in message["files"]:
        history.append(((x,), None))
    if message["text"] is not None:
        history.append((message["text"], None))
    return history, gr.MultimodalTextbox(value=None, interactive=False)


@spaces.GPU
def bot(history, temperature, top_p, max_output_tokens):
    print("### turn start history",history)
    print("### turn start conv",our_chatbot.conversation)
    text = history[-1][0]
    images_this_term = []
    text_this_term = ""
    # import pdb;pdb.set_trace()
    num_new_images = 0
    for i, message in enumerate(history[:-1]):
        if type(message[0]) is tuple:
            images_this_term.append(message[0][0])
            if is_valid_video_filename(message[0][0]):
                # 不接受视频
                raise ValueError("Video is not supported")
                num_new_images += our_chatbot.num_frames
            elif is_valid_image_filename(message[0][0]):
                print("#### Load image from local file",message[0][0])
                num_new_images += 1
            else:
                raise ValueError("Invalid image file")
        else:
            num_new_images = 0

    # for message in history[-i-1:]:
    #     images_this_term.append(message[0][0])

    assert len(images_this_term) > 0, "must have an image"
    # image_files = (args.image_file).split(',')
    # image = [load_image(f) for f in images_this_term if f]

    all_image_hash = []
    all_image_path = []
    for image_path in images_this_term:
        with open(image_path, "rb") as image_file:
            image_data = image_file.read()
            image_hash = hashlib.md5(image_data).hexdigest()
            all_image_hash.append(image_hash)
            image = PIL.Image.open(image_path).convert("RGB")
            t = datetime.datetime.now()
            filename = os.path.join(
                LOGDIR,
                "serve_images",
                f"{t.year}-{t.month:02d}-{t.day:02d}",
                f"{image_hash}.jpg",
            )
            all_image_path.append(filename)
            if not os.path.isfile(filename):
                os.makedirs(os.path.dirname(filename), exist_ok=True)
                print("image save to",filename)
                image.save(filename)
    
    image_list = []
    for f in images_this_term:
        if is_valid_video_filename(f):
            image_list += sample_frames(f, our_chatbot.num_frames)
        elif is_valid_image_filename(f):
            image_list.append(load_image(f))
        else:
            raise ValueError("Invalid image file")
    
    image_tensor = [
        process_images([f], our_chatbot.image_processor, our_chatbot.model.config)[0]
        .to(our_chatbot.model.device)
        for f in image_list
    ]


    image_tensor = torch.stack(image_tensor)
    image_token = DEFAULT_IMAGE_TOKEN * num_new_images
    # if our_chatbot.model.config.mm_use_im_start_end:
    #     inp = DEFAULT_IM_START_TOKEN + image_token + DEFAULT_IM_END_TOKEN + "\n" + inp
    # else:
    inp = text
    inp = image_token + "\n" + inp
    our_chatbot.conversation.append_message(our_chatbot.conversation.roles[0], inp)
    # image = None
    our_chatbot.conversation.append_message(our_chatbot.conversation.roles[1], None)
    prompt = our_chatbot.conversation.get_prompt()

    # input_ids = (
    #     tokenizer_image_token(
    #         prompt, our_chatbot.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
    #     )
    #     .unsqueeze(0)
    #     .to(our_chatbot.model.device)
    # )
    input_ids = tokenizer_image_token(
            prompt, our_chatbot.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
        ).unsqueeze(0).to(our_chatbot.model.device)
    # print("### input_id",input_ids)
    stop_str = (
        our_chatbot.conversation.sep
        if our_chatbot.conversation.sep_style != SeparatorStyle.TWO
        else our_chatbot.conversation.sep2
    )
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(
        keywords, our_chatbot.tokenizer, input_ids
    )
    # streamer = TextStreamer(
    #     our_chatbot.tokenizer, skip_prompt=True, skip_special_tokens=True
    # )
    streamer = TextIteratorStreamer(
        our_chatbot.tokenizer, skip_prompt=True, skip_special_tokens=True
    )
    print(our_chatbot.model.device)
    print(input_ids.device)
    print(image_tensor.device)
    
    # with torch.inference_mode():
    #     output_ids = our_chatbot.model.generate(
    #         input_ids,
    #         images=image_tensor,
    #         do_sample=True,
    #         temperature=0.7,
    #         top_p=1.0,
    #         max_new_tokens=4096,
    #         streamer=streamer,
    #         use_cache=False,
    #         stopping_criteria=[stopping_criteria],
    #     )

    # outputs = our_chatbot.tokenizer.decode(output_ids[0]).strip()
    # if outputs.endswith(stop_str):
    #     outputs = outputs[: -len(stop_str)]
    # our_chatbot.conversation.messages[-1][-1] = outputs

    # history[-1] = [text, outputs]
    
    # return history
    generate_kwargs = dict(
        inputs=input_ids,
        streamer=streamer,
        images=image_tensor,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        max_new_tokens=max_output_tokens,
        use_cache=False,
        stopping_criteria=[stopping_criteria],
    )
    
    t = Thread(target=our_chatbot.model.generate, kwargs=generate_kwargs)
    t.start()
    
    outputs = []
    for stream_token in streamer:
        outputs.append(stream_token)
        # print("### stream_token",stream_token)
        # our_chatbot.conversation.messages[-1][-1] = "".join(outputs)
        history[-1] = [text, "".join(outputs)]
        yield history
    our_chatbot.conversation.messages[-1][-1] = "".join(outputs)
    print("### turn end history", history)
    print("### turn end conv",our_chatbot.conversation)
        
    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "type": "chat",
            "model": "PULSE-7b",
            "state": history,
            "images": all_image_hash,
            "images_path": all_image_path
        }
        print("#### conv log",data)
        fout.write(json.dumps(data) + "\n")
    for upload_img in all_image_path:
        api.upload_file(
            path_or_fileobj=upload_img,
            path_in_repo=upload_img.replace("./logs/", ""),
            repo_id=repo_name,
            repo_type="dataset",
            # revision=revision,
            # ignore_patterns=["data*"]
        )
    # upload json
    api.upload_file(
        path_or_fileobj=get_conv_log_filename(),
        path_in_repo=get_conv_log_filename().replace("./logs/", ""),
        repo_id=repo_name,
        repo_type="dataset")
        
    

txt = gr.Textbox(
    scale=4,
    show_label=False,
    placeholder="Enter text and press enter.",
    container=False,
)

with gr.Blocks(
    css=".message-wrap.svelte-1lcyrx4>div.svelte-1lcyrx4  img {min-width: 40px}",
) as demo:

    cur_dir = os.path.dirname(os.path.abspath(__file__))
    # gr.Markdown(title_markdown)
    gr.HTML(html_header)
    
    with gr.Column():
        with gr.Accordion("Parameters", open=False) as parameter_row:
                temperature = gr.Slider(
                    minimum=0.0,
                    maximum=1.0,
                    value=0.0,
                    step=0.1,
                    interactive=True,
                    label="Temperature",
                )
                top_p = gr.Slider(
                    minimum=0.0,
                    maximum=1.0,
                    value=1,
                    step=0.1,
                    interactive=True,
                    label="Top P",
                )
                max_output_tokens = gr.Slider(
                    minimum=0,
                    maximum=8192,
                    value=4096,
                    step=256,
                    interactive=True,
                    label="Max output tokens",
                )
        with gr.Row():
            chatbot = gr.Chatbot([], elem_id="PULSE", bubble_full_width=False, height=750)

        with gr.Row():
            upvote_btn = gr.Button(value="👍  Upvote", interactive=True)
            downvote_btn = gr.Button(value="👎  Downvote", interactive=True)
            flag_btn = gr.Button(value="⚠️  Flag", interactive=True)
            # stop_btn = gr.Button(value="⏹️  Stop Generation", interactive=True)
            regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=True)
            clear_btn = gr.Button(value="🗑️  Clear history", interactive=True)
            

        chat_input = gr.MultimodalTextbox(
            interactive=True,
            file_types=["image"],
            placeholder="Enter message or upload file...",
            show_label=False,
            submit_btn="🚀"
        )

        print(cur_dir)
        gr.Examples(
                examples_per_page=5,
                examples=[
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/ecg_example2.png",
                            ],
                            "text": "What are the main features in this ECG image?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/ecg_example1.jpg",
                            ],
                            "text": "What can be inferred from the pattern of the qR complexes and rS complexes in the leads of this ECG image?",
                        },
                    ]
                ],
                inputs=[chat_input],
                label="Image",
            )

        gr.Markdown(tos_markdown)
        gr.Markdown(learn_more_markdown)
        gr.Markdown(bibtext)

    chat_msg = chat_input.submit(
        add_message, [chatbot, chat_input], [chatbot, chat_input]
    )
    bot_msg = chat_msg.then(bot, [chatbot,temperature, top_p, max_output_tokens], chatbot, api_name="bot_response")
    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])

    # chatbot.like(print_like_dislike, None, None)
    clear_btn.click(
        fn=clear_history, inputs=[chatbot], outputs=[chatbot], api_name="clear_all"
    )


demo.queue()
    
if __name__ == "__main__":
    import argparse

    argparser = argparse.ArgumentParser()
    argparser.add_argument("--server_name", default="0.0.0.0", type=str)
    argparser.add_argument("--port", default="6123", type=str)
    argparser.add_argument(
        "--model_path", default="PULSE-ECG/PULSE-7B", type=str
    )
    # argparser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    argparser.add_argument("--model-base", type=str, default=None)
    argparser.add_argument("--num-gpus", type=int, default=1)
    argparser.add_argument("--conv-mode", type=str, default=None)
    argparser.add_argument("--temperature", type=float, default=0.0)
    argparser.add_argument("--max-new-tokens", type=int, default=1024)
    argparser.add_argument("--num_frames", type=int, default=16)
    argparser.add_argument("--load-8bit", action="store_true")
    argparser.add_argument("--load-4bit", action="store_true")
    argparser.add_argument("--debug", action="store_true")

    args = argparser.parse_args()

    model_path = args.model_path
    filt_invalid = "cut"
    model_name = get_model_name_from_path(args.model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit)
    print("### image_processor",image_processor)
    model=model.to(torch.device('cuda'))
    our_chatbot = None
    demo.launch()