import time
from threading import Thread

import gradio as gr
import torch
from PIL import Image
from transformers import AutoProcessor, LlavaForConditionalGeneration, TextIteratorStreamer, TextStreamer

# import spaces
import argparse

from llava_llama3.model.builder import load_pretrained_model
from llava_llama3.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava_llama3.conversation import conv_templates, SeparatorStyle
from llava_llama3.utils import disable_torch_init
from llava_llama3.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path
from llava_llama3.serve.cli import chat_llava

import requests
from io import BytesIO
import base64
import os
import glob
import pandas as pd
from tqdm import tqdm
import json

root_path = os.path.dirname(os.path.abspath(__file__))
print(f'\033[92m{root_path}\033[0m')
os.environ['GRADIO_TEMP_DIR'] = root_path

parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="/mnt/nvme1n1/toby/LLaVA/checkpoints/0806_onlyllava_llava-finma-8B-v0.4-v8/checkpoint-2000")
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--conv-mode", type=str, default="llama_3")
parser.add_argument("--temperature", type=float, default=0.7)
parser.add_argument("--max-new-tokens", type=int, default=512)
parser.add_argument("--load-8bit", action="store_true")
parser.add_argument("--load-4bit", action="store_true")
args = parser.parse_args()

# Load model
tokenizer, llava_model, image_processor, context_len = load_pretrained_model(
    args.model_path, 
    None, 
    'llava_llama3', 
    args.load_8bit, 
    args.load_4bit, 
    device=args.device)

def bot_streaming(message, history):
    print(message)
    image_file = None
    if message["files"]:
        if type(message["files"][-1]) == dict:
            image_file = message["files"][-1]["path"]
        else:
            image_file = message["files"][-1]
    else:
        for hist in history:
            if type(hist[0]) == tuple:
                image_file = hist[0][0]
                
    if image_file is None:
        gr.Error("You need to upload an image for LLaVA to work.")
        return
    
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    def generate():
        print('\033[92mRunning chat\033[0m')
        output = chat_llava(
                    args=args,
                    image_file=image_file,
                    text=message['text'],
                    tokenizer=tokenizer,
                    model=llava_model,
                    image_processor=image_processor,
                    context_len=context_len,
                    streamer=streamer)
        return output

    thread = Thread(target=generate)
    thread.start()
    # thread.join()

    buffer = ""
    # output = generate()
    for new_text in streamer:
        buffer += new_text
        generated_text_without_prompt = buffer
        time.sleep(0.06)
        yield generated_text_without_prompt

chatbot = gr.Chatbot(scale=1)
chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)
with gr.Blocks(fill_height=True) as demo:
    gr.ChatInterface(
        fn=bot_streaming,
        title="FinLLaVA Demo",
        examples=[
            {"text": "What is in this picture?", "files": ["http://images.cocodataset.org/val2017/000000039769.jpg"]},
        ],
        description="",
        stop_btn="Stop Generation",
        multimodal=True,
        textbox=chat_input,
        chatbot=chatbot,
    )

demo.queue(api_open=False)
demo.launch(show_api=False, share=False)