import spaces
import os
import re
import time
import gradio as gr
import torch
from transformers import AutoModelForCausalLM
from transformers import TextIteratorStreamer
from threading import Thread

model_name = 'AIDC-AI/Ovis1.6-Gemma2-9B'

# load model
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             multimodal_max_length=8192,
                                             trust_remote_code=True).to(device='cuda')
text_tokenizer = model.get_text_tokenizer()
visual_tokenizer = model.get_visual_tokenizer()
streamer = TextIteratorStreamer(text_tokenizer, skip_prompt=True, skip_special_tokens=True)
image_placeholder = '<image>'
cur_dir = os.path.dirname(os.path.abspath(__file__))

def submit_chat(chatbot, text_input):
    response = ''
    chatbot.append((text_input, response))
    return chatbot ,''

@spaces.GPU
def ovis_chat(chatbot, image_input):
    # preprocess inputs
    conversations = []
    response = ""
    text_input = chatbot[-1][0]
    for query, response in chatbot[:-1]:
        conversations.append({
            "from": "human",
            "value": query
        })
        conversations.append({
            "from": "gpt",
            "value": response
        })
    text_input = text_input.replace(image_placeholder, '')
    conversations.append({
        "from": "human",
        "value": text_input
    })
    if image_input is not None:
        conversations[0]["value"] = image_placeholder + '\n' + conversations[0]["value"]
    prompt, input_ids, pixel_values = model.preprocess_inputs(conversations, [image_input])
    attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
    input_ids = input_ids.unsqueeze(0).to(device=model.device)
    attention_mask = attention_mask.unsqueeze(0).to(device=model.device)
    if image_input is None:
        pixel_values = [None]
    else:
        pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)]

    with torch.inference_mode():
        gen_kwargs = dict(
            max_new_tokens=512,
            do_sample=False,
            top_p=None,
            top_k=None,
            temperature=None,
            repetition_penalty=None,
            eos_token_id=model.generation_config.eos_token_id,
            pad_token_id=text_tokenizer.pad_token_id,
            use_cache=True
        )
    response = ""
    thread = Thread(target=model.generate, 
                kwargs={"inputs": input_ids,
                        "pixel_values": pixel_values,
                        "attention_mask": attention_mask,
                        "streamer": streamer,
                        **gen_kwargs})
    thread.start()
    for new_text in streamer:
        response += new_text
        chatbot[-1][1] = response
        yield chatbot
    thread.join()
    # debug
    print('*'*60)
    print('*'*60)
    print('OVIS_CONV_START')
    for i, (request, answer) in enumerate(chatbot[:-1], 1):
        print(f'Q{i}:\n {request}')
        print(f'A{i}:\n {answer}')
    print('New_Q:\n', text_input)
    print('New_A:\n', response)
    print('OVIS_CONV_END')

def clear_chat():
    return [], None, ""

with open(f"{cur_dir}/resource/logo.svg", "r", encoding="utf-8") as svg_file:
    svg_content = svg_file.read()
font_size = "2.5em"
svg_content = re.sub(r'(<svg[^>]*)(>)', rf'\1 height="{font_size}" style="vertical-align: middle; display: inline-block;"\2', svg_content)
html = f"""
<p align="center" style="font-size: {font_size}; line-height: 1;">
    <span style="display: inline-block; vertical-align: middle;">{svg_content}</span>
    <span style="display: inline-block; vertical-align: middle;">{model_name.split('/')[-1]}</span>
</p>
<center><font size=3><b>Ovis</b> has been open-sourced on <a href='https://huggingface.co/{model_name}'>😊 Huggingface</a> and <a href='https://github.com/AIDC-AI/Ovis'>🌟 GitHub</a>. If you find Ovis useful, a like❤️ or a star🌟 would be appreciated.</font></center>
"""

latex_delimiters_set = [{
        "left": "\\(",
        "right": "\\)",
        "display": False 
    }, {
        "left": "\\begin{equation}",
        "right": "\\end{equation}",
        "display": True 
    }, {
        "left": "\\begin{align}",
        "right": "\\end{align}",
        "display": True
    }, {
        "left": "\\begin{alignat}",
        "right": "\\end{alignat}",
        "display": True
    }, {
        "left": "\\begin{gather}",
        "right": "\\end{gather}",
        "display": True
    }, {
        "left": "\\begin{CD}",
        "right": "\\end{CD}",
        "display": True
    }, {
        "left": "\\[",
        "right": "\\]",
        "display": True
    }]

text_input = gr.Textbox(label="prompt", placeholder="Enter your text here...", lines=1, container=False)
with gr.Blocks(title=model_name.split('/')[-1]) as demo:
    gr.HTML(html)
    with gr.Row():
        with gr.Column(scale=3):
            image_input = gr.Image(label="image", height=350, type="pil")
            gr.Examples(
                examples=[
                    [f"{cur_dir}/examples/case0.png", "Find the area of the shaded region."],
                    [f"{cur_dir}/examples/case1.png", "explain this model to me."],
                    [f"{cur_dir}/examples/case2.png", "What is net profit margin as a percentage of total revenue?"],
                ],
                inputs=[image_input, text_input]
            )
        with gr.Column(scale=7):
            chatbot = gr.Chatbot(label="Ovis", layout="panel", height=600, show_copy_button=True, latex_delimiters=latex_delimiters_set)
            text_input.render()
            with gr.Row():
                send_btn = gr.Button("Send", variant="primary")
                clear_btn = gr.Button("Clear", variant="secondary")

    send_click_event = send_btn.click(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot)
    submit_event = text_input.submit(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot)
    clear_btn.click(clear_chat, outputs=[chatbot, image_input, text_input])

demo.launch()