app.py

import subprocess
subprocess.run('pip install flash-attn==2.7.0.post2 --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

import spaces
import os
import re
import logging
from typing import List, Any
from threading import Thread

import torch
import gradio as gr
from transformers import AutoModelForCausalLM, TextIteratorStreamer

model_name = 'AIDC-AI/Ovis2-2B'
use_thread = True

# 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__))

logging.getLogger("httpx").setLevel(logging.WARNING)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def initialize_gen_kwargs():
    return {
        "max_new_tokens": 1536,
        "do_sample": False,
        "top_p": None,
        "top_k": None,
        "temperature": None,
        "repetition_penalty": 1.05,
        "eos_token_id": model.generation_config.eos_token_id,
        "pad_token_id": text_tokenizer.pad_token_id,
        "use_cache": True
    }

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

@spaces.GPU
def ovis_chat(chatbot: List[List[str]], image_input: Any):
    conversations, model_inputs = prepare_inputs(chatbot, image_input)
    gen_kwargs = initialize_gen_kwargs()

    with torch.inference_mode():
        generate_func = lambda: model.generate(**model_inputs, **gen_kwargs, streamer=streamer)
        
        if use_thread:
            thread = Thread(target=generate_func)
            thread.start()
        else:
            generate_func()

        response = ""
        for new_text in streamer:
            response += new_text
            chatbot[-1][1] = response
            yield chatbot

        if use_thread:
            thread.join()

    log_conversation(chatbot)

    
def prepare_inputs(chatbot: List[List[str]], image_input: Any):
    # conversations = [{
    #     "from": "system",
    #     "value": "You are a helpful assistant, and your task is to provide reliable and structured responses to users."
    # }]
    conversations= []

    for query, response in chatbot[:-1]:
        conversations.extend([
            {"from": "human", "value": query},
            {"from": "gpt", "value": response}
        ])
    
    last_query = chatbot[-1][0].replace(image_placeholder, '')
    conversations.append({"from": "human", "value": last_query})

    if image_input is not None:
        for conv in conversations:
            if conv["from"] == "human":
                conv["value"] = f'{image_placeholder}\n{conv["value"]}'
                break

    logger.info(conversations)
    
    prompt, input_ids, pixel_values = model.preprocess_inputs(conversations, [image_input], max_partition=16)
    attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
    
    model_inputs = {
        "inputs": input_ids.unsqueeze(0).to(device=model.device),
        "attention_mask": attention_mask.unsqueeze(0).to(device=model.device),
        "pixel_values": [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)] if image_input is not None else [None]
    }
    
    return conversations, model_inputs

def log_conversation(chatbot):
    logger.info("[OVIS_CONV_START]")
    [print(f'Q{i}:\n {request}\nA{i}:\n {answer}') for i, (request, answer) in enumerate(chatbot, 1)]
    logger.info("[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], theme=gr.themes.Ocean()) 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/ovis2_caption0.jpg", "Describe the image."],
                    [f"{cur_dir}/examples/ovis2_ocr1.jpg", "Recognize text in images."],
                    [f"{cur_dir}/examples/ovsi2_know0.png", "What is the animal in the picture?"],
                ],
                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()