app.py

import gradio as gr
import spaces
import os
import time
import json
from PIL import Image
import functools
from transformers import AutoProcessor, Idefics2ForConditionalGeneration
from models.conversation import conv_templates
from typing import List
processor = AutoProcessor.from_pretrained("MFuyu/mantis-8b-idefics2-video-eval_8192_lora")
model = Idefics2ForConditionalGeneration.from_pretrained("MFuyu/mantis-8b-idefics2-video-eval_8192_lora")
conv_template = conv_templates["idefics_2"]

with open("./examples/data_subset.json", 'r') as f:
    examples = json.load(f)

for item in examples:
    video_id = item['images'][0].split("_")[0]
    item['images'] = [os.path.join("./examples", video_id, x) for x in item['images']]

prompt = "Suppose you are an expert in judging and evaluating the quality of AI-generated videos, \nplease watch the following frames of a given video and see the text prompt for generating the video, \nthen give scores from 7 different dimensions:\n(1) visual quality, \n(2) object consistency,\n(3) dynamic degree,\n(4) motion smoothness,\n(5) text-to-video alignment,\n(6) factual consistency, \n(7) overall score\nfor each dimension, output a number from [1,2,3], in which '1' stands for 'Bad', '2' stands for 'Average', '3' stands for 'Good'.\nHere is an output example: \nvisual quality: 3\nobject consistency: 2 \ndynamic degree: 2\nmotion smoothness: 1\ntext-to-video alignment: 1\nfactual consistency: 2\noverall score: 1\n\nFor this item, the text prompt is the beautiful girl, long hair,walk on the sity street, red cloth  ,\nall the frames of video are as follows: \n\n"
@spaces.GPU
def generate(text:str, images:List[Image.Image], history: List[dict], **kwargs):
    global processor, model
    model = model.to("cuda") if model.device.type != "cuda" else model
    if not images:
        images = None
    
    user_role = conv_template.roles[0]
    assistant_role = conv_template.roles[1]
    
    idefics_2_message = []
    cur_img_idx = 0
    
    print(history)
    for i, message in enumerate(history):
        if message["role"] == user_role:
            idefics_2_message.append({
                "role": user_role,
                "content": []
            })
            message_text = message["text"]
            num_image_tokens_in_text = message_text.count("<image>")
            if num_image_tokens_in_text > 0:
                sub_texts = [x.strip() for x in message_text.split("<image>")]
                if sub_texts[0]:
                    idefics_2_message[-1]["content"].append({"type": "text", "text": sub_texts[0]})
                for sub_text in sub_texts[1:]:
                    idefics_2_message[-1]["content"].append({"type": "image"})
                    if sub_text:
                        idefics_2_message.append({
                            "role": user_role,
                            "content": [{"type": "text", "text": sub_text}]
                        })
            else:
                idefics_2_message[-1]["content"].append({"type": "text", "text": message_text})
        elif message["role"] == assistant_role:
            if i == len(history) - 1 and not message["text"]:
                break
            idefics_2_message.append({
                "role": assistant_role,
                "content": [{"type": "text", "text": message["text"]}]
            })
    if text:
        assert idefics_2_message[-1]["role"] == assistant_role and not idefics_2_message[-1]["content"], "Internal error"
        idefics_2_message.append({
            "role": user_role,
            "content": [{"type": "text", "text": text}]
        })
    
    print(idefics_2_message)
    prompt = processor.apply_chat_template(idefics_2_message, add_generation_prompt=True) 
    
    images = [Image.open(x) for x in images]
    inputs = processor(text=prompt, images=images, return_tensors="pt")
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    outputs = model.generate(**inputs, max_new_tokens=1024)
    generated_text = processor.decode(outputs[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
    return generated_text

def enable_next_image(uploaded_images, image):
    uploaded_images.append(image)
    return uploaded_images, gr.MultimodalTextbox(value=None, interactive=False)

def add_message(history, message):
    if message["files"]:
        for file in message["files"]:
            history.append([(file,), None])
    if message["text"]:
        history.append([message["text"], None])
    return history, gr.MultimodalTextbox(value=None)

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


def get_chat_history(history):
    chat_history = []
    user_role = conv_template.roles[0]
    assistant_role = conv_template.roles[1]
    for i, message in enumerate(history):
        if isinstance(message[0], str):
            chat_history.append({"role": user_role, "text": message[0]})
            if i != len(history) - 1:
                assert message[1], "The bot message is not provided, internal error"
                chat_history.append({"role": assistant_role, "text": message[1]})
            else:
                assert not message[1], "the bot message internal error, get: {}".format(message[1])
                chat_history.append({"role": assistant_role, "text": ""})
    return chat_history


def get_chat_images(history):
    images = []
    for message in history:
        if isinstance(message[0], tuple):
            images.extend(message[0])
    return images


def bot(history):
    cur_messages = {"text": "", "images": []}
    for message in history[::-1]:
        if message[1]:
            break
        if isinstance(message[0], str):
            cur_messages["text"] = message[0] + " " + cur_messages["text"]
        elif isinstance(message[0], tuple):
            cur_messages["images"].extend(message[0])
    cur_messages["text"] = cur_messages["text"].strip()
    cur_messages["images"] = cur_messages["images"][::-1]
    if not cur_messages["text"]:
        raise gr.Error("Please enter a message")
    if cur_messages['text'].count("<image>") < len(cur_messages['images']):
        gr.Warning("The number of images uploaded is more than the number of <image> placeholders in the text. Will automatically prepend <image> to the text.")
        cur_messages['text'] += "<image> "* (len(cur_messages['images']) - cur_messages['text'].count("<image>"))
        history[-1][0] = cur_messages["text"]
    if cur_messages['text'].count("<image>") > len(cur_messages['images']):
        gr.Warning("The number of images uploaded is less than the number of <image> placeholders in the text. Will automatically remove extra <image> placeholders from the text.")
        cur_messages['text'] = cur_messages['text'][::-1].replace("<image>"[::-1], "", cur_messages['text'].count("<image>") - len(cur_messages['images']))[::-1]
        history[-1][0] = cur_messages["text"]
        
    
    
    chat_history = get_chat_history(history)
    chat_images = get_chat_images(history)
    
    generation_kwargs = {
        "max_new_tokens": 4096,
        "num_beams": 1,
        "do_sample": False
    }
    
    response = generate(None, chat_images, chat_history, **generation_kwargs) 
    return response
    # for _output in response:
    #     history[-1][1] = _output
    #     time.sleep(0.05)
    #     yield history

def get_images(video_folder:str):
    """
    video folder contains images files like {video_folder_name}_00.jpg, {video_folder_name}_01.jpg, ...
    """
    images = []
    for file in os.listdir(video_folder):
        if file.endswith(".jpg"):
            images.append(Image.open(os.path.join(video_folder, file)))
    # sort images by name
    images.sort(key=lambda x: int(x.filename.split("_")[-1].split(".")[0]))
    return images
        
def build_demo():
    with gr.Blocks() as demo:
        
        gr.Markdown(""" # Mantis
Mantis is a multimodal conversational AI model that can chat with users about images and text. It's optimized for multi-image reasoning, where inverleaved text and images can be used to generate responses.

### [Paper](https://arxiv.org/abs/2405.01483) | [Github](https://github.com/TIGER-AI-Lab/Mantis) | [Models](https://huggingface.co/collections/TIGER-Lab/mantis-6619b0834594c878cdb1d6e4) | [Dataset](https://huggingface.co/datasets/TIGER-Lab/Mantis-Instruct) | [Website](https://tiger-ai-lab.github.io/Mantis/)            
        """)
        
        gr.Markdown("""## Chat with Mantis
        Mantis supports interleaved text-image input format, where you can simply use the placeholder `<image>` to indicate the position of uploaded images.
        The model is optimized for multi-image reasoning, while preserving the ability to chat about text and images in a single conversation.
        (The model currently serving is [🤗 TIGER-Lab/Mantis-8B-siglip-llama3](https://huggingface.co/TIGER-Lab/Mantis-8B-siglip-llama3))
        """)
        
        chatbot = gr.Chatbot(line_breaks=True)
        chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload images. Please use <image> to indicate the position of uploaded images", show_label=True)
        
        chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
        
        """
        with gr.Accordion(label='Advanced options', open=False):
            temperature = gr.Slider(
                label='Temperature',
                minimum=0.1,
                maximum=2.0,
                step=0.1,
                value=0.2,
                interactive=True
            )
            top_p = gr.Slider(
                label='Top-p',
                minimum=0.05,
                maximum=1.0,
                step=0.05,
                value=1.0,
                interactive=True
            )
        """

        bot_msg = chat_msg.success(bot, chatbot, chatbot, api_name="bot_response")
        
        chatbot.like(print_like_dislike, None, None)

        with gr.Row():
            send_button = gr.Button("Send")
            clear_button = gr.ClearButton([chatbot, chat_input])

        send_button.click(
            add_message, [chatbot, chat_input], [chatbot, chat_input]
        ).then(
            bot, chatbot, chatbot, api_name="bot_response"
        )
        dummy_id = gr.Textbox("dummy_id", label="dummy_id", visible=False)
        dummy_output = gr.Textbox("dummy_output", label="dummy_output", visible=False)
        
        gr.Examples(
            examples=[
                [
                    item['id'], 
                    {
                        "text": item['conversations'][0]['value'],
                        "files": item['images']
                    },
                    item['conversations'][1]['value']
                ] for item in examples
            ],
            inputs=[dummy_id, chat_input, dummy_output],
        )        
        
        gr.Markdown("""
## Citation
```
@article{jiang2024mantis,
  title={MANTIS: Interleaved Multi-Image Instruction Tuning},
  author={Jiang, Dongfu and He, Xuan and Zeng, Huaye and Wei, Con and Ku, Max and Liu, Qian and Chen, Wenhu},
  journal={arXiv preprint arXiv:2405.01483},
  year={2024}
}
```""")
    return demo    
    

if __name__ == "__main__":
    demo = build_demo()
    demo.launch()