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import gradio as gr |
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import spaces |
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import os |
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import time |
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from PIL import Image |
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import functools |
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from models.mllava import MLlavaProcessor, LlavaForConditionalGeneration, chat_mllava_stream, MLlavaForConditionalGeneration, chat_mllava |
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from models.conversation import conv_templates |
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from typing import List |
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processor = MLlavaProcessor.from_pretrained("TIGER-Lab/Mantis-8B-siglip-llama3") |
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model = LlavaForConditionalGeneration.from_pretrained("TIGER-Lab/Mantis-8B-siglip-llama3") |
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conv_template = conv_templates['llama_3'] |
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@spaces.GPU |
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def generate_stream(text:str, images:List[Image.Image], history: List[dict], **kwargs): |
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global processor, model |
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model = model.to("cuda") |
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if not images: |
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images = None |
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for text, history in chat_mllava_stream(text, images, model, processor, history=history, **kwargs): |
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yield text |
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return text |
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@spaces.GPU |
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def generate(text:str, images:List[Image.Image], history: List[dict], **kwargs): |
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global processor, model |
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model = model.to("cuda") |
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if not images: |
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images = None |
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generated_text, history = chat_mllava(text, images, model, processor, history=history, **kwargs) |
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return generated_text |
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def enable_next_image(uploaded_images, image): |
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uploaded_images.append(image) |
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return uploaded_images, gr.MultimodalTextbox(value=None, interactive=False) |
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def add_message(history, message): |
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if message["files"]: |
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for file in message["files"]: |
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history.append([(file,), None]) |
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if message["text"]: |
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history.append([message["text"], None]) |
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return history, gr.MultimodalTextbox(value=None) |
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def print_like_dislike(x: gr.LikeData): |
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print(x.index, x.value, x.liked) |
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def get_chat_history(history): |
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chat_history = [] |
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user_role = conv_template.roles[0] |
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assistant_role = conv_template.roles[1] |
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for i, message in enumerate(history): |
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if isinstance(message[0], str): |
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chat_history.append({"role": user_role, "text": message[0]}) |
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if i != len(history) - 1: |
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assert message[1], "The bot message is not provided, internal error" |
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chat_history.append({"role": assistant_role, "text": message[1]}) |
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else: |
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assert not message[1], "the bot message internal error, get: {}".format(message[1]) |
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chat_history.append({"role": assistant_role, "text": ""}) |
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return chat_history |
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def get_chat_images(history): |
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images = [] |
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for message in history: |
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if isinstance(message[0], tuple): |
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images.extend(message[0]) |
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return images |
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def bot(history): |
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print(history) |
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cur_messages = {"text": "", "images": []} |
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for message in history[::-1]: |
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if message[1]: |
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break |
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if isinstance(message[0], str): |
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cur_messages["text"] = message[0] + " " + cur_messages["text"] |
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elif isinstance(message[0], tuple): |
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cur_messages["images"].extend(message[0]) |
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cur_messages["text"] = cur_messages["text"].strip() |
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cur_messages["images"] = cur_messages["images"][::-1] |
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if not cur_messages["text"]: |
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raise gr.Error("Please enter a message") |
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if cur_messages['text'].count("<image>") < len(cur_messages['images']): |
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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.") |
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cur_messages['text'] = "<image> "* (len(cur_messages['images']) - cur_messages['text'].count("<image>")) + cur_messages['text'] |
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history[-1][0] = cur_messages["text"] |
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if cur_messages['text'].count("<image>") > len(cur_messages['images']): |
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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.") |
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cur_messages['text'] = cur_messages['text'][::-1].replace("<image>"[::-1], "", cur_messages['text'].count("<image>") - len(cur_messages['images']))[::-1] |
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history[-1][0] = cur_messages["text"] |
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chat_history = get_chat_history(history) |
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chat_images = get_chat_images(history) |
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generation_kwargs = { |
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"max_new_tokens": 4096, |
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"num_beams": 1, |
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"do_sample": False |
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} |
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response = generate_stream(None, chat_images, chat_history, **generation_kwargs) |
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for _output in response: |
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history[-1][1] = _output |
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time.sleep(0.05) |
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yield history |
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def build_demo(): |
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with gr.Blocks() as demo: |
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gr.Markdown(""" # Mantis |
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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. |
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### [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/) |
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""") |
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gr.Markdown("""## Chat with Mantis |
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Mantis supports interleaved text-image input format, where you can simply use the placeholder `<image>` to indicate the position of uploaded images. |
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The model is optimized for multi-image reasoning, while preserving the ability to chat about text and images in a single conversation. |
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(The model currently serving is [🤗 TIGER-Lab/Mantis-8B-siglip-llama3](https://huggingface.co/TIGER-Lab/Mantis-8B-siglip-llama3)) |
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""") |
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chatbot = gr.Chatbot(line_breaks=True) |
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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) |
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chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input]) |
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""" |
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with gr.Accordion(label='Advanced options', open=False): |
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temperature = gr.Slider( |
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label='Temperature', |
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minimum=0.1, |
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maximum=2.0, |
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step=0.1, |
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value=0.2, |
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interactive=True |
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) |
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top_p = gr.Slider( |
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label='Top-p', |
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minimum=0.05, |
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maximum=1.0, |
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step=0.05, |
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value=1.0, |
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interactive=True |
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) |
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""" |
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bot_msg = chat_msg.success(bot, chatbot, chatbot, api_name="bot_response") |
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chatbot.like(print_like_dislike, None, None) |
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with gr.Row(): |
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send_button = gr.Button("Send") |
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clear_button = gr.ClearButton([chatbot, chat_input]) |
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send_button.click( |
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add_message, [chatbot, chat_input], [chatbot, chat_input] |
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).then( |
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bot, chatbot, chatbot, api_name="bot_response" |
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) |
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gr.Examples( |
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examples=[ |
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{ |
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"text": "<image> <image> <image> Which image shows a different mood of character from the others?", |
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"files": ["./examples/image12.jpg", "./examples/image13.jpg", "./examples/image14.jpg"] |
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}, |
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{ |
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"text": "<image> <image> What's the difference between these two images? Please describe as much as you can.", |
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"files": ["./examples/image1.jpg", "./examples/image2.jpg"] |
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}, |
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{ |
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"text": "<image> <image> Which image shows an older dog?", |
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"files": ["./examples/image8.jpg", "./examples/image9.jpg"] |
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}, |
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{ |
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"text": "Write a description for the given image sequence in a single paragraph, what is happening in this episode?", |
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"files": ["./examples/image3.jpg", "./examples/image4.jpg", "./examples/image5.jpg", "./examples/image6.jpg", "./examples/image7.jpg"] |
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}, |
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{ |
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"text": "<image> <image> How many dices are there in image 1 and image 2 respectively?", |
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"files": ["./examples/image10.jpg", "./examples/image15.jpg"] |
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}, |
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], |
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inputs=[chat_input], |
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) |
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gr.Markdown(""" |
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## Citation |
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``` |
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@article{jiang2024mantis, |
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title={MANTIS: Interleaved Multi-Image Instruction Tuning}, |
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author={Jiang, Dongfu and He, Xuan and Zeng, Huaye and Wei, Con and Ku, Max and Liu, Qian and Chen, Wenhu}, |
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journal={arXiv preprint arXiv:2405.01483}, |
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year={2024} |
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} |
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```""") |
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return demo |
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if __name__ == "__main__": |
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demo = build_demo() |
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demo.launch() |
