app.py

# from .demo_modelpart import InferenceDemo
import gradio as gr
import os

# import time
import cv2


# import copy
import torch

import spaces
import numpy as np

from llava import conversation as conversation_lib
from llava.constants import DEFAULT_IMAGE_TOKEN


from llava.constants import (
    IMAGE_TOKEN_INDEX,
    DEFAULT_IMAGE_TOKEN,
    DEFAULT_IM_START_TOKEN,
    DEFAULT_IM_END_TOKEN,
)
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import (
    tokenizer_image_token,
    get_model_name_from_path,
    KeywordsStoppingCriteria,
)

from serve_constants import html_header

from PIL import Image

import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer

import gradio as gr
import gradio_client
import subprocess
import sys

def install_gradio_4_35_0():
    current_version = gr.__version__
    if current_version != "4.35.0":
        print(f"Current Gradio version: {current_version}")
        print("Installing Gradio 4.35.0...")
        subprocess.check_call([sys.executable, "-m", "pip", "install", "gradio==4.35.0", "--force-reinstall"])
        print("Gradio 4.35.0 installed successfully.")
    else:
        print("Gradio 4.35.0 is already installed.")

# Call the function to install Gradio 4.35.0 if needed
install_gradio_4_35_0()

import gradio as gr
import gradio_client
print(f"Gradio version: {gr.__version__}")
print(f"Gradio-client version: {gradio_client.__version__}")

class InferenceDemo(object):
    def __init__(
        self, args, model_path, tokenizer, model, image_processor, context_len
    ) -> None:
        disable_torch_init()

        self.tokenizer, self.model, self.image_processor, self.context_len = (
            tokenizer,
            model,
            image_processor,
            context_len,
        )

        if "llama-2" in model_name.lower():
            conv_mode = "llava_llama_2"
        elif "v1" in model_name.lower():
            conv_mode = "llava_v1"
        elif "mpt" in model_name.lower():
            conv_mode = "mpt"
        elif "qwen" in model_name.lower():
            conv_mode = "qwen_1_5"
        elif "pangea" in model_name.lower():
            conv_mode = "qwen_1_5"
        else:
            conv_mode = "llava_v0"

        if args.conv_mode is not None and conv_mode != args.conv_mode:
            print(
                "[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format(
                    conv_mode, args.conv_mode, args.conv_mode
                )
            )
        else:
            args.conv_mode = conv_mode
        self.conv_mode = conv_mode
        self.conversation = conv_templates[args.conv_mode].copy()
        self.num_frames = args.num_frames


def is_valid_video_filename(name):
    video_extensions = ["avi", "mp4", "mov", "mkv", "flv", "wmv", "mjpeg"]

    ext = name.split(".")[-1].lower()

    if ext in video_extensions:
        return True
    else:
        return False


def sample_frames(video_file, num_frames):
    video = cv2.VideoCapture(video_file)
    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
    interval = total_frames // num_frames
    frames = []
    for i in range(total_frames):
        ret, frame = video.read()
        pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        if not ret:
            continue
        if i % interval == 0:
            frames.append(pil_img)
    video.release()
    return frames


def load_image(image_file):
    if image_file.startswith("http") or image_file.startswith("https"):
        response = requests.get(image_file)
        if response.status_code == 200:
            image = Image.open(BytesIO(response.content)).convert("RGB")
        else:
            print("failed to load the image")
    else:
        print("Load image from local file")
        print(image_file)
        image = Image.open(image_file).convert("RGB")

    return image


def clear_history(history):

    our_chatbot.conversation = conv_templates[our_chatbot.conv_mode].copy()

    return None


def clear_response(history):
    for index_conv in range(1, len(history)):
        # loop until get a text response from our model.
        conv = history[-index_conv]
        if not (conv[0] is None):
            break
    question = history[-index_conv][0]
    history = history[:-index_conv]
    return history, question


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


def add_message(history, message):
    # history=[]
    global our_chatbot
    if len(history) == 0:
        our_chatbot = InferenceDemo(
            args, model_path, tokenizer, model, image_processor, context_len
        )

    for x in message["files"]:
        history.append(((x,), None))
    if message["text"] is not None:
        history.append((message["text"], None))
    return history, gr.MultimodalTextbox(value=None, interactive=False)


@spaces.GPU
def bot(history):
    text = history[-1][0]
    images_this_term = []
    text_this_term = ""
    # import pdb;pdb.set_trace()
    num_new_images = 0
    for i, message in enumerate(history[:-1]):
        if type(message[0]) is tuple:
            images_this_term.append(message[0][0])
            if is_valid_video_filename(message[0][0]):
                num_new_images += our_chatbot.num_frames
            else:
                num_new_images += 1
        else:
            num_new_images = 0

    # for message in history[-i-1:]:
    #     images_this_term.append(message[0][0])

    assert len(images_this_term) > 0, "must have an image"
    # image_files = (args.image_file).split(',')
    # image = [load_image(f) for f in images_this_term if f]
    image_list = []
    for f in images_this_term:
        if is_valid_video_filename(f):
            image_list += sample_frames(f, our_chatbot.num_frames)
        else:
            image_list.append(load_image(f))
    image_tensor = [
        our_chatbot.image_processor.preprocess(f, return_tensors="pt")["pixel_values"][
            0
        ]
        .half()
        .to(our_chatbot.model.device)
        for f in image_list
    ]

    image_tensor = torch.stack(image_tensor)
    image_token = DEFAULT_IMAGE_TOKEN * num_new_images
    # if our_chatbot.model.config.mm_use_im_start_end:
    #     inp = DEFAULT_IM_START_TOKEN + image_token + DEFAULT_IM_END_TOKEN + "\n" + inp
    # else:
    inp = text
    inp = image_token + "\n" + inp
    our_chatbot.conversation.append_message(our_chatbot.conversation.roles[0], inp)
    # image = None
    our_chatbot.conversation.append_message(our_chatbot.conversation.roles[1], None)
    prompt = our_chatbot.conversation.get_prompt()

    input_ids = (
        tokenizer_image_token(
            prompt, our_chatbot.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
        )
        .unsqueeze(0)
        .to(our_chatbot.model.device)
    )
    stop_str = (
        our_chatbot.conversation.sep
        if our_chatbot.conversation.sep_style != SeparatorStyle.TWO
        else our_chatbot.conversation.sep2
    )
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(
        keywords, our_chatbot.tokenizer, input_ids
    )
    streamer = TextStreamer(
        our_chatbot.tokenizer, skip_prompt=True, skip_special_tokens=True
    )
    print(our_chatbot.model.device)
    print(input_ids.device)
    print(image_tensor.device)
    # import pdb;pdb.set_trace()
    with torch.inference_mode():
        output_ids = our_chatbot.model.generate(
            input_ids,
            images=image_tensor,
            do_sample=True,
            temperature=0.2,
            max_new_tokens=1024,
            streamer=streamer,
            use_cache=False,
            stopping_criteria=[stopping_criteria],
        )

    outputs = our_chatbot.tokenizer.decode(output_ids[0]).strip()
    if outputs.endswith(stop_str):
        outputs = outputs[: -len(stop_str)]
    our_chatbot.conversation.messages[-1][-1] = outputs

    history[-1] = [text, outputs]

    return history


txt = gr.Textbox(
    scale=4,
    show_label=False,
    placeholder="Enter text and press enter.",
    container=False,
)

with gr.Blocks(
    css=".message-wrap.svelte-1lcyrx4>div.svelte-1lcyrx4  img {min-width: 40px}",
) as demo:

    # Informations
    title_markdown = """
        # LLaVA-NeXT Interleave
        [[Blog]](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/)  [[Code]](https://github.com/LLaVA-VL/LLaVA-NeXT) [[Model]](https://huggingface.co/lmms-lab/llava-next-interleave-7b)
        Note: The internleave checkpoint is updated (Date: Jul. 24, 2024), the wrong checkpiont is used before.
    """
    
    
    tos_markdown = """
    ### TODO!. Terms of use
    By using this service, users are required to agree to the following terms:
    The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
    Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
    For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
    """
    learn_more_markdown = """
    ### TODO!. License
    The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
    """
    models = [
        "LLaVA-Interleave-7B",
    ]
    cur_dir = os.path.dirname(os.path.abspath(__file__))
    # gr.Markdown(title_markdown)
    gr.HTML(html_header)
    
    with gr.Column():
        with gr.Row():
            chatbot = gr.Chatbot([], elem_id="chatbot", bubble_full_width=False)

        with gr.Row():
            upvote_btn = gr.Button(value="👍  Upvote", interactive=True)
            downvote_btn = gr.Button(value="👎  Downvote", interactive=True)
            flag_btn = gr.Button(value="⚠️  Flag", interactive=True)
            # stop_btn = gr.Button(value="⏹️  Stop Generation", interactive=True)
            regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=True)
            clear_btn = gr.Button(value="🗑️  Clear history", interactive=True)

        chat_input = gr.MultimodalTextbox(
            interactive=True,
            file_types=["image", "video"],
            placeholder="Enter message or upload file...",
            show_label=False,
        )

        print(cur_dir)
        gr.Examples(
            examples=[
                [
                    {
                        "files": [
                            f"{cur_dir}/examples/shub.jpg",
                        ],
                        "text": "what is fun about the image?",
                    }
                ],

            ],
            inputs=[chat_input],
            label="Compare images: "
        )

    chat_msg = chat_input.submit(
        add_message, [chatbot, chat_input], [chatbot, chat_input]
    )
    bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])

    # chatbot.like(print_like_dislike, None, None)
    clear_btn.click(
        fn=clear_history, inputs=[chatbot], outputs=[chatbot], api_name="clear_all"
    )


demo.queue()
    
if __name__ == "__main__":
    import argparse

    argparser = argparse.ArgumentParser()
    argparser.add_argument("--server_name", default="0.0.0.0", type=str)
    argparser.add_argument("--port", default="6123", type=str)
    argparser.add_argument(
        "--model_path", default="neulab/Pangea-7B", type=str
    )
    # argparser.add_argument("--model-path", type=str, default="facebook/opt-350m")
    argparser.add_argument("--model-base", type=str, default=None)
    argparser.add_argument("--num-gpus", type=int, default=1)
    argparser.add_argument("--conv-mode", type=str, default=None)
    argparser.add_argument("--temperature", type=float, default=0.2)
    argparser.add_argument("--max-new-tokens", type=int, default=512)
    argparser.add_argument("--num_frames", type=int, default=16)
    argparser.add_argument("--load-8bit", action="store_true")
    argparser.add_argument("--load-4bit", action="store_true")
    argparser.add_argument("--debug", action="store_true")

    args = argparser.parse_args()

    model_path = args.model_path
    filt_invalid = "cut"
    model_name = get_model_name_from_path(args.model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit)
    model=model.to(torch.device('cuda'))
    our_chatbot = None
    demo.launch()