app.py

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

# import time
import cv2

import datetime
# 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

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

import hashlib
import PIL
import base64
import json

import datetime
import gradio as gr
import gradio_client
import subprocess
import sys

external_log_dir = "./logs"
LOGDIR = external_log_dir

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__}")

def get_conv_log_filename():
    t = datetime.datetime.now()
    name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-user_conv.json")
    return name

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 is_valid_image_filename(name):
    image_extensions = ["jpg", "jpeg", "png", "bmp", "gif", "tiff", "webp", "heic", "heif", "jfif", "svg", "eps", "raw"] 

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

    if ext in image_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]):
                # 不接受视频
                raise ValueError("Video is not supported")
                num_new_images += our_chatbot.num_frames
            elif is_valid_image_filename(message[0][0]):
                print("#### Load image from local file",message[0][0])
                num_new_images += 1
            else:
                raise ValueError("Invalid image file")
        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]

    all_image_hash = []
    all_image_path = []
    for image_path in images_this_term:
        with open(image_path, "rb") as image_file:
            image_data = image_file.read()
            image_hash = hashlib.md5(image_data).hexdigest()
            all_image_hash.append(image_hash)
            image = PIL.Image.open(image_path).convert("RGB")
            t = datetime.datetime.now()
            filename = os.path.join(
                LOGDIR,
                "serve_images",
                f"{t.year}-{t.month:02d}-{t.day:02d}",
                f"{image_hash}.jpg",
            )
            all_image_path.append(filename)
            if not os.path.isfile(filename):
                os.makedirs(os.path.dirname(filename), exist_ok=True)
                print("image save to",filename)
                image.save(filename)
    
    image_list = []
    for f in images_this_term:
        if is_valid_video_filename(f):
            image_list += sample_frames(f, our_chatbot.num_frames)
        elif is_valid_image_filename(f):
            image_list.append(load_image(f))
        else:
            raise ValueError("Invalid image file")
    
    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)
    # )
    input_ids = tokenizer_image_token(
            prompt, our_chatbot.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
        ).unsqueeze(0).to(our_chatbot.model.device)
    # print("### input_id",input_ids)
    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
    # )
    streamer = TextIteratorStreamer(
        our_chatbot.tokenizer, skip_prompt=True, skip_special_tokens=True
    )
    print(our_chatbot.model.device)
    print(input_ids.device)
    print(image_tensor.device)
    
    # with torch.inference_mode():
    #     output_ids = our_chatbot.model.generate(
    #         input_ids,
    #         images=image_tensor,
    #         do_sample=True,
    #         temperature=0.7,
    #         top_p=1.0,
    #         max_new_tokens=4096,
    #         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
    generate_kwargs = dict(
        inputs=input_ids,
        streamer=streamer,
        images=image_tensor,
        max_new_tokens=1024,
        do_sample=True,
        temperature=0.2,
        num_beams=1,
        use_cache=False,
        stopping_criteria=[stopping_criteria],
    )
    
    t = Thread(target=our_chatbot.model.generate, kwargs=generate_kwargs)
    t.start()
    
    outputs = []
    for text in streamer:
        outputs.append(text)
        our_chatbot.conversation.messages[-1][-1] = "".join(outputs)
        history[-1] = [text, "".join(outputs)]
        yield history
        
    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "type": "chat",
            "model": "Pangea-7b",
            "state": history,
            "images": all_image_hash,
            "images_path": all_image_path
        }
        print("#### conv log",data)
        fout.write(json.dumps(data) + "\n")
    
    

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:

    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="Pangea", bubble_full_width=False, height=750)

        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"],
            placeholder="Enter message or upload file...",
            show_label=False,
            submit_btn="🚀"
        )

        print(cur_dir)
        gr.Examples(
                examples_per_page=20,
                examples=[
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/user_example_07.jpg",
                            ],
                            "text": "那要我问问你，你这个是什么🐱？",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/user_example_05.jpg",
                            ],
                            "text": "この猫の目の大きさは、どのような理由で他の猫と比べて特に大きく見えますか？",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/172197131626056_P7966202.png",
                            ],
                            "text": "Why this image funny?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/norway.jpg",
                            ],
                            "text": "Analysieren, in welchem Land diese Szene höchstwahrscheinlich gedreht wurde.",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/totoro.jpg",
                            ],
                            "text": "¿En qué anime aparece esta escena? ¿Puedes presentarlo?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/africa.jpg",
                            ],
                            "text": "इस तस्वीर में हर एक दृश्य तत्व का क्या प्रतिनिधित्व करता है?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/hot_ballon.jpg",
                            ],
                            "text": "ฉากบอลลูนลมร้อนในภาพนี้อาจอยู่ที่ไหน? สถานที่นี้มีความพิเศษอย่างไร?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/bar.jpg",
                            ],
                            "text": "Você pode me dar ideias de design baseadas no tema de coquetéis deste letreiro?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/pink_lake.jpg",
                            ],
                            "text": "Обясни защо езерото на този остров е в този цвят.",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/hanzi.jpg",
                            ],
                            "text": "Can you describe in Hebrew the evolution process of these four Chinese characters from pictographs to modern characters?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/ballon.jpg",
                            ],
                            "text": "இந்த காட்சியை விவரிக்கவும், மேலும் இந்த படத்தின் அடிப்படையில் துருக்கியில் இந்த காட்சியுடன் தொடர்பான சில பிரபலமான நிகழ்வுகள் என்ன?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/pie.jpg",
                            ],
                            "text": "Décrivez ce graphique. Quelles informations pouvons-nous en tirer?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/camera.jpg",
                            ],
                            "text": "Apa arti dari dua angka di sebelah kiri yang ditampilkan di layar kamera?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/dog.jpg",
                            ],
                            "text": "이 강아지의 표정을 보고 어떤 기분이나 감정을 느끼고 있는지 설명해 주시겠어요?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/book.jpg",
                            ],
                            "text": "What language is the text in, and what does the title mean in English?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/food.jpg",
                            ],
                            "text": "Unaweza kunipa kichocheo cha kutengeneza hii pancake?",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/line chart.jpg",
                            ],
                            "text": "Hãy trình bày những xu hướng mà bạn quan sát được từ biểu đồ và hiện tượng xã hội tiềm ẩn từ đó.",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/south africa.jpg",
                            ],
                            "text": "Waar is hierdie plek? Help my om ’n reisroete vir hierdie land te beplan.",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/girl.jpg",
                            ],
                            "text": "لماذا هذه الصورة مضحكة؟",
                        },
                    ],
                    [
                        {
                            "files": [
                                f"{cur_dir}/examples/eagles.jpg",
                            ],
                            "text": "Какой креатив должен быть в этом логотипе?",
                        },
                    ],
                ],
                inputs=[chat_input],
                label="Image",
            )

    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=0.7)
    argparser.add_argument("--max-new-tokens", type=int, default=4096)
    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()