app.py

import shutil
import subprocess

import torch
import gradio as gr
from fastapi import FastAPI
import os
from PIL import Image
import tempfile
from decord import VideoReader, cpu
from transformers import TextStreamer

from llava.constants import DEFAULT_X_TOKEN, X_TOKEN_INDEX
from llava.conversation import conv_templates, SeparatorStyle, Conversation
from llava.serve.gradio_utils import Chat, tos_markdown, learn_more_markdown, title_markdown, block_css


def save_image_to_local(image):
    filename = os.path.join('temp', next(tempfile._get_candidate_names()) + '.jpg')
    image = Image.open(image)
    image.save(filename)
    # print(filename)
    return filename


def save_video_to_local(video_path):
    filename = os.path.join('temp', next(tempfile._get_candidate_names()) + '.mp4')
    shutil.copyfile(video_path, filename)
    return filename


def generate(image1, video, textbox_in, first_run, state, state_, images_tensor):
    flag = 1
    if not textbox_in:
        if len(state_.messages) > 0:
            textbox_in = state_.messages[-1][1]
            state_.messages.pop(-1)
            flag = 0
        else:
            return "Please enter instruction"

    image1 = image1 if image1 else "none"
    video = video if video else "none"
    # assert not (os.path.exists(image1) and os.path.exists(video))

    if type(state) is not Conversation:
        state = conv_templates[conv_mode].copy()
        state_ = conv_templates[conv_mode].copy()
        images_tensor = [[], []]

    first_run = False if len(state.messages) > 0 else True

    text_en_in = textbox_in.replace("picture", "image")

    # images_tensor = [[], []]
    image_processor = handler.image_processor
    if os.path.exists(image1) and not os.path.exists(video):
        tensor = image_processor.preprocess(image1, return_tensors='pt')['pixel_values'][0]
        # print(tensor.shape)
        tensor = tensor.to(handler.model.device, dtype=dtype)
        images_tensor[0] = images_tensor[0] + [tensor]
        images_tensor[1] = images_tensor[1] + ['image']
        print(torch.cuda.memory_allocated())
        print(torch.cuda.max_memory_allocated())
    video_processor = handler.video_processor
    if not os.path.exists(image1) and os.path.exists(video):
        tensor = video_processor(video, return_tensors='pt')['pixel_values'][0]
        # print(tensor.shape)
        tensor = tensor.to(handler.model.device, dtype=dtype)
        images_tensor[0] = images_tensor[0] + [tensor]
        images_tensor[1] = images_tensor[1] + ['video']
        print(torch.cuda.memory_allocated())
        print(torch.cuda.max_memory_allocated())
    if os.path.exists(image1) and os.path.exists(video):
        tensor = video_processor(video, return_tensors='pt')['pixel_values'][0]
        # print(tensor.shape)
        tensor = tensor.to(handler.model.device, dtype=dtype)
        images_tensor[0] = images_tensor[0] + [tensor]
        images_tensor[1] = images_tensor[1] + ['video']
        
        
        tensor = image_processor.preprocess(image1, return_tensors='pt')['pixel_values'][0]
        # print(tensor.shape)
        tensor = tensor.to(handler.model.device, dtype=dtype)
        images_tensor[0] = images_tensor[0] + [tensor]
        images_tensor[1] = images_tensor[1] + ['image']
        print(torch.cuda.memory_allocated())
        print(torch.cuda.max_memory_allocated())
        


    if os.path.exists(image1) and not os.path.exists(video):
        text_en_in = DEFAULT_X_TOKEN['IMAGE'] + '\n' + text_en_in
    if not os.path.exists(image1) and os.path.exists(video):
        text_en_in = DEFAULT_X_TOKEN['VIDEO'] + '\n' + text_en_in
    if os.path.exists(image1) and os.path.exists(video):
        text_en_in = DEFAULT_X_TOKEN['VIDEO'] + '\n' + text_en_in + '\n' + DEFAULT_X_TOKEN['IMAGE']

    text_en_out, state_ = handler.generate(images_tensor, text_en_in, first_run=first_run, state=state_)
    state_.messages[-1] = (state_.roles[1], text_en_out)

    text_en_out = text_en_out.split('#')[0]
    textbox_out = text_en_out

    show_images = ""
    if os.path.exists(image1):
        filename = save_image_to_local(image1)
        show_images += f'<img src="./file={filename}" style="display: inline-block;width: 250px;max-height: 400px;">'
    if os.path.exists(video):
        filename = save_video_to_local(video)
        show_images += f'<video controls playsinline width="500" style="display: inline-block;"  src="./file={filename}"></video>'

    if flag:
        state.append_message(state.roles[0], textbox_in + "\n" + show_images)
    state.append_message(state.roles[1], textbox_out)
    torch.cuda.empty_cache()
    return (state, state_, state.to_gradio_chatbot(), False, gr.update(value=None, interactive=True), images_tensor, gr.update(value=image1 if os.path.exists(image1) else None, interactive=True), gr.update(value=video if os.path.exists(video) else None, interactive=True))

def regenerate(state, state_):
    state.messages.pop(-1)
    state_.messages.pop(-1)
    if len(state.messages) > 0:
        return state, state_, state.to_gradio_chatbot(), False
    return (state, state_, state.to_gradio_chatbot(), True)


def clear_history(state, state_):
    state = conv_templates[conv_mode].copy()
    state_ = conv_templates[conv_mode].copy()
    return (gr.update(value=None, interactive=True),
        gr.update(value=None, interactive=True),\
        gr.update(value=None, interactive=True),\
        True, state, state_, state.to_gradio_chatbot(), [[], []])


conv_mode = "llava_v1"
model_path = 'LanguageBind/Video-LLaVA-7B'
device = 'cpu'
load_8bit = False
load_4bit = True
dtype = torch.get_default_dtype()
handler = Chat(model_path, conv_mode=conv_mode, load_8bit=load_8bit, load_4bit=load_8bit, device=device)
# handler.model.to(dtype=dtype)
if not os.path.exists("temp"):
    os.makedirs("temp")

'''
conv_mode = "llava_v1"
model_path = 'LanguageBind/Video-LLaVA-7B'
device = 'cuda'
load_8bit = False
load_4bit = True
dtype = torch.float16
handler = Chat(model_path, conv_mode=conv_mode, load_8bit=load_8bit, load_4bit=load_8bit, device=device)
# handler.model.to(dtype=dtype)
if not os.path.exists("temp"):
    os.makedirs("temp")
'''
print(torch.cuda.memory_allocated())
print(torch.cuda.max_memory_allocated())

app = FastAPI()

textbox = gr.Textbox(
        show_label=False, placeholder="Enter text and press ENTER", container=False
    )
with gr.Blocks(title='Video-LLaVA🚀', theme=gr.themes.Default(), css=block_css) as demo:
    gr.Markdown(title_markdown)
    state = gr.State()
    state_ = gr.State()
    first_run = gr.State()
    images_tensor = gr.State()

    with gr.Row():
        with gr.Column(scale=3):
            image1 = gr.Image(label="Input Image", type="filepath")
            video = gr.Video(label="Input Video")

            cur_dir = os.path.dirname(os.path.abspath(__file__))
            gr.Examples(
                examples=[
                    [
                        f"{cur_dir}/examples/extreme_ironing.jpg",
                        "What is unusual about this image?",
                    ],
                    [
                        f"{cur_dir}/examples/waterview.jpg",
                        "What are the things I should be cautious about when I visit here?",
                    ],
                    [
                        f"{cur_dir}/examples/desert.jpg",
                        "If there are factual errors in the questions, point it out; if not, proceed answering the question. What’s happening in the desert?",
                    ],
                ],
                inputs=[image1, textbox],
            )

        with gr.Column(scale=7):
            chatbot = gr.Chatbot(label="Video-LLaVA", bubble_full_width=True).style(height=750)
            with gr.Row():
                with gr.Column(scale=8):
                    textbox.render()
                with gr.Column(scale=1, min_width=50):
                    submit_btn = gr.Button(
                        value="Send", variant="primary", interactive=True
                    )
            with gr.Row(elem_id="buttons") as button_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=False)
                regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=True)
                clear_btn = gr.Button(value="🗑️  Clear history", interactive=True)

    with gr.Row():
        gr.Examples(
            examples=[
                [
                    f"{cur_dir}/examples/sample_img_8.png",
                    f"{cur_dir}/examples/sample_demo_8.mp4",
                    "Are the image and the video depicting the same place?",
                ],
                [
                    f"{cur_dir}/examples/sample_img_22.png",
                    f"{cur_dir}/examples/sample_demo_22.mp4",
                    "Are the instruments in the pictures used in the video?",
                ],
                [
                    f"{cur_dir}/examples/sample_img_13.png",
                    f"{cur_dir}/examples/sample_demo_13.mp4",
                    "Does the flag in the image appear in the video?",
                ],
            ],
            inputs=[image1, video, textbox],
        )
        gr.Examples(
            examples=[
                [
                    f"{cur_dir}/examples/sample_demo_1.mp4",
                    "Why is this video funny?",
                ],
                '''[
                    f"{cur_dir}/examples/sample_demo_7.mp4",
                    "Create a short fairy tale with a moral lesson inspired by the video.",
                ],
                [
                    f"{cur_dir}/examples/sample_demo_8.mp4",
                    "Where is this video taken from? What place/landmark is shown in the video?",
                ],
                [
                    f"{cur_dir}/examples/sample_demo_12.mp4",
                    "What does the woman use to split the logs and how does she do it?",
                ],
                [
                    f"{cur_dir}/examples/sample_demo_18.mp4",
                    "Describe the video in detail.",
                ],
                [
                    f"{cur_dir}/examples/sample_demo_22.mp4",
                    "Describe the activity in the video.",
                ],'''
            ],
            inputs=[video, textbox],
        )
    gr.Markdown(tos_markdown)
    gr.Markdown(learn_more_markdown)

    submit_btn.click(generate, [image1, video, textbox, first_run, state, state_, images_tensor],
                     [state, state_, chatbot, first_run, textbox, images_tensor, image1, video])

    regenerate_btn.click(regenerate, [state, state_], [state, state_, chatbot, first_run]).then(
        generate, [image1, video, textbox, first_run, state, state_, images_tensor], [state, state_, chatbot, first_run, textbox, images_tensor, image1, video])

    clear_btn.click(clear_history, [state, state_],
                    [image1, video, textbox, first_run, state, state_, chatbot, images_tensor])

# app = gr.mount_gradio_app(app, demo, path="/")
demo.launch()


# uvicorn llava.serve.gradio_web_server:app