"""
Adapted from: https://github.com/Vision-CAIR/MiniGPT-4/blob/main/demo.py
"""
import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from video_llama.common.config import Config
from video_llama.common.dist_utils import get_rank
from video_llama.common.registry import registry
from video_llama.conversation.conversation_video import Chat, Conversation, default_conversation,SeparatorStyle
import decord
decord.bridge.set_bridge('torch')
#%%
# imports modules for registration
from video_llama.datasets.builders import *
from video_llama.models import *
from video_llama.processors import *
from video_llama.runners import *
from video_llama.tasks import *
#%%
def parse_args():
parser = argparse.ArgumentParser(description="Demo")
parser.add_argument("--cfg-path", default='eval_configs/video_llama_eval.yaml', help="path to configuration file.")
parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
parser.add_argument(
"--options",
nargs="+",
help="override some settings in the used config, the key-value pair "
"in xxx=yyy format will be merged into config file (deprecate), "
"change to --cfg-options instead.",
)
args = parser.parse_args()
return args
def setup_seeds(config):
seed = config.run_cfg.seed + get_rank()
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
cudnn.benchmark = False
cudnn.deterministic = True
# ========================================
# Model Initialization
# ========================================
print('Initializing Chat')
args = parse_args()
cfg = Config(args)
model_config = cfg.model_cfg
model_config.device_8bit = args.gpu_id
model_cls = registry.get_model_class(model_config.arch)
model = model_cls.from_config(model_config).to('cuda:{}'.format(args.gpu_id))
vis_processor_cfg = cfg.datasets_cfg.webvid.vis_processor.train
vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
chat = Chat(model, vis_processor, device='cuda:{}'.format(args.gpu_id))
print('Initialization Finished')
# ========================================
# Gradio Setting
# ========================================
def gradio_reset(chat_state, img_list):
if chat_state is not None:
chat_state.messages = []
if img_list is not None:
img_list = []
return None, gr.update(value=None, interactive=True), gr.update(value=None, interactive=True), gr.update(placeholder='Please upload your video first', interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list
def upload_imgorvideo(gr_video, gr_img, text_input, chat_state):
if gr_img is None and gr_video is None:
return None, None, None, gr.update(interactive=True), chat_state, None
elif gr_img is not None and gr_video is None:
print(gr_img)
chat_state = Conversation(
system= "You are able to understand the visual content that the user provides."
"Follow the instructions carefully and explain your answers in detail.",
roles=("Human", "Assistant"),
messages=[],
offset=0,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
img_list = []
llm_message = chat.upload_img(gr_img, chat_state, img_list)
return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list
elif gr_video is not None and gr_img is None:
print(gr_video)
chat_state = default_conversation.copy()
chat_state = Conversation(
system= "You are able to understand the visual content that the user provides."
"Follow the instructions carefully and explain your answers in detail.",
roles=("Human", "Assistant"),
messages=[],
offset=0,
sep_style=SeparatorStyle.SINGLE,
sep="###",
)
img_list = []
llm_message = chat.upload_video(gr_video, chat_state, img_list)
return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_list
else:
# img_list = []
return gr.update(interactive=False), gr.update(interactive=False, placeholder='Currently, only one input is supported'), gr.update(value="Currently, only one input is supported", interactive=False), chat_state, None
def gradio_ask(user_message, chatbot, chat_state):
if len(user_message) == 0:
return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
chat.ask(user_message, chat_state)
chatbot = chatbot + [[user_message, None]]
return '', chatbot, chat_state
def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
llm_message = chat.answer(conv=chat_state,
img_list=img_list,
num_beams=num_beams,
temperature=temperature,
max_new_tokens=300,
max_length=2000)[0]
chatbot[-1][1] = llm_message
print(chat_state.get_prompt())
print(chat_state)
return chatbot, chat_state, img_list
title = """
# Video-LLaMA: An Instruction-Finetuned Visual Language Model for Video Understanding
This is the demo for the Video-LLaMA project, which is working on empowering large language models with video understanding capability. Upload your images/videos and start chatting!!!
Continuously upgrading, stay tuned for more updates!
"""
#TODO show examples below
with gr.Blocks() as demo:
gr.Markdown(title)
with gr.Row():
with gr.Column(scale=0.5):
video = gr.Video()
image = gr.Image(type="pil")
upload_button = gr.Button(value="Upload & Start Chat", interactive=True, variant="primary")
clear = gr.Button("Restart")
num_beams = gr.Slider(
minimum=1,
maximum=10,
value=1,
step=1,
interactive=True,
label="beam search numbers)",
)
temperature = gr.Slider(
minimum=0.1,
maximum=2.0,
value=1.0,
step=0.1,
interactive=True,
label="Temperature",
)
with gr.Column():
chat_state = gr.State()
img_list = gr.State()
chatbot = gr.Chatbot(label='Video-LLaMA')
text_input = gr.Textbox(label='User', placeholder='Please upload your image/video first', interactive=False)
upload_button.click(upload_imgorvideo, [video, image, text_input, chat_state], [video, image, text_input, upload_button, chat_state, img_list])
text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
gradio_answer, [chatbot, chat_state, img_list, num_beams, temperature], [chatbot, chat_state, img_list]
)
clear.click(gradio_reset, [chat_state, img_list], [chatbot, video, image, text_input, upload_button, chat_state, img_list], queue=False)
demo.launch(share=False, enable_queue=False)
# %%
