# Copyright (2024) Bytedance Ltd. and/or its affiliates
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# copy and modify from: https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/demo/demo.py
import spaces
from copy import deepcopy
import gradio as gr
from gradio.themes.utils import colors, fonts, sizes
from tools.conversation import Chat, conv_templates
from tools.utils import load_model_and_processor, file_to_base64
from dataset.processor import Processor
import os
import torch
# huggingface-cli login
model_path = os.getenv("MODEL_PATH", "omni-research/Tarsier2-7b")
max_n_frames = int(os.getenv("MAX_N_FRAMES", 16))
debug = False
device = 'cuda' if not debug else 'cpu'
# ========================================
# Model Initialization
# ========================================
def init_model():
print("Start Initialization...")
# if torch.cuda.is_available():
if not debug:
model, processor = load_model_and_processor(model_path, max_n_frames)
else:
print(f"No Valid GPU! Lauch in debug mode!")
processor = Processor(model_path, max_n_frames)
model = None
chat = Chat(model, processor, device, debug)
print('Initialization Finished')
return chat
# ========================================
# Gradio Setting
# ========================================
def gradio_reset(chat_state, img_file, img_list):
if chat_state is not None:
chat_state.messages = []
img_file = None
if img_list is not None:
img_list = []
return None, gr.update(value=None, interactive=True), 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_file, img_list
def upload_img(gr_img, gr_video, gr_gif, chat_state, num_frames):
print("video, image or gif:", gr_video, gr_img, gr_gif)
conv_type = ''
if 'tarsier2-7b' in model_path.lower():
conv_type = 'tarsier2-7b'
elif '7b' in model_path.lower():
conv_type = 'tarsier-7b'
elif '13b' in model_path.lower():
conv_type = 'tarsier-13b'
elif '34b' in model_path.lower():
conv_type = 'tarsier-34b'
else:
raise ValueError(f"Unknow model: {model_path}")
chat_state = deepcopy(conv_templates[conv_type])
img_list = []
if gr_img is None and gr_video is None and gr_gif is None:
return None, None, None, gr.update(interactive=True), gr.update(interactive=True, placeholder='Please upload video/image first!'), chat_state, None, None
if gr_video or gr_img or gr_gif:
for img_file in [gr_video, gr_img, gr_gif]:
if img_file is not None:
break
return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, img_file, img_list
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_state = chat.ask(user_message, chat_state)
chatbot = chatbot + [[user_message, None]]
return '', chatbot, chat_state
@spaces.GPU(duration=120)
def gradio_answer(chatbot, chat_state, img_file, img_list, top_p, temperature, n_frames=None):
llm_message, chat_state, img_list = chat.answer(conv=chat_state, visual_data_file=img_file, images=img_list, n_frames=n_frames, max_new_tokens=256, num_beams=1, temperature=temperature, top_p=top_p)
chatbot[-1][1] = llm_message
print(chat_state)
print(f"Answer: {llm_message}")
return chatbot, chat_state, img_list
class OpenGVLab(gr.themes.base.Base):
def __init__(
self,
*,
primary_hue=colors.blue,
secondary_hue=colors.sky,
neutral_hue=colors.gray,
spacing_size=sizes.spacing_md,
radius_size=sizes.radius_sm,
text_size=sizes.text_md,
font=(
fonts.GoogleFont("Noto Sans"),
"ui-sans-serif",
"sans-serif",
),
font_mono=(
fonts.GoogleFont("IBM Plex Mono"),
"ui-monospace",
"monospace",
),
):
super().__init__(
primary_hue=primary_hue,
secondary_hue=secondary_hue,
neutral_hue=neutral_hue,
spacing_size=spacing_size,
radius_size=radius_size,
text_size=text_size,
font=font,
font_mono=font_mono,
)
super().set(
body_background_fill="*neutral_50",
)
gvlabtheme = OpenGVLab(primary_hue=colors.blue,
secondary_hue=colors.sky,
neutral_hue=colors.gray,
spacing_size=sizes.spacing_md,
radius_size=sizes.radius_sm,
text_size=sizes.text_md,
)
logo_b64 = file_to_base64("assets/figures/tarsier_logo.jpg")
title = f"""
"""
description ="""
"""
with gr.Blocks(title="Tarsier",theme=gvlabtheme,css="#chatbot {overflow:auto; height:500px;} #InputVideo {overflow:visible; height:320px;} footer {visibility: none}") as demo:
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column(scale=0.5, visible=True) as video_upload:
with gr.Column(elem_id="image", scale=0.5) as img_part:
with gr.Tab("Video", elem_id='video_tab'):
up_video = gr.Video(interactive=True, include_audio=True, elem_id="video_upload", height=360)
with gr.Tab("Image", elem_id='image_tab'):
up_image = gr.Image(type="filepath", interactive=True, elem_id="image_upload", height=360)
with gr.Tab("GIF", elem_id='gif_tab'):
up_gif = gr.File(type="filepath", file_count="single", file_types=[".gif"], interactive=True, elem_id="gif_upload", height=360)
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=0.0,
maximum=1.0,
value=0.0,
step=0.1,
interactive=True,
label="Temperature",
)
top_p = gr.Slider(
minimum=0.1,
maximum=1.0,
value=1.0,
step=0.1,
interactive=True,
label="Top_p",
)
num_frames = gr.Slider(
minimum=4,
maximum=16,
value=16,
step=2,
interactive=True,
label="#Frames",
)
with gr.Column(visible=True) as input_raws:
chat_state = gr.State()
img_list = gr.State()
img_file = gr.State()
chatbot = gr.Chatbot(elem_id="chatbot",label='VideoChat')
with gr.Row():
with gr.Column(scale=0.7):
text_input = gr.Textbox(show_label=False, placeholder='Please upload your video first', interactive=False, container=False)
with gr.Column(scale=0.15, min_width=0):
run = gr.Button("💭Send")
with gr.Column(scale=0.15, min_width=0):
clear = gr.Button("🔄Clear️")
gr.Examples(examples=[
[f"examples/test1.mp4", "Describe the video in detail."],
[f"examples/test2.mp4", "Are they having a pleasant conversation?"],
], inputs=[up_video, text_input])
chat = init_model()
upload_button.click(upload_img, [up_image, up_video, up_gif, chat_state, num_frames], [up_image, up_video, up_gif, text_input, upload_button, chat_state, img_file, img_list])
text_input.submit(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
gradio_answer, [chatbot, chat_state, img_file, img_list, top_p, temperature, num_frames], [chatbot, chat_state, img_list]
)
run.click(gradio_ask, [text_input, chatbot, chat_state], [text_input, chatbot, chat_state]).then(
gradio_answer, [chatbot, chat_state, img_file, img_list, top_p, temperature, num_frames], [chatbot, chat_state, img_list]
)
run.click(lambda: "", None, text_input)
clear.click(gradio_reset, [chat_state, img_file, img_list], [chatbot, up_image, up_video, up_gif, text_input, upload_button, chat_state, img_file, img_list], queue=False)
demo.launch()
# demo.launch(server_name="0.0.0.0", server_port=11451)