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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']
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']
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']
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)
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 = '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")
app = FastAPI()
textbox = gr.Textbox(
show_label=False, placeholder="Enter text and press ENTER", container=False
)
with gr.Blocks(title='Video-LLaVAπ', 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/glove.jpg",
"What happens when the glove drops?",
],
[
f"{cur_dir}/examples/desert.jpg",
"If there are factual errors in the questions, point it out; if not, proceed answering the question. Whatβs happening in the desert?",
],
],
inputs=[image1, textbox],
)
with gr.Column(scale=7):
chatbot = gr.Chatbot(label="Video-LLaVA", bubble_full_width=True).style(height=850)
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="π Upvote", interactive=True)
downvote_btn = gr.Button(value="π Downvote", interactive=True)
flag_btn = gr.Button(value="β οΈ Flag", interactive=True)
# stop_btn = gr.Button(value="βΉοΈ Stop Generation", interactive=False)
regenerate_btn = gr.Button(value="π Regenerate", interactive=True)
clear_btn = gr.Button(value="ποΈ Clear history", interactive=True)
with gr.Row():
gr.Examples(
examples=[
[
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?",
],
[
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?",
],
],
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_3.mp4",
"Can you identify any safety hazards in this video?"
],
[
f"{cur_dir}/examples/sample_demo_9.mp4",
"Describe the video.",
],
[
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
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