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main_demo_7B.py
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main_demo_7B.py
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import torch
import gradio as gr
from fastapi import FastAPI
from ChatUniVi.conversation import conv_templates, Conversation
from ChatUniVi.demo import Chat
from ChatUniVi.constants import *
import os
from PIL import Image
import tempfile
import imageio
from decord import VideoReader, cpu
import shutil
app = FastAPI()
model_path = "Chat-UniVi/Chat-UniVi" # model_path = [model path]
def save_image_to_local(image):
filename = os.path.join('temp', next(tempfile._get_candidate_names()) + '.jpg')
image = Image.open(image)
image.save(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, image2, 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"
image2 = image2 if image2 else "none"
video = video if video else "none"
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")
image_processor = handler.image_processor
if os.path.exists(image1):
images = [Image.open(image1)]
images_tensor.append(image_processor(images, return_tensors='pt')['pixel_values'][0].to(handler.model.device, dtype=torch.float16))
if os.path.exists(image2):
images = [Image.open(image2)]
images_tensor.append(image_processor(images, return_tensors='pt')['pixel_values'][0].to(handler.model.device, dtype=torch.float16))
if os.path.exists(video):
video_tensor = handler._get_rawvideo_dec(video, image_processor, max_frames=MAX_IMAGE_LENGTH)
for img in video_tensor:
images_tensor.append(image_processor(img, return_tensors='pt')['pixel_values'][0].to(handler.model.device, dtype=torch.float16))
if os.path.exists(video):
text_en_in = DEFAULT_IMAGE_TOKEN * len(video_tensor) + '\n' + text_en_in
if os.path.exists(image2):
text_en_in = DEFAULT_IMAGE_TOKEN + '\n' + text_en_in
if os.path.exists(image1):
text_en_in = DEFAULT_IMAGE_TOKEN + '\n' + text_en_in
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(image2):
filename = save_image_to_local(image2)
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=None, interactive=True), gr.update(value=None, interactive=True), gr.update(value=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),\
gr.update(value=None, interactive=True),\
True, state, state_, state.to_gradio_chatbot(), [])
conv_mode = "simple"
handler = Chat(model_path, conv_mode=conv_mode)
if not os.path.exists("temp"):
os.makedirs("temp")
with gr.Blocks(gr.themes.Soft()) as demo:
demo.title = 'Demo'
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 Image1", type="filepath")
image2 = gr.Image(label="Input Image2", type="filepath")
video = gr.Video(label="Input Video")
with gr.Column(scale=6):
chatbot = gr.Chatbot(label="Chat-UniVi", bubble_full_width=True).style(height=1200)
with gr.Row():
with gr.Column(scale=8):
textbox = gr.Textbox(label="Input Text")
with gr.Column(scale=1, min_width=60, label="Input Text"):
submit_btn = gr.Button(value="Submit", visible=True)
with gr.Row(visible=True) 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)
regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=True)
clear_btn = gr.Button(value="🗑️ Clear history", interactive=True)
submit_btn.click(generate, [image1, image2, video, textbox, first_run, state, state_, images_tensor], [state, state_, chatbot, first_run, textbox, images_tensor, image1, image2, video])
regenerate_btn.click(regenerate, [state, state_], [state, state_, chatbot, first_run]).then(
generate, [image1, image2, video, textbox, first_run, state, state_, images_tensor], [state, state_, chatbot, first_run, textbox, images_tensor, image1, image2, video])
clear_btn.click(clear_history, [state, state_],
[image1, image2, video, textbox, first_run, state, state_, chatbot, images_tensor])
app = gr.mount_gradio_app(app, demo, path="/")