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import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import gradio as gr
from bliva.common.config import Config
from bliva.common.dist_utils import get_rank
from bliva.common.registry import registry
from bliva.conversation.conversation import Chat, CONV_VISION, CONV_DIRECT
# imports modules for registration
from bliva.models import *
from bliva.processors import *
from bliva.models import load_model_and_preprocess
from evaluate import disable_torch_init
def parse_args():
parser = argparse.ArgumentParser(description="Demo")
parser.add_argument("--model_name",default='bliva_vicuna', type=str, help='model name')
parser.add_argument("--gpu_id", type=int, default=0, help="specify the gpu to load the model.")
args = parser.parse_args()
return args
# ========================================
# Model Initialization
# ========================================
print('Initializing Chat')
args = parse_args()
if torch.cuda.is_available():
device='cuda:{}'.format(args.gpu_id)
else:
device=torch.device('cpu')
disable_torch_init()
if args.model_name == "blip2_vicuna_instruct":
model, vis_processors, _ = load_model_and_preprocess(name=args.model_name, model_type="vicuna7b", is_eval=True, device=device)
elif args.model_name == "bliva_vicuna":
model, vis_processors, _ = load_model_and_preprocess(name=args.model_name, model_type="vicuna7b", is_eval=True, device=device)
elif args.model_name == "bliva_flant5":
model, vis_processors, _ = load_model_and_preprocess(name=args.model_name, model_type="flant5xxl", is_eval=True, device=device)
else:
print("Model not found")
vis_processor = vis_processors["eval"]
# vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train
# vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
chat = Chat(model, vis_processor, device=device)
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(placeholder='Please upload your image first', interactive=False),gr.update(value="Upload & Start Chat", interactive=True), chat_state, img_list
def upload_img(gr_img, text_input, chat_state):
if gr_img is None:
return None, None, gr.update(interactive=True), chat_state, None
chat_state = CONV_DIRECT.copy() #CONV_VISION.copy()
img_list = []
llm_message = chat.upload_img(gr_img, chat_state, img_list)
return gr.update(interactive=False), gr.update(interactive=True, placeholder='Type and press Enter'), gr.update(value="Start Chatting", interactive=False), chat_state, 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.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[0]
return chatbot, chat_state, img_list
title = """<h1 align="center">Demo of BLIVA</h1>"""
description = """<h3>This is the demo of BLIVA. Upload your images and start chatting!. <br> To use
example questions, click example image, hit upload, and press enter in the chatbox.</h3>"""
article = """<p><a href='https://gordonhu608.github.io/bliva/'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p><p><a href='https://github.com/mlpc-ucsd/BLIVA'><img src='https://img.shields.io/badge/Github-Code-blue'></a></p><p><a href='https://arxiv.org/abs/2308.09936'><img src='https://img.shields.io/badge/Paper-ArXiv-red'></a></p>
"""
#TODO show examples below
with gr.Blocks() as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown(article)
with gr.Row():
with gr.Column(scale=0.5):
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=5,
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='BLIVA')
text_input = gr.Textbox(label='User', placeholder='Please upload your image first', interactive=False)
gr.Examples(examples=[
[f"images/example.jpg", "Describe this image in detail."],
[f"images/img3.jpg", "What is this image about?"],
[f"images/img4.jpg", "What is the title of this movie?"],
], inputs=[image, text_input])
upload_button.click(upload_img, [image, text_input, chat_state], [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, image, text_input, upload_button, chat_state, img_list], queue=False)
demo.launch(enable_queue=True) |