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demo.py
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demo.py
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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 xraygpt.common.config import Config
from xraygpt.common.dist_utils import get_rank
from xraygpt.common.registry import registry
from xraygpt.conversation.conversation import Chat, CONV_VISION
# imports modules for registration
from xraygpt.datasets.builders import *
from xraygpt.models import *
from xraygpt.processors import *
from xraygpt.runners import *
from xraygpt.tasks import *
def parse_args():
parser = argparse.ArgumentParser(description="Demo")
parser.add_argument("--cfg-path", required=True, 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.openi.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(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_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
return chatbot, chat_state, img_list
title = """<h1 align="center">Demo of XrayGPT</h1>"""
description = """<h3>Upload your X-Ray images and start asking queries!</h3>"""
disclaimer = """
<h1 >Terms of Use:</h1>
<ul>
<li>You acknowledge that the XrayGPT service is designed for research purposes with the ultimate aim of assisting medical professionals in their diagnostic process. It is important to note that the Service does not replace professional medical advice or diagnosis.</li>
<li>XrayGPT utilizes advanced artificial intelligence algorithms (LLVM's) to carefully analyze and summarize X-ray images for medical diagnostic purposes. The results provided by the Service are derived from the thorough analysis conducted by the AI system, based on the X-ray images provided by the user.</li>
<li>We strive to provide accurate and helpful results through XrayGPT. However, it is important to understand that we do not make any explicit warranties or representations regarding the effectiveness, reliability, or completeness of the results provided. Our aim is to continually improve and refine the Service to provide the best possible assistance to medical professionals.</li>
</ul>
<hr>
<h3 align="center">Designed and Developed by IVAL Lab, MBZUAI</h3>
"""
def set_example_xray(example: list) -> dict:
return gr.Image.update(value=example[0])
def set_example_text_input(example_text: str) -> dict:
return gr.Textbox.update(value=example_text[0])
#TODO show examples below
with gr.Blocks() as demo:
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column(scale=0.5):
image = gr.Image(type="pil")
upload_button = gr.Button(value="Upload and Ask Queries", interactive=True, variant="primary")
clear = gr.Button("Reset")
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='XrayGPT')
text_input = gr.Textbox(label='User', placeholder='Please upload your X-Ray image.', interactive=False)
with gr.Row():
example_xrays = gr.Dataset(components=[image], label="X-Ray Examples",
samples=[
[os.path.join(os.path.dirname(__file__), "images/example_test_images/img1.png")],
[os.path.join(os.path.dirname(__file__), "images/example_test_images/img2.png")],
[os.path.join(os.path.dirname(__file__), "images/example_test_images/img3.png")],
[os.path.join(os.path.dirname(__file__), "images/example_test_images/img4.png")],
[os.path.join(os.path.dirname(__file__), "images/example_test_images/img5.png")],
[os.path.join(os.path.dirname(__file__), "images/example_test_images/img6.png")],
[os.path.join(os.path.dirname(__file__), "images/example_test_images/img7.png")],
[os.path.join(os.path.dirname(__file__), "images/example_test_images/img8.png")],
[os.path.join(os.path.dirname(__file__), "images/example_test_images/img9.png")],
])
with gr.Row():
example_texts = gr.Dataset(components=[gr.Textbox(visible=False)],
label="Prompt Examples",
samples=[
["Describe the given chest x-ray image in detail."],
["Take a look at this chest x-ray and describe the findings and impression."],
["Could you provide a detailed description of the given x-ray image?"],
["Describe the given chest x-ray image as detailed as possible."],
["What are the key findings in this chest x-ray image?"],
["Could you highlight any abnormalities or concerns in this chest x-ray image?"],
["What specific features of the lungs and heart are visible in this chest x-ray image?"],
["What is the most prominent feature visible in this chest x-ray image, and how is it indicative of the patient's health?"],
["Based on the findings in this chest x-ray image, what is the overall impression?"],
],)
example_xrays.click(fn=set_example_xray, inputs=example_xrays, outputs=example_xrays.components)
upload_button.click(upload_img, [image, text_input, chat_state], [image, text_input, upload_button, chat_state, img_list])
example_texts.click(set_example_text_input, inputs=example_texts, outputs=text_input).then(
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]
)
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)
gr.Markdown(disclaimer)
demo.launch(share=True, enable_queue=True)