vlm-demo / serve /gradio_web_server.py
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"""
gradio_web_server.py
Entry point for all VLM-Bench interactive demos; specify model and get a gradio UI where you can chat with it!
This file is copied from the script used to define the gradio web server in the LLaVa codebase:
https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/gradio_web_server.py with only very minor
modifications.
"""
import argparse
import datetime
import hashlib
import json
import os
import time
import gradio as gr
import requests
from llava.constants import LOGDIR
from llava.conversation import conv_templates, default_conversation
from llava.utils import build_logger, moderation_msg, server_error_msg, violates_moderation
from serve import INTERACTION_MODES_MAP, MODEL_ID_TO_NAME
logger = build_logger("gradio_web_server", "gradio_web_server.log")
headers = {"User-Agent": "PrismaticVLMs Client"}
no_change_btn = gr.Button.update()
enable_btn = gr.Button.update(interactive=True)
disable_btn = gr.Button.update(interactive=False)
def get_conv_log_filename():
t = datetime.datetime.now()
name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
return name
def get_model_list():
ret = requests.post(args.controller_url + "/refresh_all_workers")
assert ret.status_code == 200
ret = requests.post(args.controller_url + "/list_models")
models = ret.json()["models"]
models = sorted(
models, key=lambda x: list(MODEL_ID_TO_NAME.values()).index(x) if x in MODEL_ID_TO_NAME.values() else len(models)
)
logger.info(f"Models: {models}")
return models
get_window_url_params = """
function() {
const params = new URLSearchParams(window.location.search);
url_params = Object.fromEntries(params);
console.log(url_params);
return url_params;
}
"""
def load_demo(url_params, request: gr.Request):
logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")
dropdown_update = gr.Dropdown.update(visible=True)
if "model" in url_params:
model = url_params["model"]
if model in models:
dropdown_update = gr.Dropdown.update(value=model, visible=True)
state = default_conversation.copy()
return state, dropdown_update
def load_demo_refresh_model_list(request: gr.Request):
logger.info(f"load_demo. ip: {request.client.host}")
models = get_model_list()
state = default_conversation.copy()
dropdown_update = gr.Dropdown.update(choices=models, value=models[0] if len(models) > 0 else "")
return state, dropdown_update
def vote_last_response(state, vote_type, model_selector, request: gr.Request):
with open(get_conv_log_filename(), "a") as fout:
data = {
"tstamp": round(time.time(), 4),
"type": vote_type,
"model": model_selector,
"state": state.dict(),
"ip": request.client.host,
}
fout.write(json.dumps(data) + "\n")
# def upvote_last_response(state, model_selector, request: gr.Request):
# logger.info(f"upvote. ip: {request.client.host}")
# vote_last_response(state, "upvote", model_selector, request)
# return ("",) + (disable_btn,) * 3
# def downvote_last_response(state, model_selector, request: gr.Request):
# logger.info(f"downvote. ip: {request.client.host}")
# vote_last_response(state, "downvote", model_selector, request)
# return ("",) + (disable_btn,) * 3
# def flag_last_response(state, model_selector, request: gr.Request):
# logger.info(f"flag. ip: {request.client.host}")
# vote_last_response(state, "flag", model_selector, request)
# return ("",) + (disable_btn,) * 3
def regenerate(state, image_process_mode, request: gr.Request):
logger.info(f"regenerate. ip: {request.client.host}")
state.messages[-1][-1] = None
prev_human_msg = state.messages[-2]
if type(prev_human_msg[1]) in (tuple, list):
prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode)
state.skip_next = False
return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
def clear_history(request: gr.Request):
logger.info(f"clear_history. ip: {request.client.host}")
state = default_conversation.copy()
return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
def add_text(state, text, image, image_process_mode, request: gr.Request):
logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")
if len(text) <= 0 and image is None:
state.skip_next = True
return (state, state.to_gradio_chatbot(), "", None) + (no_change_btn,) * 5
if args.moderate:
flagged = violates_moderation(text)
if flagged:
state.skip_next = True
return (state, state.to_gradio_chatbot(), moderation_msg, None) + (no_change_btn,) * 5
text = text[:1536] # Hard cut-off
if image is not None:
text = text[:1200] # Hard cut-off for images
if "<image>" not in text:
# text = '<Image><image></Image>' + text
text = text + "\n<image>"
text = (text, image, image_process_mode)
if len(state.get_images(return_pil=True)) > 0:
state = default_conversation.copy()
state.append_message(state.roles[0], text)
state.append_message(state.roles[1], None)
state.skip_next = False
return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
def http_bot(state, model_selector, interaction_mode, temperature, max_new_tokens, request: gr.Request):
logger.info(f"http_bot. ip: {request.client.host}")
start_tstamp = time.time()
model_name = model_selector
if state.skip_next:
# This generate call is skipped due to invalid inputs
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
return
if len(state.messages) == state.offset + 2:
# First round of conversation
# (Note): Hardcoding llava_v1 conv template for now
new_state = conv_templates["llava_v1"].copy()
new_state.append_message(new_state.roles[0], state.messages[-2][1])
new_state.append_message(new_state.roles[1], None)
state = new_state
# Query worker address
controller_url = args.controller_url
ret = requests.post(controller_url + "/get_worker_address", json={"model": model_name})
worker_addr = ret.json()["address"]
logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}")
# No available worker
if worker_addr == "":
state.messages[-1][-1] = server_error_msg
yield (state, state.to_gradio_chatbot(), disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
return
# Construct prompt
prompt = state.get_prompt()
all_images = state.get_images(return_pil=True)
all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images]
for image, im_hash in zip(all_images, all_image_hash):
t = datetime.datetime.now()
filename = os.path.join(LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{im_hash}.jpg")
if not os.path.isfile(filename):
os.makedirs(os.path.dirname(filename), exist_ok=True)
image.save(filename)
# Make requests
pload = {
"model": model_name,
"prompt": prompt,
"interaction_mode": interaction_mode,
"temperature": float(temperature),
"max_new_tokens": int(max_new_tokens),
"images": f"List of {len(state.get_images())} images: {all_image_hash}",
}
logger.info(f"==== request ====\n{pload}")
pload["images"] = state.get_images()
state.messages[-1][-1] = "β–Œ"
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
try:
# Stream output
response = requests.post(
worker_addr + "/worker_generate_stream", headers=headers, json=pload, stream=True, timeout=10
)
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
if chunk:
data = json.loads(chunk.decode())
if data["error_code"] == 0:
output = data["text"][len(prompt) :].strip()
state.messages[-1][-1] = output + "β–Œ"
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
else:
output = data["text"] + f" (error_code: {data['error_code']})"
state.messages[-1][-1] = output
yield (state, state.to_gradio_chatbot()) + (
disable_btn,
disable_btn,
disable_btn,
enable_btn,
enable_btn,
)
return
time.sleep(0.03)
except requests.exceptions.RequestException:
state.messages[-1][-1] = server_error_msg
yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
return
state.messages[-1][-1] = state.messages[-1][-1][:-1]
yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
finish_tstamp = time.time()
logger.info(f"{output}")
with open(get_conv_log_filename(), "a") as fout:
data = {
"tstamp": round(finish_tstamp, 4),
"type": "chat",
"model": model_name,
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"state": state.dict(),
"images": all_image_hash,
"ip": request.client.host,
}
fout.write(json.dumps(data) + "\n")
title_markdown = """
# Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
[[Project Page](TODO)] [[Code](TODO)]
[[Models](TODO)]
| πŸ“š [[Paper](TODO)]
"""
tos_markdown = """
### Terms of use
By using this service, users are required to agree to the following terms:
The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may
generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The
service may collect user dialogue data for future research. Please click the "Flag" button if you get any
inappropriate answer! We will collect those to keep improving our moderator. For an optimal experience,
please use desktop computers for this demo, as mobile devices may compromise its quality. This website
is heavily inspired by the website released by [LLaVA](https://github.com/haotian-liu/LLaVA).
"""
learn_more_markdown = """
### License
The service is a research preview intended for non-commercial use only, subject to the model
[License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA,
[Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI,
and [Privacy Practices]
(https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb)
of ShareGPT. Please contact us if you find any potential violation.
"""
block_css = """
#buttons button {
min-width: min(120px,100%);
}
"""
def build_demo(embed_mode):
textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False)
with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="stone")) as demo:
state = gr.State()
if not embed_mode:
gr.Markdown(title_markdown)
with gr.Row():
with gr.Column(scale=3):
with gr.Row(elem_id="model_selector_row"):
model_selector = gr.Dropdown(
choices=models,
value=models[0] if len(models) > 0 else "",
interactive=True,
show_label=False,
container=False,
)
imagebox = gr.Image(type="pil")
image_process_mode = gr.Radio(
["Crop", "Resize", "Pad", "Default"],
value="Default",
label="Preprocess for non-square image",
visible=False,
)
cur_dir = os.path.dirname(os.path.abspath(__file__))
gr.Examples(
examples=[
[f"{cur_dir}/examples/cows_in_pasture.png", "How many cows are in this image?"],
[
f"{cur_dir}/examples/monkey_knives.png",
"What is happening in this image?",
],
],
inputs=[imagebox, textbox],
)
with gr.Accordion("Parameters", open=False):
temperature = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.2,
step=0.1,
interactive=True,
label="Temperature",
)
max_output_tokens = gr.Slider(
minimum=0,
maximum=4096,
value=2048,
step=64,
interactive=True,
label="Max output tokens",
)
with gr.Accordion("Interaction Mode", open=False):
interaction_modes = list(INTERACTION_MODES_MAP.keys())
interaction_mode = gr.Dropdown(
choices=interaction_modes,
value=interaction_modes[0] if len(interaction_modes) > 0 else "Chat",
interactive=True,
show_label=False,
container=False,
)
with gr.Column(scale=8):
chatbot = gr.Chatbot(elem_id="chatbot", label="PrismaticVLMs Chatbot", height=550)
with gr.Row():
with gr.Column(scale=8):
textbox.render()
with gr.Column(scale=1, min_width=50):
submit_btn = gr.Button(value="Generate", variant="primary")
with gr.Row(elem_id="buttons"):
# upvote_btn = gr.Button(value="πŸ‘ Upvote", interactive=False)
# downvote_btn = gr.Button(value="πŸ‘Ž Downvote", interactive=False)
# flag_btn = gr.Button(value="⚠️ Flag", interactive=False)
# stop_btn = gr.Button(value="⏹️ Stop Generation", interactive=False)
regenerate_btn = gr.Button(value="πŸ”„ Regenerate", interactive=False)
clear_btn = gr.Button(value="πŸ—‘οΈ Clear", interactive=False)
if not embed_mode:
gr.Markdown(tos_markdown)
gr.Markdown(learn_more_markdown)
url_params = gr.JSON(visible=False)
# Register listeners
btn_list = [regenerate_btn, clear_btn]
# upvote_btn.click(
# upvote_last_response, [state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn], queue=False
# )
# downvote_btn.click(
# downvote_last_response, [state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn], queue=False
# )
# flag_btn.click(
# flag_last_response, [state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn], queue=False
# )
regenerate_btn.click(
regenerate, [state, image_process_mode], [state, chatbot, textbox, imagebox, *btn_list], queue=False
).then(
http_bot,
[state, model_selector, interaction_mode, temperature, max_output_tokens],
[state, chatbot, *btn_list],
)
clear_btn.click(clear_history, None, [state, chatbot, textbox, imagebox, *btn_list], queue=False)
textbox.submit(
add_text,
[state, textbox, imagebox, image_process_mode],
[state, chatbot, textbox, imagebox, *btn_list],
queue=False,
).then(
http_bot,
[state, model_selector, interaction_mode, temperature, max_output_tokens],
[state, chatbot, *btn_list],
)
submit_btn.click(
add_text,
[state, textbox, imagebox, image_process_mode],
[state, chatbot, textbox, imagebox, *btn_list],
queue=False,
).then(
http_bot,
[state, model_selector, interaction_mode, temperature, max_output_tokens],
[state, chatbot, *btn_list],
)
if args.model_list_mode == "once":
demo.load(load_demo, [url_params], [state, model_selector], _js=get_window_url_params, queue=False)
elif args.model_list_mode == "reload":
demo.load(load_demo_refresh_model_list, None, [state, model_selector], queue=False)
else:
raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
return demo
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="0.0.0.0")
parser.add_argument("--port", type=int)
parser.add_argument("--controller-url", type=str, default="http://localhost:21001")
parser.add_argument("--concurrency-count", type=int, default=10)
parser.add_argument("--model-list-mode", type=str, default="once", choices=["once", "reload"])
parser.add_argument("--share", action="store_true")
parser.add_argument("--moderate", action="store_true")
parser.add_argument("--embed", action="store_true")
args = parser.parse_args()
logger.info(f"args: {args}")
models = get_model_list()
logger.info(args)
demo = build_demo(args.embed)
demo.queue(concurrency_count=args.concurrency_count, api_open=False).launch(
server_name=args.host, server_port=args.port, share=args.share
)