"""
The gradio demo server for chatting with a single model.
"""
import argparse
from collections import defaultdict
import datetime
import hashlib
import json
import os
import random
import time
import uuid
import gradio as gr
import requests
from fastchat.constants import (
LOGDIR,
WORKER_API_TIMEOUT,
ErrorCode,
MODERATION_MSG,
CONVERSATION_LIMIT_MSG,
RATE_LIMIT_MSG,
SERVER_ERROR_MSG,
INPUT_CHAR_LEN_LIMIT,
CONVERSATION_TURN_LIMIT,
SESSION_EXPIRATION_TIME,
)
from fastchat.model.model_adapter import (
get_conversation_template,
)
from fastchat.model.model_registry import get_model_info, model_info
from fastchat.serve.api_provider import get_api_provider_stream_iter
from fastchat.utils import (
build_logger,
get_window_url_params_js,
get_window_url_params_with_tos_js,
moderation_filter,
parse_gradio_auth_creds,
load_image,
)
logger = build_logger("gradio_web_server", "gradio_web_server.log")
headers = {"User-Agent": "FastChat Client"}
no_change_btn = gr.Button()
enable_btn = gr.Button(interactive=True, visible=True)
disable_btn = gr.Button(interactive=False)
invisible_btn = gr.Button(interactive=False, visible=False)
controller_url = None
enable_moderation = False
acknowledgment_md = """
### Terms of Service
Users are required to agree to the following terms before using the service:
The service is a research preview. 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 collects user dialogue data and reserves the right to distribute it under a Creative Commons Attribution (CC-BY) or a similar license.
Additionally, Bard is offered on LMSys for research purposes only. To access the Bard product, please visit its [website](http://bard.google.com).
### Acknowledgment
We thank [Kaggle](https://www.kaggle.com/), [MBZUAI](https://mbzuai.ac.ae/), [a16z](https://www.a16z.com/), [Together AI](https://www.together.ai/), [Anyscale](https://www.anyscale.com/), [HuggingFace](https://huggingface.co/) for their generous [sponsorship](https://lmsys.org/donations/).
<div class="sponsor-image-about">
<img src="https://storage.googleapis.com/public-arena-asset/kaggle.png" alt="Kaggle">
<img src="https://storage.googleapis.com/public-arena-asset/mbzuai.jpeg" alt="MBZUAI">
<img src="https://storage.googleapis.com/public-arena-asset/a16z.jpeg" alt="a16z">
<img src="https://storage.googleapis.com/public-arena-asset/together.png" alt="Together AI">
<img src="https://storage.googleapis.com/public-arena-asset/anyscale.png" alt="AnyScale">
<img src="https://storage.googleapis.com/public-arena-asset/huggingface.png" alt="HuggingFace">
</div>
"""
# JSON file format of API-based models:
# {
# "gpt-3.5-turbo-0613": {
# "model_name": "gpt-3.5-turbo-0613",
# "api_type": "openai",
# "api_base": "https://api.openai.com/v1",
# "api_key": "sk-******",
# "anony_only": false
# }
# }
# "api_type" can be one of the following: openai, anthropic, gemini, mistral.
# "anony_only" means whether to show this model in anonymous mode only.
api_endpoint_info = {}
class State:
def __init__(self, model_name):
self.conv = get_conversation_template(model_name)
self.conv_id = uuid.uuid4().hex
self.skip_next = False
self.model_name = model_name
def to_gradio_chatbot(self):
return self.conv.to_gradio_chatbot()
def dict(self):
base = self.conv.dict()
base.update(
{
"conv_id": self.conv_id,
"model_name": self.model_name,
}
)
return base
def set_global_vars(controller_url_, enable_moderation_):
global controller_url, enable_moderation
controller_url = controller_url_
enable_moderation = enable_moderation_
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(controller_url, register_api_endpoint_file, multimodal):
global api_endpoint_info
# Add models from the controller
if controller_url:
ret = requests.post(controller_url + "/refresh_all_workers")
assert ret.status_code == 200
if multimodal:
ret = requests.post(controller_url + "/list_multimodal_models")
models = ret.json()["models"]
else:
ret = requests.post(controller_url + "/list_language_models")
models = ret.json()["models"]
else:
models = []
# Add models from the API providers
if register_api_endpoint_file:
api_endpoint_info = json.load(open(register_api_endpoint_file))
for mdl, mdl_dict in api_endpoint_info.items():
mdl_multimodal = mdl_dict.get("multimodal", False)
if multimodal and mdl_multimodal:
models += [mdl]
elif not multimodal and not mdl_multimodal:
models += [mdl]
# Remove anonymous models
models = list(set(models))
visible_models = models.copy()
for mdl in visible_models:
if mdl not in api_endpoint_info:
continue
mdl_dict = api_endpoint_info[mdl]
if mdl_dict["anony_only"]:
visible_models.remove(mdl)
# Sort models and add descriptions
priority = {k: f"___{i:03d}" for i, k in enumerate(model_info)}
models.sort(key=lambda x: priority.get(x, x))
visible_models.sort(key=lambda x: priority.get(x, x))
logger.info(f"All models: {models}")
logger.info(f"Visible models: {visible_models}")
return visible_models, models
def load_demo_single(models, url_params):
selected_model = models[0] if len(models) > 0 else ""
if "model" in url_params:
model = url_params["model"]
if model in models:
selected_model = model
dropdown_update = gr.Dropdown(choices=models, value=selected_model, visible=True)
state = None
return state, dropdown_update
def load_demo(url_params, request: gr.Request):
global models
ip = get_ip(request)
logger.info(f"load_demo. ip: {ip}. params: {url_params}")
if args.model_list_mode == "reload":
models, all_models = get_model_list(
controller_url, args.register_api_endpoint_file, False
)
return load_demo_single(models, url_params)
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": get_ip(request),
}
fout.write(json.dumps(data) + "\n")
def upvote_last_response(state, model_selector, request: gr.Request):
ip = get_ip(request)
logger.info(f"upvote. ip: {ip}")
vote_last_response(state, "upvote", model_selector, request)
return ("",) + (disable_btn,) * 3
def downvote_last_response(state, model_selector, request: gr.Request):
ip = get_ip(request)
logger.info(f"downvote. ip: {ip}")
vote_last_response(state, "downvote", model_selector, request)
return ("",) + (disable_btn,) * 3
def flag_last_response(state, model_selector, request: gr.Request):
ip = get_ip(request)
logger.info(f"flag. ip: {ip}")
vote_last_response(state, "flag", model_selector, request)
return ("",) + (disable_btn,) * 3
def regenerate(state, request: gr.Request):
ip = get_ip(request)
logger.info(f"regenerate. ip: {ip}")
state.conv.update_last_message(None)
return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
def clear_history(request: gr.Request):
ip = get_ip(request)
logger.info(f"clear_history. ip: {ip}")
state = None
return (state, [], "", None) + (disable_btn,) * 5
def get_ip(request: gr.Request):
if "cf-connecting-ip" in request.headers:
ip = request.headers["cf-connecting-ip"]
else:
ip = request.client.host
return ip
def _prepare_text_with_image(state, text, image):
if image is not None:
if len(state.conv.get_images()) > 0:
# reset convo with new image
state.conv = get_conversation_template(state.model_name)
image = state.conv.convert_image_to_base64(
image
) # PIL type is not JSON serializable
text = text, [image]
return text
def add_text(state, model_selector, text, image, request: gr.Request):
ip = get_ip(request)
logger.info(f"add_text. ip: {ip}. len: {len(text)}")
if state is None:
state = State(model_selector)
if len(text) <= 0:
state.skip_next = True
return (state, state.to_gradio_chatbot(), "") + (no_change_btn,) * 5
flagged = moderation_filter(text, [state.model_name])
if flagged:
logger.info(f"violate moderation. ip: {ip}. text: {text}")
# overwrite the original text
text = MODERATION_MSG
if (len(state.conv.messages) - state.conv.offset) // 2 >= CONVERSATION_TURN_LIMIT:
logger.info(f"conversation turn limit. ip: {ip}. text: {text}")
state.skip_next = True
return (state, state.to_gradio_chatbot(), CONVERSATION_LIMIT_MSG) + (
no_change_btn,
) * 5
text = text[:INPUT_CHAR_LEN_LIMIT] # Hard cut-off
text = _prepare_text_with_image(state, text, image)
state.conv.append_message(state.conv.roles[0], text)
state.conv.append_message(state.conv.roles[1], None)
return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
def model_worker_stream_iter(
conv,
model_name,
worker_addr,
prompt,
temperature,
repetition_penalty,
top_p,
max_new_tokens,
images,
):
# Make requests
gen_params = {
"model": model_name,
"prompt": prompt,
"temperature": temperature,
"repetition_penalty": repetition_penalty,
"top_p": top_p,
"max_new_tokens": max_new_tokens,
"stop": conv.stop_str,
"stop_token_ids": conv.stop_token_ids,
"echo": False,
}
logger.info(f"==== request ====\n{gen_params}")
if len(images) > 0:
gen_params["images"] = images
# Stream output
response = requests.post(
worker_addr + "/worker_generate_stream",
headers=headers,
json=gen_params,
stream=True,
timeout=WORKER_API_TIMEOUT,
)
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
if chunk:
data = json.loads(chunk.decode())
yield data
def is_limit_reached(model_name, ip):
monitor_url = "http://localhost:9090"
try:
ret = requests.get(
f"{monitor_url}/is_limit_reached?model={model_name}&user_id={ip}", timeout=1
)
obj = ret.json()
return obj
except Exception as e:
logger.info(f"monitor error: {e}")
return None
def bot_response(
state,
temperature,
top_p,
max_new_tokens,
request: gr.Request,
apply_rate_limit=True,
):
ip = get_ip(request)
logger.info(f"bot_response. ip: {ip}")
start_tstamp = time.time()
temperature = float(temperature)
top_p = float(top_p)
max_new_tokens = int(max_new_tokens)
if state.skip_next:
# This generate call is skipped due to invalid inputs
state.skip_next = False
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
return
if apply_rate_limit:
ret = is_limit_reached(state.model_name, ip)
if ret is not None and ret["is_limit_reached"]:
error_msg = RATE_LIMIT_MSG + "\n\n" + ret["reason"]
logger.info(f"rate limit reached. ip: {ip}. error_msg: {ret['reason']}")
state.conv.update_last_message(error_msg)
yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
return
conv, model_name = state.conv, state.model_name
model_api_dict = (
api_endpoint_info[model_name] if model_name in api_endpoint_info else None
)
images = conv.get_images()
if model_api_dict is None:
# Query worker address
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 == "":
conv.update_last_message(SERVER_ERROR_MSG)
yield (
state,
state.to_gradio_chatbot(),
disable_btn,
disable_btn,
disable_btn,
enable_btn,
enable_btn,
)
return
# Construct prompt.
# We need to call it here, so it will not be affected by "▌".
prompt = conv.get_prompt()
# Set repetition_penalty
if "t5" in model_name:
repetition_penalty = 1.2
else:
repetition_penalty = 1.0
stream_iter = model_worker_stream_iter(
conv,
model_name,
worker_addr,
prompt,
temperature,
repetition_penalty,
top_p,
max_new_tokens,
images,
)
else:
stream_iter = get_api_provider_stream_iter(
conv,
model_name,
model_api_dict,
temperature,
top_p,
max_new_tokens,
)
conv.update_last_message("▌")
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
try:
for i, data in enumerate(stream_iter):
if data["error_code"] == 0:
output = data["text"].strip()
conv.update_last_message(output + "▌")
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
else:
output = data["text"] + f"\n\n(error_code: {data['error_code']})"
conv.update_last_message(output)
yield (state, state.to_gradio_chatbot()) + (
disable_btn,
disable_btn,
disable_btn,
enable_btn,
enable_btn,
)
return
output = data["text"].strip()
conv.update_last_message(output)
yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
except requests.exceptions.RequestException as e:
conv.update_last_message(
f"{SERVER_ERROR_MSG}\n\n"
f"(error_code: {ErrorCode.GRADIO_REQUEST_ERROR}, {e})"
)
yield (state, state.to_gradio_chatbot()) + (
disable_btn,
disable_btn,
disable_btn,
enable_btn,
enable_btn,
)
return
except Exception as e:
conv.update_last_message(
f"{SERVER_ERROR_MSG}\n\n"
f"(error_code: {ErrorCode.GRADIO_STREAM_UNKNOWN_ERROR}, {e})"
)
yield (state, state.to_gradio_chatbot()) + (
disable_btn,
disable_btn,
disable_btn,
enable_btn,
enable_btn,
)
return
finish_tstamp = time.time()
logger.info(f"{output}")
# We load the image because gradio accepts base64 but that increases file size by ~1.33x
loaded_images = [load_image(image) for image in images]
images_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in loaded_images]
for image, hash_str in zip(loaded_images, images_hash):
t = datetime.datetime.now()
filename = os.path.join(
LOGDIR,
"serve_images",
f"{hash_str}.jpg",
)
if not os.path.isfile(filename):
os.makedirs(os.path.dirname(filename), exist_ok=True)
image.save(filename)
with open(get_conv_log_filename(), "a") as fout:
data = {
"tstamp": round(finish_tstamp, 4),
"type": "chat",
"model": model_name,
"gen_params": {
"temperature": temperature,
"top_p": top_p,
"max_new_tokens": max_new_tokens,
},
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"state": state.dict(),
"ip": get_ip(request),
"images": images_hash,
}
fout.write(json.dumps(data) + "\n")
block_css = """
#notice_markdown .prose {
font-size: 120% !important;
}
#notice_markdown th {
display: none;
}
#notice_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#model_description_markdown {
font-size: 120% !important;
}
#leaderboard_markdown .prose {
font-size: 120% !important;
}
#leaderboard_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_dataframe td {
line-height: 0.1em;
}
#about_markdown .prose {
font-size: 120% !important;
}
#ack_markdown .prose {
font-size: 120% !important;
}
footer {
display:none !important;
}
.sponsor-image-about img {
margin: 0 20px;
margin-top: 20px;
height: 40px;
max-height: 100%;
width: auto;
float: left;
}
"""
def get_model_description_md(models):
model_description_md = """
| | | |
| ---- | ---- | ---- |
"""
ct = 0
visited = set()
for i, name in enumerate(models):
minfo = get_model_info(name)
if minfo.simple_name in visited:
continue
visited.add(minfo.simple_name)
one_model_md = f"[{minfo.simple_name}]({minfo.link}): {minfo.description}"
if ct % 3 == 0:
model_description_md += "|"
model_description_md += f" {one_model_md} |"
if ct % 3 == 2:
model_description_md += "\n"
ct += 1
return model_description_md
def build_about():
about_markdown = """
# About Us
Chatbot Arena is an open-source research project developed by members from [LMSYS](https://lmsys.org/about/) and UC Berkeley [SkyLab](https://sky.cs.berkeley.edu/). Our mission is to build an open crowdsourced platform to collect human feedback and evaluate LLMs under real-world scenarios. We open-source our [FastChat](https://github.com/lm-sys/FastChat) project at GitHub and release chat and human feedback datasets [here](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md). We invite everyone to join us in this journey!
## Read More
- Chatbot Arena [launch post](https://lmsys.org/blog/2023-05-03-arena/), [data release](https://lmsys.org/blog/2023-07-20-dataset/)
- LMSYS-Chat-1M [report](https://arxiv.org/abs/2309.11998)
## Core Members
[Lianmin Zheng](https://lmzheng.net/), [Wei-Lin Chiang](https://infwinston.github.io/), [Ying Sheng](https://sites.google.com/view/yingsheng/home), [Siyuan Zhuang](https://scholar.google.com/citations?user=KSZmI5EAAAAJ)
## Advisors
[Ion Stoica](http://people.eecs.berkeley.edu/~istoica/), [Joseph E. Gonzalez](https://people.eecs.berkeley.edu/~jegonzal/), [Hao Zhang](https://cseweb.ucsd.edu/~haozhang/)
## Contact Us
- Follow our [Twitter](https://twitter.com/lmsysorg), [Discord](https://discord.gg/HSWAKCrnFx) or email us at lmsys.org@gmail.com
- File issues on [GitHub](https://github.com/lm-sys/FastChat)
- Download our datasets and models on [HuggingFace](https://huggingface.co/lmsys)
## Acknowledgment
We thank [SkyPilot](https://github.com/skypilot-org/skypilot) and [Gradio](https://github.com/gradio-app/gradio) team for their system support.
We also thank [Kaggle](https://www.kaggle.com/), [MBZUAI](https://mbzuai.ac.ae/), [a16z](https://www.a16z.com/), [Together AI](https://www.together.ai/), [Anyscale](https://www.anyscale.com/), [HuggingFace](https://huggingface.co/) for their generous sponsorship. Learn more about partnership [here](https://lmsys.org/donations/).
<div class="sponsor-image-about">
<img src="https://storage.googleapis.com/public-arena-asset/kaggle.png" alt="Kaggle">
<img src="https://storage.googleapis.com/public-arena-asset/mbzuai.jpeg" alt="MBZUAI">
<img src="https://storage.googleapis.com/public-arena-asset/a16z.jpeg" alt="a16z">
<img src="https://storage.googleapis.com/public-arena-asset/together.png" alt="Together AI">
<img src="https://storage.googleapis.com/public-arena-asset/anyscale.png" alt="AnyScale">
<img src="https://storage.googleapis.com/public-arena-asset/huggingface.png" alt="HuggingFace">
</div>
"""
gr.Markdown(about_markdown, elem_id="about_markdown")
def build_single_model_ui(models, add_promotion_links=False):
promotion = (
"""
- | [GitHub](https://github.com/lm-sys/FastChat) | [Dataset](https://github.com/lm-sys/FastChat/blob/main/docs/dataset_release.md) | [Twitter](https://twitter.com/lmsysorg) | [Discord](https://discord.gg/HSWAKCrnFx) |
- Introducing Llama 2: The Next Generation Open Source Large Language Model. [[Website]](https://ai.meta.com/llama/)
- Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality. [[Blog]](https://lmsys.org/blog/2023-03-30-vicuna/)
## 🤖 Choose any model to chat
"""
if add_promotion_links
else ""
)
notice_markdown = f"""
# 🏔️ Chat with Open Large Language Models
{promotion}
"""
state = gr.State()
gr.Markdown(notice_markdown, elem_id="notice_markdown")
with gr.Group(elem_id="share-region-named"):
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,
)
with gr.Row():
with gr.Accordion(
f"🔍 Expand to see the descriptions of {len(models)} models",
open=False,
):
model_description_md = get_model_description_md(models)
gr.Markdown(model_description_md, elem_id="model_description_markdown")
chatbot = gr.Chatbot(
elem_id="chatbot",
label="Scroll down and start chatting",
height=550,
show_copy_button=True,
)
with gr.Row():
textbox = gr.Textbox(
show_label=False,
placeholder="👉 Enter your prompt and press ENTER",
elem_id="input_box",
)
send_btn = gr.Button(value="Send", variant="primary", scale=0)
with gr.Row() as button_row:
upvote_btn = gr.Button(value="👍 Upvote", interactive=False)
downvote_btn = gr.Button(value="👎 Downvote", interactive=False)
flag_btn = gr.Button(value="⚠️ Flag", interactive=False)
regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False)
clear_btn = gr.Button(value="🗑️ Clear history", interactive=False)
with gr.Accordion("Parameters", open=False) as parameter_row:
temperature = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.7,
step=0.1,
interactive=True,
label="Temperature",
)
top_p = gr.Slider(
minimum=0.0,
maximum=1.0,
value=1.0,
step=0.1,
interactive=True,
label="Top P",
)
max_output_tokens = gr.Slider(
minimum=16,
maximum=2048,
value=1024,
step=64,
interactive=True,
label="Max output tokens",
)
if add_promotion_links:
gr.Markdown(acknowledgment_md, elem_id="ack_markdown")
# Register listeners
imagebox = gr.State(None)
btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn]
upvote_btn.click(
upvote_last_response,
[state, model_selector],
[textbox, upvote_btn, downvote_btn, flag_btn],
)
downvote_btn.click(
downvote_last_response,
[state, model_selector],
[textbox, upvote_btn, downvote_btn, flag_btn],
)
flag_btn.click(
flag_last_response,
[state, model_selector],
[textbox, upvote_btn, downvote_btn, flag_btn],
)
regenerate_btn.click(
regenerate, state, [state, chatbot, textbox, imagebox] + btn_list
).then(
bot_response,
[state, temperature, top_p, max_output_tokens],
[state, chatbot] + btn_list,
)
clear_btn.click(clear_history, None, [state, chatbot, textbox, imagebox] + btn_list)
model_selector.change(
clear_history, None, [state, chatbot, textbox, imagebox] + btn_list
)
textbox.submit(
add_text,
[state, model_selector, textbox, imagebox],
[state, chatbot, textbox, imagebox] + btn_list,
).then(
bot_response,
[state, temperature, top_p, max_output_tokens],
[state, chatbot] + btn_list,
)
send_btn.click(
add_text,
[state, model_selector, textbox, imagebox],
[state, chatbot, textbox, imagebox] + btn_list,
).then(
bot_response,
[state, temperature, top_p, max_output_tokens],
[state, chatbot] + btn_list,
)
return [state, model_selector]
def build_demo(models):
with gr.Blocks(
title="Chat with Open Large Language Models",
theme=gr.themes.Default(),
css=block_css,
) as demo:
url_params = gr.JSON(visible=False)
state, model_selector = build_single_model_ui(models)
if args.model_list_mode not in ["once", "reload"]:
raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
if args.show_terms_of_use:
load_js = get_window_url_params_with_tos_js
else:
load_js = get_window_url_params_js
demo.load(
load_demo,
[url_params],
[
state,
model_selector,
],
js=load_js,
)
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(
"--share",
action="store_true",
help="Whether to generate a public, shareable link",
)
parser.add_argument(
"--controller-url",
type=str,
default="http://localhost:21001",
help="The address of the controller",
)
parser.add_argument(
"--concurrency-count",
type=int,
default=10,
help="The concurrency count of the gradio queue",
)
parser.add_argument(
"--model-list-mode",
type=str,
default="once",
choices=["once", "reload"],
help="Whether to load the model list once or reload the model list every time",
)
parser.add_argument(
"--moderate",
action="store_true",
help="Enable content moderation to block unsafe inputs",
)
parser.add_argument(
"--show-terms-of-use",
action="store_true",
help="Shows term of use before loading the demo",
)
parser.add_argument(
"--register-api-endpoint-file",
type=str,
help="Register API-based model endpoints from a JSON file",
)
parser.add_argument(
"--gradio-auth-path",
type=str,
help='Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"',
)
parser.add_argument(
"--gradio-root-path",
type=str,
help="Sets the gradio root path, eg /abc/def. Useful when running behind a reverse-proxy or at a custom URL path prefix",
)
args = parser.parse_args()
logger.info(f"args: {args}")
# Set global variables
set_global_vars(args.controller_url, args.moderate)
models, all_models = get_model_list(
args.controller_url, args.register_api_endpoint_file, False
)
# Set authorization credentials
auth = None
if args.gradio_auth_path is not None:
auth = parse_gradio_auth_creds(args.gradio_auth_path)
# Launch the demo
demo = build_demo(models)
demo.queue(
default_concurrency_limit=args.concurrency_count,
status_update_rate=10,
api_open=False,
).launch(
server_name=args.host,
server_port=args.port,
share=args.share,
max_threads=200,
auth=auth,
root_path=args.gradio_root_path,
)