"""
The gradio demo server for chatting with a single model.
"""
import argparse
from collections import defaultdict
import datetime
import json
import os
import random
import time
from PIL import Image
import numpy as np
import uuid
import gradio as gr
import requests
from fastchat.conversation import SeparatorStyle
from fastchat.constants import (
LOGDIR,
WORKER_API_TIMEOUT,
ErrorCode,
MODERATION_MSG,
CONVERSATION_LIMIT_MSG,
SERVER_ERROR_MSG,
INPUT_CHAR_LEN_LIMIT,
CONVERSATION_TURN_LIMIT,
SESSION_EXPIRATION_TIME,
)
from fastchat.model.model_adapter import (
get_conversation_template,
ANTHROPIC_MODEL_LIST,
)
from fastchat.model.model_registry import get_model_info, model_info
from fastchat.serve.api_provider import (
anthropic_api_stream_iter,
openai_api_stream_iter,
palm_api_stream_iter,
init_palm_chat,
)
from fastchat.utils import (
build_logger,
moderation_filter,
get_window_url_params_js,
get_window_url_params_with_tos_js,
parse_gradio_auth_creds,
)
from fastchat.serve.model_worker import ModelWorker
from fastchat.serve.base_model_worker import app
import uvicorn
logger = build_logger("gradio_web_server", "gradio_web_server.log")
headers = {"User-Agent": "VisualArena Client"}
# no_change_btn = gr.Button.update()
# enable_btn = gr.Button.update(interactive=True, visible=True)
# disable_btn = gr.Button.update(interactive=False)
# invisible_btn = gr.Button.update(interactive=False, visible=False)
enable_btn = gr.Button(interactive=True, visible=True)
disable_btn = gr.Button(interactive=False)
invisible_btn = gr.Button(interactive=False, visible=False)
no_change_btn = gr.Button(value="No Change", interactive=True, visible=True)
# enable_btn = gr.Button(value="Enable", interactive=True, visible=True)
# disable_btn = gr.Button(value="Disable", interactive=False)
# invisible_btn = gr.Button(value="Invisible", interactive=False, visible=False)
controller_url = None
enable_moderation = False
acknowledgment_md = """
### Acknowledgment
"""
ip_expiration_dict = defaultdict(lambda: 0)
# Information about custom OpenAI compatible API models.
# JSON file format:
# {
# "vicuna-7b": {
# "model_name": "vicuna-7b-v1.5",
# "api_base": "http://8.8.8.55:5555/v1",
# "api_key": "password"
# },
# }
openai_compatible_models_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
if model_name == "palm-2":
# According to release note, "chat-bison@001" is PaLM 2 for chat.
# https://cloud.google.com/vertex-ai/docs/release-notes#May_10_2023
self.palm_chat = init_palm_chat("chat-bison@001")
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
class ImageState:
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
self.online_load = False
self.prompt = None
# self.conv = prompt
self.output = None
# if model_name == "palm-2":
# # According to release note, "chat-bison@001" is PaLM 2 for chat.
# # https://cloud.google.com/vertex-ai/docs/release-notes#May_10_2023
# self.palm_chat = init_palm_chat("chat-bison@001")
# def to_gradio_chatbot(self):
# return self.conv.to_gradio_chatbot()
def dict(self):
base = {
"conv_id": self.conv_id,
"model_name": self.model_name,
"online_load": self.online_load
}
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_openai_compatible_models, add_chatgpt, add_claude, add_palm, post_load=False
):
if not post_load:
if controller_url:
ret = requests.post(controller_url + "/refresh_all_workers")
assert ret.status_code == 200
ret = requests.post(controller_url + "/list_models")
models = ret.json()["models"]
else:
models = []
# Add API providers
if register_openai_compatible_models:
global openai_compatible_models_info
openai_compatible_models_info = json.load(
open(register_openai_compatible_models)
)
models += list(openai_compatible_models_info.keys())
if add_chatgpt:
models += ["gpt-3.5-turbo", "gpt-3.5-turbo-1106"]
if add_claude:
models += ["claude-2.0", "claude-2.1", "claude-instant-1"]
if add_palm:
models += ["palm-2"]
models = list(set(models))
if "deluxe-chat-v1" in models:
del models[models.index("deluxe-chat-v1")]
if "deluxe-chat-v1.1" in models:
del models[models.index("deluxe-chat-v1.1")]
priority = {k: f"___{i:02d}" for i, k in enumerate(model_info)}
models.sort(key=lambda x: priority.get(x, x))
else:
models = ['imagenhub_SD', 'imagenhub_SDXL', 'imagenhub_SSD']
logger.info(f"Models: {models}")
return models
def load_demo_single(models, url_params):
logger.info("load_demo_single")
selected_model = models[0] if len(models) > 0 else ""
logger.info(f"url_params: {url_params}")
if "model" in url_params:
model = url_params["model"]
if model in models:
selected_model = model
logger.info(f"selected_model: {selected_model}")
dropdown_update = gr.Dropdown.update(
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}")
ip_expiration_dict[ip] = time.time() + SESSION_EXPIRATION_TIME
if args.model_list_mode == "reload":
models = get_model_list(
controller_url,
args.register_openai_compatible_models,
args.add_chatgpt,
args.add_claude,
args.add_palm,
)
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(), "") + (disable_btn,) * 5
def regenerate(state, request: gr.Request):
ip = get_ip(request)
logger.info(f"regenerate. ip: {ip}")
state.conv.update_last_message(None)
return (state, gr.Image(), "") + (disable_btn,) * 5
# return (state, gr.Video(), "") + (disable_btn,) * 5
def clear_history(request: gr.Request):
ip = get_ip(request)
logger.info(f"clear_history. ip: {ip}")
state = None
return (state, gr.Image(), "") + (disable_btn,) * 5
# return (state, gr.Video(), "") + (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 add_text(state, model_selector, text, request: gr.Request):
ip = get_ip(request)
logger.info(f"add_text. ip: {ip}. len: {len(text)}")
if state is None:
state = ImageState(model_selector)
# add for online
state.online_load = False
if len(text) <= 0:
state.skip_next = True
return (state, "", "") + (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
conv = state.conv
if (len(conv.messages) - conv.offset) // 2 >= CONVERSATION_TURN_LIMIT:
logger.info(f"conversation turn limit. ip: {ip}. text: {text}")
state.skip_next = True
return (state, "", CONVERSATION_LIMIT_MSG) + (
no_change_btn,
) * 5
text = text[:INPUT_CHAR_LEN_LIMIT] # Hard cut-off
conv.append_message(conv.roles[0], text)
# conv.append_message(conv.roles[1], None)
return (state, gr.Image(), text) + (disable_btn,) * 5
# return (state, gr.Video(), text) + (disable_btn,) * 5
# def add_text(state, model_selector, text, request: gr.Request):
# ip = get_ip(request)
# logger.info(f"add_text. ip: {ip}. len: {len(text)}")
#
# if state is None:
# state = ImageState(model_selector)
#
# if len(text) <= 0:
# state.skip_next = True
# return (state, "", "") + (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(conv.messages) - 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
# conv.append_message(conv.roles[0], text)
# return (state, "", "") + (disable_btn,) * 5
def post_process_code(code):
sep = "\n```"
if sep in code:
blocks = code.split(sep)
if len(blocks) % 2 == 1:
for i in range(1, len(blocks), 2):
blocks[i] = blocks[i].replace("\\_", "_")
code = sep.join(blocks)
return code
def model_diffusion_worker_stream_iter(
model_name,
worker_addr,
prompt,
):
# Make requests
gen_params = {
"model": model_name,
"prompt": prompt
}
logger.info(f"==== request ====\n{gen_params}")
# Stream output
response = requests.post(
worker_addr + "/worker_generate_stream",
headers=headers,
json=gen_params,
stream=True,
timeout=WORKER_API_TIMEOUT,
)
logger.info(f"==== 2request2 ====\n{gen_params}")
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
if chunk:
data = json.loads(chunk.decode())
yield data
def model_worker_stream_iter(
conv,
model_name,
worker_addr,
prompt,
temperature,
repetition_penalty,
top_p,
max_new_tokens,
):
# 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}")
# 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 load_model_worker(model_path):
worker_id = str(uuid.uuid4())[:8]
controller_addr = "http://localhost:21001"
worker_addr = "http://localhost:21002"
model_names = ''
max_gpu_memory = ''
host = "localhost"
port = 21002
limit_worker_concurrency = 10
worker = ModelWorker(
controller_addr,
worker_addr,
worker_id,
model_path,
model_names,
limit_worker_concurrency,
no_register=False,
device='cuda',
num_gpus=1,
max_gpu_memory=max_gpu_memory
)
logger.info("before")
# uvicorn.run(app, host=host, port=port, log_level="info")
logger.info("after")
return
def diffusion_response(state, request: gr.Request):
ip = get_ip(request)
logger.info(f"diffusion_response. ip: {ip}")
start_tstamp = time.time()
conv, model_name = state.conv, state.model_name
prompt = conv.messages[0][1]
logger.info(f'prompt message: {prompt}')
if state.online_load:
load_model_worker(model_name)
ret = requests.post(
controller_url + "/get_worker_address", json={"model": model_name}
)
logger.info(f"ret: {ret}")
worker_addr = ret.json()["address"]
logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}")
# 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_diffusion_worker_stream_iter(
model_name,
worker_addr,
prompt
)
# try:
for i, data in enumerate(stream_iter):
error_code = data["error_code"]
logger.info(f"error_code: {error_code}")
if error_code == 0:
logger.info(f"yes")
# if data:
grey = np.array(data['text'])
logger.info(f"grey.shape: {grey.shape}")
# logger.info(f"grey: {grey[0]}")
# array_img = np.dstack((grey, grey, grey))
output = Image.fromarray(grey.astype(np.uint8))
logger.info(f"output.size {output.size}")
yield (state, output) + (disable_btn,) * 5
else:
output = data + f"\n\n(error_code: {data['error_code']})"
state.output = output
yield (state, output) + (
disable_btn,
disable_btn,
disable_btn,
enable_btn,
enable_btn,
)
return
# output = data
# if "vicuna" in model_name:
# output = post_process_code(output)
state.output = output
# yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
yield (state, output) + (disable_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})"
# # )
# logger.error(f"{SERVER_ERROR_MSG}\n\n"
# f"(error_code: {ErrorCode.GRADIO_REQUEST_ERROR}, {e})")
# yield (state, "") + (
# disable_btn,
# disable_btn,
# disable_btn,
# enable_btn,
# enable_btn,
# )
# return
# except Exception as e:
# logger.error(
# f"{SERVER_ERROR_MSG}\n\n"
# f"(error_code: {ErrorCode.GRADIO_STREAM_UNKNOWN_ERROR}, {e})"
# )
# yield (state, "") + (
# disable_btn,
# disable_btn,
# disable_btn,
# enable_btn,
# enable_btn,
# )
# return
finish_tstamp = time.time()
# logger.info(f"===output===: {output}")
with open(get_conv_log_filename(), "a") as fout:
data = {
"tstamp": round(finish_tstamp, 4),
"type": "chat",
"model": model_name,
"gen_params": {},
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"state": state.dict(),
"ip": get_ip(request),
}
fout.write(json.dumps(data) + "\n")
def bot_response(state, temperature, top_p, max_new_tokens, request: gr.Request):
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
conv, model_name = state.conv, state.model_name
if model_name in openai_compatible_models_info:
model_info = openai_compatible_models_info[model_name]
prompt = conv.to_openai_api_messages()
stream_iter = openai_api_stream_iter(
model_info["model_name"],
prompt,
temperature,
top_p,
max_new_tokens,
api_base=model_info["api_base"],
api_key=model_info["api_key"],
)
elif model_name in ["gpt-3.5-turbo", "gpt-4", "gpt-4-turbo", "gpt-3.5-turbo-1106"]:
# avoid conflict with Azure OpenAI
assert model_name not in openai_compatible_models_info
prompt = conv.to_openai_api_messages()
stream_iter = openai_api_stream_iter(
model_name, prompt, temperature, top_p, max_new_tokens
)
elif model_name in ANTHROPIC_MODEL_LIST:
prompt = conv.get_prompt()
stream_iter = anthropic_api_stream_iter(
model_name, prompt, temperature, top_p, max_new_tokens
)
elif model_name == "palm-2":
stream_iter = palm_api_stream_iter(
state.palm_chat, conv.messages[-2][1], temperature, top_p, max_new_tokens
)
else:
# 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,
)
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()
if "vicuna" in model_name:
output = post_process_code(output)
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}")
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),
}
fout.write(json.dumps(data) + "\n")
block_css = """
#notice_markdown {
font-size: 110%
}
#notice_markdown th {
display: none;
}
#notice_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#model_description_markdown {
font-size: 110%
}
#leaderboard_markdown {
font-size: 110%
}
#leaderboard_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_dataframe td {
line-height: 0.1em;
}
#about_markdown {
font-size: 110%
}
#ack_markdown {
font-size: 110%
}
#input_box textarea {
}
footer {
display:none !important
}
.image-container {
display: flex;
align-items: center;
padding: 1px;
}
.image-container img {
margin: 0 30px;
height: 30px;
max-height: 100%;
width: auto;
max-width: 30%;
}
.image-about img {
margin: 0 30px;
margin-top: 30px;
height: 60px;
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 = f"""
# 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 [VisualArena](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/), [Anyscale](https://www.anyscale.com/), [HuggingFace](https://huggingface.co/) for their generous sponsorship.
Learn more about partnership [here](https://lmsys.org/donations/).
"""
# state = gr.State()
gr.Markdown(about_markdown, elem_id="about_markdown")
# return [state]
def build_single_model_ui(models, add_promotion_links=False):
promotion = (
"""
- | [GitHub](https://github.com/TIGER-AI-Lab/ImagenHub) | [Paper](https://arxiv.org/abs/2310.01596) | [Dataset](https://huggingface.co/ImagenHub) | [Twitter](https://twitter.com/???) | [Discord](https://discord.gg/???) |
"""
if add_promotion_links
else ""
)
notice_markdown = f"""
# 🏔️ Chat with Image Generation Models
{promotion}
## 🤖 Choose any model to generate
"""
state = gr.State()
gr.Markdown(notice_markdown, elem_id="notice_markdown")
with gr.Box(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,
)
logger.info(f"model_selector: {model_selector}")
with gr.Row():
with gr.Accordion(
"🔍 Expand to see 20+ model descriptions",
open=False,
elem_id="model_description_accordion",
):
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,
# )
# 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():
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():
chatbot = gr.Image()
# chatbot = gr.Video()
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=1024,
# value=512,
# step=64,
# interactive=True,
# label="Max output tokens",
# )
if add_promotion_links:
gr.Markdown(acknowledgment_md, elem_id="ack_markdown")
# Register listeners
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] + btn_list).then(
# bot_response,
# [state, temperature, top_p, max_output_tokens],
# [state, chatbot] + btn_list,
# )
regenerate_btn.click(regenerate, state, [state, chatbot, textbox] + btn_list).then(
diffusion_response,
[state],
[state, chatbot] + btn_list,
)
clear_btn.click(clear_history, None, [state, chatbot, textbox] + btn_list)
model_selector.change(clear_history, None, [state, chatbot, textbox] + btn_list)
# textbox.submit(
# add_text, [state, model_selector, textbox], [state, chatbot, textbox] + btn_list
# ).then(
# bot_response,
# [state, temperature, top_p, max_output_tokens],
# [state, chatbot] + btn_list,
# )
textbox.submit(
add_text, [state, model_selector, textbox], [state, chatbot, textbox] + btn_list
).then(
diffusion_response,
[state],
[state, chatbot] + btn_list,
)
# send_btn.click(
# add_text,
# [state, model_selector, textbox],
# [state, chatbot, textbox] + 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],
[state, chatbot, textbox] + btn_list,
).then(
diffusion_response,
[state],
[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:
logger.info("begin")
logger.info(f"======models=====: {models}")
state, model_selector = build_single_model_ui(models, add_promotion_links=True)
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
url_params = gr.JSON(visible=True)
logger.info(f"======url_params=====: {url_params}")
demo.load(
load_demo,
[url_params],
[
state,
model_selector,
],
_js=load_js,
)
logger.info(f"======url_params=====: {url_params}")
logger.info("end")
return demo
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--host", type=str, default="localhost")
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(
"--add-chatgpt",
action="store_true",
help="Add OpenAI's ChatGPT models (gpt-3.5-turbo, gpt-4)",
)
parser.add_argument(
"--add-claude",
action="store_true",
help="Add Anthropic's Claude models (claude-2, claude-instant-1)",
)
parser.add_argument(
"--add-palm",
action="store_true",
help="Add Google's PaLM model (PaLM 2 for Chat: chat-bison@001)",
)
parser.add_argument(
"--register-openai-compatible-models",
type=str,
help="Register custom OpenAI API compatible models by loading them 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(
"--online-load",
action="store_true",
help="Whether to load the model online",
)
args = parser.parse_args()
logger.info(f"args: {args}")
# Set global variables
set_global_vars(args.controller_url, args.moderate)
models = get_model_list(
args.controller_url,
args.register_openai_compatible_models,
args.add_chatgpt,
args.add_claude,
args.add_palm,
post_load=args.online_load
)
# 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)
# concurrency_count=args.concurrency_count,
demo.queue(
concurrency_count=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,
)
