import argparse
import datetime
import hashlib
import json
import os
import subprocess
import sys
import time
import gradio as gr
import requests
from llava.constants import LOGDIR
from llava.conversation import SeparatorStyle, conv_templates, default_conversation
from llava.utils import (
build_logger,
moderation_msg,
server_error_msg,
violates_moderation,
)
logger = build_logger("gradio_web_server", "gradio_web_server.log")
headers = {"User-Agent": "LLaVA Client"}
no_change_btn = gr.Button.update()
enable_btn = gr.Button.update(interactive=True)
disable_btn = gr.Button.update(interactive=False)
priority = {
"vicuna-13b": "aaaaaaa",
"koala-13b": "aaaaaab",
}
def get_conv_log_filename():
t = datetime.datetime.now()
name = os.path.join('LLaVA_v1/logs', 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.sort(key=lambda x: priority.get(x, x))
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_dummy_fn(tmp, index_state, data: gr.LikeData):
value_new = data.value
index_new = data.index
if len(index_state) == 0 :
index_state.append(index_new)
else:
if index_new in index_state:
gr.Warning('Your feedback is already saved.')
return index_state
else:
index_state.append(index_new)
#return str(data.value) + ";" + str(data.index)+";"+ str(data.liked)+";"+str(index_state), index_state
return index_state
#chatbot.like(vote_last_response, [model_selector, index_state], [index_state])
def vote_last_response(chatbot, model_selector, index_state, request: gr.Request, data: gr.LikeData):
vote_type = "upvote" if data.liked else "downvote"
vote_value = data.value
vote_index = data.index
if len(index_state) == 0 :
index_state.append(vote_index)
else:
if vote_index in index_state:
gr.Warning('Your feedback is already saved.')
return index_state
else:
index_state.append(index_new)
with open(get_conv_log_filename(), "a") as fout:
data = {
"tstamp": round(time.time(), 4),
"type": vote_type,
"model": model_selector,
"state": chatbot[vote_index[0]],
"ip": request.client.host,
}
logger.info(f"^^^^^ data is - {data}")
fout.write(json.dumps(data) + "\n")
return index_state
def vote_last_response_old(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,
}
#print(f"^^^^^ data is - {data}")
logger.info(f"^^^^^ data is - {data}")
fout.write(json.dumps(data) + "\n")
def upvote_last_response(state, model_selector, request: gr.Request):
print()
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, temperature, top_p, 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
if "llava" in model_name.lower():
if "llama-2" in model_name.lower():
template_name = "llava_llama_2"
elif "v1" in model_name.lower():
if "mmtag" in model_name.lower():
template_name = "v1_mmtag"
elif (
"plain" in model_name.lower()
and "finetune" not in model_name.lower()
):
template_name = "v1_mmtag"
else:
template_name = "llava_v1"
elif "mpt" in model_name.lower():
template_name = "mpt"
else:
if "mmtag" in model_name.lower():
template_name = "v0_mmtag"
elif (
"plain" in model_name.lower()
and "finetune" not in model_name.lower()
):
template_name = "v0_mmtag"
else:
template_name = "llava_v0"
elif "mpt" in model_name:
template_name = "mpt_text"
elif "llama-2" in model_name:
template_name = "llama_2"
else:
template_name = "vicuna_v1"
new_state = conv_templates[template_name].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, 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"{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,
"temperature": float(temperature),
"top_p": float(top_p),
"max_new_tokens": min(int(max_new_tokens), 1536),
"stop": state.sep
if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT]
else state.sep2,
"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] = "â"
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 + "â"
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 as e:
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(start_tstamp, 4),
"state": state.dict(),
"images": all_image_hash,
"ip": request.client.host,
}
fout.write(json.dumps(data) + "\n")
title_markdown = """
# đ LLaVA: Large Language and Vision Assistant
[[Project Page]](https://llava-vl.github.io) [[Paper]](https://arxiv.org/abs/2304.08485) [[Code]](https://github.com/haotian-liu/LLaVA) [[Model]](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)
ONLY WORKS WITH GPU!
You can load the model with 8-bit or 4-bit quantization to make it fit in smaller hardwares. Setting the environment variable `bits` to control the quantization.
Recommended configurations:
| Hardware | A10G-Large (24G) | T4-Medium (15G) | A100-Large (40G) |
|-------------------|------------------|-----------------|------------------|
| **Bits** | 8 (default) | 4 | 16 |
"""
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.
"""
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):
models = get_model_list()
textbox = gr.Textbox(
show_label=False, placeholder="Enter text and press ENTER", container=False
)
with gr.Blocks(title="LLaVA", theme=gr.themes.Default(), css=block_css) as demo:
state = gr.State(default_conversation.copy())
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/extreme_ironing.jpg",
"What is unusual about this image?",
],
[
f"{cur_dir}/examples/waterview.jpg",
"What are the things I should be cautious about when I visit here?",
],
],
inputs=[imagebox, textbox],
)
with gr.Accordion("Parameters", open=False) as parameter_row:
temperature = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.2,
step=0.1,
interactive=True,
label="Temperature",
)
top_p = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.7,
step=0.1,
interactive=True,
label="Top P",
)
max_output_tokens = gr.Slider(
minimum=0,
maximum=1024,
value=512,
step=64,
interactive=True,
label="Max output tokens",
)
with gr.Column(scale=8):
chatbot = gr.Chatbot(
elem_id="chatbot", label="LLaVA 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="Send", variant="primary")
with gr.Row(elem_id="buttons") as button_row:
upvote_btn = gr.Button(value="đ Upvote", interactive=False)
downvote_btn = gr.Button(value="đ Downvote", interactive=False)
flag_btn = gr.Button(value="â ī¸ Flag", interactive=False)
# stop_btn = gr.Button(value="âšī¸ Stop Generation", interactive=False)
regenerate_btn = gr.Button(value="đ Regenerate", interactive=False)
clear_btn = gr.Button(value="đī¸ Clear history", 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 = [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, image_process_mode],
[state, chatbot, textbox, imagebox] + btn_list,
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens],
[state, chatbot] + btn_list,
)
clear_btn.click(
clear_history, None, [state, chatbot, textbox, imagebox] + btn_list
)
textbox.submit(
add_text,
[state, textbox, imagebox, image_process_mode],
[state, chatbot, textbox, imagebox] + btn_list,
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens],
[state, chatbot] + btn_list,
)
submit_btn.click(
add_text,
[state, textbox, imagebox, image_process_mode],
[state, chatbot, textbox, imagebox] + btn_list,
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens],
[state, chatbot] + btn_list,
)
# For adding voting option to chatbot
chatbot.like(vote_last_response, [chatbot, model_selector, index_state], [index_state])
#def vote_last_response(chatbot, model_selector, index_state, request: gr.Request, data: gr.LikeData):
#state, model_selector, request: gr.Request
if args.model_list_mode == "once":
demo.load(
load_demo,
[url_params],
[state, model_selector],
_js=get_window_url_params,
)
elif args.model_list_mode == "reload":
demo.load(load_demo_refresh_model_list, None, [state, model_selector])
else:
raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
return demo
def start_controller():
logger.info("Starting the controller")
controller_command = [
"python",
"-m",
"llava.serve.controller",
"--host",
"0.0.0.0",
"--port",
"10000",
]
return subprocess.Popen(controller_command)
def start_worker(model_path: str, bits=16):
logger.info(f"Starting the model worker for the model {model_path}")
model_name = model_path.strip('/').split('/')[-1]
assert bits in [4, 8, 16], "It can be only loaded with 16-bit, 8-bit, and 4-bit."
if bits != 16:
model_name += f'-{bits}bit'
worker_command = [
"python",
"-m",
"llava.serve.model_worker",
"--host",
"0.0.0.0",
"--controller",
"http://localhost:10000",
"--model-path",
model_path,
"--model-name",
model_name,
]
if bits != 16:
worker_command += [f'--load-{bits}bit']
return subprocess.Popen(worker_command)
def preload_models(model_path: str):
import torch
from llava.model import LlavaLlamaForCausalLM
model = LlavaLlamaForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, torch_dtype=torch.float16
)
vision_tower = model.get_vision_tower()
vision_tower.load_model()
del vision_tower
del model
def get_args():
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:10000")
parser.add_argument("--concurrency-count", type=int, default=8)
parser.add_argument(
"--model-list-mode", type=str, default="reload", 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()
return args
def start_demo(args):
demo = build_demo(args.embed)
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)
if __name__ == "__main__":
args = get_args()
logger.info(f"args: {args}")
model_path = "liuhaotian/llava-v1.5-13b"
bits = int(os.getenv("bits", 8))
preload_models(model_path)
controller_proc = start_controller()
worker_proc = start_worker(model_path, bits=bits)
# Wait for worker and controller to start
time.sleep(10)
try:
start_demo(args)
except Exception as e:
worker_proc.terminate()
controller_proc.terminate()
print(e)
sys.exit(1)