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import argparse
import datetime
import json
import os
import time
from PIL import Image
import gradio as gr
import requests
from llava.conversation import (default_conversation, conv_templates,
SeparatorStyle)
from llava.constants import LOGDIR
from llava.utils import (build_logger, server_error_msg,
violates_moderation, moderation_msg)
import hashlib
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(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.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_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 batch_process_images(folder_path, textbox, model_selector, temperature, top_p, max_output_tokens, request: gr.Request):
print("calling batch_process_images")
for filename in os.listdir(folder_path):
if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):
image_path = os.path.join(folder_path, filename)
with Image.open(image_path) as image:
state = default_conversation.copy()
state, _, _, _, _, _, _, _, _ = add_text(state, textbox, image, "Default", request)
# Call http_bot and iterate over the generator
response_text = ""
for state_update in http_bot(state, model_selector, temperature, top_p, max_output_tokens, request):
# Update state and extract response text
state, chatbot_output, *_ = state_update
response_text = chatbot_output
# Save the final response to a file
try:
with open(os.path.splitext(image_path)[0] + '.txt', 'w') as f:
f.write(response_text[0][1])
except Exception as e:
print(f"An error occurred: {e}")
return "Batch processing completed."
def http_bot(state, model_selector, temperature, top_p, max_new_tokens, request: gr.Request):
logger.info(f"http_bot. ip: {request.client.host}")
print(f"model_selector {model_selector}")
start_tstamp = time.time()
model_name = model_selector
if state.skip_next:
# This generate call is skipped due to invalid inputs
print("invalid input state.skip_next")
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"
print(f"template_name {template_name}")
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
print(f"error No available worker")
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] = "▌"
yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
print(f"entering Stream output")
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 + "▌"
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(finish_tstamp, 4),
"state": state.dict(),
"images": all_image_hash,
"ip": request.client.host,
}
fout.write(json.dumps(data) + "\n")
title_markdown = ("""
Most Up To Date Scripts On : https://www.patreon.com/posts/sota-very-best-90744385 \n
Original Project : https://llava-vl.github.io
""")
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):
textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False)
# New components for batch processing
folder_input = gr.Textbox(label="Enter Folder Path for Batch Processing")
batch_btn = gr.Button("Batch Process")
with gr.Blocks(title="LLaVA", theme=gr.themes.Default(), css=block_css) 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/extreme_ironing.jpg", "just caption the image with details, colors, items, objects, emotions, art style, drawing style and objects but do not add any description or comment. do not miss any item in the given image"],
], 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="👍 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", interactive=False)
url_params = gr.JSON(visible=False)
# Add new components for batch processing
with gr.Row():
folder_input.render()
batch_btn.render()
# Batch processing button event
batch_btn.click(
batch_process_images, # This function needs to be defined to handle batch processing
inputs=[folder_input, textbox, model_selector , temperature, top_p, max_output_tokens],
outputs=[]
)
# 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],
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, temperature, top_p, 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, 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,
queue=False
).then(
http_bot,
[state, model_selector, temperature, top_p, 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:10000")
parser.add_argument("--concurrency-count", type=int, default=10)
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()
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
)