import argparse
import datetime
import json
import os
import time
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
import subprocess
import sys
import time
logger = build_logger("gradio_web_server", "gradio_web_server.log")
headers = {"User-Agent": "LLaVA Client"}
no_change_btn = gr.Button()
enable_btn = gr.Button(interactive=True)
disable_btn = gr.Button(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(visible=True)
if "model" in url_params:
model = url_params["model"]
if model in models:
dropdown_update = gr.Dropdown(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(
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, masked_image, 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][:3], image_process_mode)
state.skip_next = False
state.messages[-2] = [
state.messages[-2][0],
(state.messages[-2][1][0],masked_image, state.messages[-2][1][2], state.messages[-2][1][3]) # Create a new tuple with the updated image
]
return (state, state.to_gradio_chatbot(), "") + (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_wCLS(state, text, masked_image, image_process_mode, imagebox, request: gr.Request):
logger.info(f"add_text_withcls. ip: {request.client.host}. len: {len(text)}")
if len(text) <= 0 and masked_image is None and imagebox 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]
if imagebox is not None:
text = text[:1200]
if '<image>' not in text:
text = text + '\n<image>'
text = (text, masked_image, imagebox, image_process_mode)
state = default_conversation.copy()
state.append_message(state.roles[0], text)
state.append_message(state.roles[1], None)
state.skip_next = False
state.cls=True
return (state, state.to_gradio_chatbot(), "") + (disable_btn,) * 5
def add_text(state, text, masked_image, image_process_mode, imagebox, request: gr.Request):
logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")
if len(text) <= 0 and masked_image is None and imagebox 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]
if imagebox is not None:
text = text[:1200]
if '<image>' not in text:
text = text + '\n<image>'
text = (text, masked_image, imagebox, image_process_mode)
state = default_conversation.copy()
state.append_message(state.roles[0], text)
state.append_message(state.roles[1], None)
state.skip_next = False
state.cls=False
return (state, state.to_gradio_chatbot(), "") + (disable_btn,) * 5
def http_bot(state, model_selector, temperature, top_p, max_new_tokens, raw_tokens, request: gr.Request):
cls_flag = state.cls
print(f">>>>>>>>CLS_FLAG_{cls_flag}")
select_tokens = raw_tokens.strip('[]')
select_tokens = list(map(int, select_tokens.split()))
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 "mistral" in model_name.lower() or "mixtral" in model_name.lower():
if 'orca' in model_name.lower():
template_name = "mistral_orca"
elif 'hermes' in model_name.lower():
template_name = "chatml_direct"
else:
template_name = "mistral_instruct"
elif 'llava-v1.6-34b' in model_name.lower():
template_name = "chatml_direct"
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}',
"select_tokens":select_tokens,
"cls_flag":cls_flag,
}
logger.info(f"==== request ====\n{pload}")
state.cls=cls_flag
pload['images'] = state.get_images()
state.messages[-1][-1] = "▌"
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=20)
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 = ("""
# VisionZip: Longer is Better but Not Necessary in Vision Language Models
[[Code](https://github.com/dvlab-research/VisionZip)] [[Demo-Visualizer](http://202.104.135.156:11030)] [[Usage-Video](https://youtu.be/9GNIJy4U6-k?si=jcWIJ2O0IjB4aamm)] [[Intro-Video](https://youtu.be/sytaAzmxxpo?si=IieArmQ7YNf2dVyM)]
This demo allows users to manually select which visual tokens to send to the LLM to observe how different visual tokens impact the final response.
### Instructions:
1. Upload an image.
2. Select the visual tokens.
3. Generate the answer.
For a step-by-step guide, refer to the [Usage Video](https://youtu.be/9GNIJy4U6-k?si=jcWIJ2O0IjB4aamm).
""")
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 [License](https://github.com/dvlab-research/VisionZip/blob/main/LICENSE) of VisionZip, 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%);
}
"""
import gradio as gr
import numpy as np
# Function to capture coordinates of the drawing on the image
import numpy as np
from PIL import Image, ImageDraw
def create_mask(image, grid_vet):
if image is None:
return None
# Resize the image to 336x336
image = image.resize((336, 336))
# Create a transparent overlay
overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
draw = ImageDraw.Draw(overlay)
grid_size = 14
grid_count = 24
for i in range(grid_count):
for j in range(grid_count):
# Calculate the bounding box of each grid cell
left = j * grid_size
top = i * grid_size
right = left + grid_size
bottom = top + grid_size
# If the value in grid_vet is 0, draw a white mask with 70% transparency
if grid_vet[i][j] == 0:
draw.rectangle([left, top, right, bottom], fill=(255, 255, 255, 178)) # 70% transparency
# Composite the image with the overlay
final_image = Image.alpha_composite(image.convert('RGBA'), overlay)
# Convert back to RGB if needed (remove alpha channel)
return final_image.convert('RGB')
def capture_coordinates(image, drawing):
outputs = drawing['layers'][0][:, :, -1] # Alpha channel (transparency)
non_zero_pixels = np.argwhere(outputs > 0) # Non-transparent pixels
grid_size = 14
grid_count = 24
grid_vector = np.zeros((grid_count, grid_count), dtype=int)
for y, x in non_zero_pixels:
grid_x = x // grid_size
grid_y = y // grid_size
grid_vector[grid_y, grid_x] = 1
grid_vector_flat = grid_vector.flatten()
index = np.where(grid_vector_flat==1)[0]
final_image = create_mask(image,grid_vector)
return str(index),final_image
def calculate_dominant_tokens_192(image, model_selector,state):
token_num=192
model_name = model_selector
controller_url = args.controller_url
ret = requests.post(controller_url + "/get_worker_address",
json={"model": model_name})
worker_addr = ret.json()["address"]
pload = {
"images": [state.process_image(image, "Default")],
"token_num":token_num,
}
response = requests.post(worker_addr + "/worker_get_visonzip",json=pload, timeout=20)
select_idx = response.json()['token_idx'][0]
grid_count=24
grid_vector = np.zeros((grid_count, grid_count), dtype=int)
for idx in select_idx:
row = idx // grid_count
col = idx % grid_count
grid_vector[row, col] = 1
final_image = create_mask(image,grid_vector)
select_idx = np.array(select_idx)
return str(select_idx), final_image
def calculate_dominant_tokens_128(image, model_selector,state):
## Call the Model to get the visionzip
## use the index to get the grid vector
token_num=128
model_name = model_selector
controller_url = args.controller_url
ret = requests.post(controller_url + "/get_worker_address",
json={"model": model_name})
worker_addr = ret.json()["address"]
pload = {
"images": [state.process_image(image, "Default")],
"token_num":token_num,
}
response = requests.post(worker_addr + "/worker_get_visonzip",json=pload, timeout=20)
select_idx = response.json()['token_idx'][0]
grid_count=24
grid_vector = np.zeros((grid_count, grid_count), dtype=int)
for idx in select_idx:
row = idx // grid_count
col = idx % grid_count
grid_vector[row, col] = 1
final_image = create_mask(image,grid_vector)
select_idx = np.array(select_idx)
return str(select_idx), final_image
def calculate_dominant_tokens_64(image, model_selector,state):
## Call the Model to get the visionzip
## use the index to get the grid vector
token_num=64
model_name = model_selector
controller_url = args.controller_url
ret = requests.post(controller_url + "/get_worker_address",
json={"model": model_name})
worker_addr = ret.json()["address"]
pload = {
"images": [state.process_image(image, "Default")],
"token_num":token_num,
}
response = requests.post(worker_addr + "/worker_get_visonzip",json=pload, timeout=20)
select_idx = response.json()['token_idx'][0]
grid_count=24
grid_vector = np.zeros((grid_count, grid_count), dtype=int)
for idx in select_idx:
row = idx // grid_count
col = idx % grid_count
grid_vector[row, col] = 1
final_image = create_mask(image,grid_vector)
select_idx = np.array(select_idx)
return str(select_idx), final_image
from PIL import Image
# Function to resize the image to 336x336 and return it
def resize_image(image):
if image is None:
return None
return image.resize((336, 336))
def default_img(image):
grid_count = 24
grid_vector = np.zeros((grid_count, grid_count), dtype=int)
default_image = create_mask(image,grid_vector)
return default_image
def build_demo(embed_mode, cur_dir=None, concurrency_count=10):
models = get_model_list()
textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER (No CLS)", container=False)
with gr.Blocks(title="VisionZip", 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", label="Upload Image", interactive=True)
image_process_mode = gr.Radio(
["Crop", "Resize", "Pad", "Default"],
value="Default",
label="Preprocess for non-square image", visible=False)
sketchbox = gr.Sketchpad(
label="Select on the Image",
height=250,
brush=gr.Brush(
colors=["#FF0000", "#0000FF", "#00FF00", "#FFFF00"], # Red, Blue, Green, Yellow, Black
default_color="#FF0000",
color_mode="defaults" # Fixed color mode (can also be "dynamic" for multiple colors)
)
)
get_coordinates_btn = gr.Button(value="Get the Selected Tokens")
with gr.Row(): # Add this new row to hold both buttons side by side
get_dominant64_btn = gr.Button(value="Get 64 Dominant Tokens")
get_dominant128_btn = gr.Button(value="Get 128 Dominant Tokens")
get_dominant192_btn = gr.Button(value="Get 192 Dominant Tokens")
coordinates_output = gr.Textbox(label="Select Tokens Index", interactive=False)
# Add the new image output area
masked_image_output = gr.Image(type="pil", label="Selected Visual Tokens", interactive=False)
get_coordinates_btn.click(
capture_coordinates,
[imagebox, sketchbox],
[coordinates_output,masked_image_output]
)
get_dominant64_btn.click(
calculate_dominant_tokens_64,
[imagebox,model_selector,state],
[coordinates_output,masked_image_output]
)
get_dominant128_btn.click(
calculate_dominant_tokens_128,
[imagebox,model_selector,state],
[coordinates_output,masked_image_output]
)
get_dominant192_btn.click(
calculate_dominant_tokens_192,
[imagebox,model_selector,state],
[coordinates_output,masked_image_output]
)
# Link the uploaded image to the sketchbox with resizing
imagebox.change(fn=lambda img: resize_image(img), inputs=imagebox, outputs=sketchbox)
# imagebox.change(fn=lambda img: default_img(img), inputs=imagebox, outputs=masked_image_output)
imagebox.change(
fn=lambda img: [default_img(img), ""] , # Reset coordinates_output to empty string
inputs=imagebox,
outputs=[masked_image_output, coordinates_output] # Include coordinates_output in outputs
)
# Example input examples
if cur_dir is None:
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=650,
layout="panel",
)
with gr.Row():
with gr.Column(scale=7):
textbox.render()
with gr.Column(scale=1, min_width=50):
CLS_btn = gr.Button(value="Add CLS", variant="primary")
with gr.Column(scale=1, min_width=50):
submit_btn = gr.Button(value="No CLS", 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)
# 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, masked_image_output, image_process_mode], # No need for imagebox here, you already have masked_image_output
[state, chatbot, textbox] + btn_list # Use masked_image_output in the outputs
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens, coordinates_output],
[state, chatbot] + btn_list,
concurrency_limit=concurrency_count
)
clear_btn.click(
clear_history,
None,
[state, chatbot, textbox, imagebox] + btn_list,
queue=False
)
textbox.submit(
add_text,
[state, textbox, masked_image_output, image_process_mode, imagebox],
[state, chatbot, textbox] + btn_list,
queue=False
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens, coordinates_output],
[state, chatbot] + btn_list,
concurrency_limit=concurrency_count
)
submit_btn.click(
add_text,
[state, textbox, masked_image_output, image_process_mode, imagebox],
[state, chatbot, textbox] + btn_list
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens, coordinates_output],
[state, chatbot] + btn_list,
concurrency_limit=concurrency_count
)
CLS_btn.click(
add_text_wCLS,
[state, textbox, masked_image_output, image_process_mode, imagebox],
[state, chatbot, textbox] + btn_list
).then(
http_bot,
[state, model_selector, temperature, top_p, max_output_tokens, coordinates_output],
[state, chatbot] + btn_list,
concurrency_limit=concurrency_count
)
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],
queue=False
)
else:
raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
return demo
def start_demo(args):
demo = build_demo(args.embed)
demo.queue(
status_update_rate=10, api_open=False
).launch(server_name=args.host, server_port=args.port, share=args.share)
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():
return subprocess.Popen(['python', '-m', 'llava.serve.model_worker', '--host', '0.0.0.0', '--controller', 'http://localhost:10000', '--model-path', 'liuhaotian/llava-v1.5-7b'])
def download_llava():
command = ['huggingface-cli', 'download', '--resume-download', 'liuhaotian/llava-v1.5-7b']
# Capture the output and errors
result = subprocess.run(command, capture_output=True, text=True)
# Print output and error (if any)
print("STDOUT:", result.stdout)
print("STDERR:", result.stderr)
# Check if the command was successful (exit code 0 means success)
if result.returncode == 0:
print("Download completed successfully.")
else:
print("Download failed.")
def download_clip():
command = ['huggingface-cli', 'download', '--resume-download', 'openai/clip-vit-large-patch14-336']
# Capture the output and errors
result = subprocess.run(command, capture_output=True, text=True)
# Print output and error (if any)
print("STDOUT:", result.stdout)
print("STDERR:", result.stderr)
# Check if the command was successful (exit code 0 means success)
if result.returncode == 0:
print("Download completed successfully.")
else:
print("Download failed.")
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=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()
logger.info(f"args: {args}")
download_clip()
download_llava()
controller_proc = start_controller()
worker_proc = start_worker()
time.sleep(100)
try:
start_demo(args)
except Exception as e:
print(e)
exit_status = 1
finally:
worker_proc.kill()
controller_proc.kill()
sys.exit(exit_status)