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"""
The gradio demo server for chatting with a large multimodal model.
Usage:
python3 -m fastchat.serve.controller
python3 -m fastchat.serve.sglang_worker --model-path liuhaotian/llava-v1.5-7b --tokenizer-path llava-hf/llava-1.5-7b-hf
python3 -m fastchat.serve.gradio_web_server_multi --share --multimodal
"""
import os
import gradio as gr
from fastchat.serve.gradio_web_server import (
upvote_last_response,
downvote_last_response,
flag_last_response,
get_model_description_md,
acknowledgment_md,
bot_response,
add_text,
clear_history,
regenerate,
)
from fastchat.utils import (
build_logger,
)
logger = build_logger("gradio_web_server_multi", "gradio_web_server_multi.log")
def build_single_vision_language_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) |
"""
if add_promotion_links
else ""
)
notice_markdown = f"""
# 🏔️ Chat with Open Large Vision-Language Models
{promotion}
"""
state = gr.State()
gr.Markdown(notice_markdown, elem_id="notice_markdown")
with gr.Group():
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.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")
with gr.Row():
with gr.Column(scale=3):
textbox = gr.Textbox(
show_label=False,
placeholder="👉 Enter your prompt and press ENTER",
container=False,
render=False,
elem_id="input_box",
)
imagebox = gr.Image(type="pil")
cur_dir = os.path.dirname(os.path.abspath(__file__))
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",
)
gr.Examples(
examples=[
[
f"{cur_dir}/example_images/city.jpeg",
"What is unusual about this image?",
],
[
f"{cur_dir}/example_images/fridge.jpeg",
"What is in this fridge?",
],
],
inputs=[imagebox, textbox],
)
with gr.Column(scale=8):
chatbot = gr.Chatbot(
elem_id="chatbot", label="Scroll down and start chatting", height=550
)
with gr.Row():
with gr.Column(scale=8):
textbox.render()
with gr.Column(scale=1, min_width=50):
send_btn = gr.Button(value="Send", variant="primary")
with gr.Row(elem_id="buttons"):
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)
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, 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]
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