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"""
"""
import gradio
import config
from app_util import *
system_list = [
"You are a helpful assistant.",
"你是一个导游。",
"你是一个英语老师。",
"你是一个程序员。",
"你是一个心理咨询师。",
]
user_simulator_doc = """\
There are maily two types of user simulator:
- prompt-based user-simulator (role-play)
- model-based user-simulator
In most cases, large language models (LLMs) are used to serve as assistant generator.
Besides, it can also used as user simulator.
"""
with gr.Blocks() as demo:
# Knowledge Distillation through Self Chatting
gr.HTML("""<h1 align="center">Distilling the Knowledge through Self Chatting</h1>""")
with gr.Row():
with gr.Column(scale=5):
system = gr.Dropdown(
choices=system_list,
value=system_list[0],
allow_custom_value=True,
interactive=True,
label="System message",
scale=5,
)
chatbot = gr.Chatbot(show_copy_button=True,
show_share_button=True,
avatar_images=("assets/man.png", "assets/bot.png"))
with gradio.Tab("Self Chat"):
generated_text_1 = gr.Textbox(show_label=False, placeholder="...", lines=10, visible=False)
generate_btn = gr.Button("🤔️ Self-Chat", variant="primary")
with gr.Row():
retry_btn = gr.Button("🔄 Retry", variant="secondary", size="sm", )
undo_btn = gr.Button("↩️ Undo", variant="secondary", size="sm", )
clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="sm", ) # 🧹 Clear History (清除历史)
# stop_btn = gr.Button("停止生成", variant="stop", visible=False)
gr.Markdown(
"Self-chat is a demo, which makes the model talk to itself. "
"It is based on user simulator and response generator.",
visible=True)
with gradio.Tab("Response Generator"):
with gr.Row():
generated_text_2 = gr.Textbox(show_label=False, placeholder="Please type your input", scale=7)
generate_btn_2 = gr.Button("Send", variant="primary")
with gr.Row():
retry_btn_2 = gr.Button("🔄 Regenerate", variant="secondary", size="sm", )
undo_btn_2 = gr.Button("↩️ Undo", variant="secondary", size="sm", )
clear_btn_2 = gr.Button("🗑️ Clear", variant="secondary", size="sm", ) # 🧹 Clear History (清除历史)
gr.Markdown("Response simulator is the most commonly used chatbot.")
with gradio.Tab("User Simulator"):
with gr.Row():
generated_text_3 = gr.Textbox(show_label=False, placeholder="Please type your response", scale=7)
generate_btn_3 = gr.Button("Send", variant="primary")
with gr.Row():
retry_btn_3 = gr.Button("🔄 Regenerate", variant="secondary", size="sm", )
undo_btn_3 = gr.Button("↩️ Undo", variant="secondary", size="sm", )
clear_btn_3 = gr.Button("🗑️ Clear", variant="secondary", size="sm", ) # 🧹 Clear History (清除历史)
gr.Markdown(user_simulator_doc)
with gr.Column(variant="compact"):
# with gr.Column():
model = gr.Dropdown(
["Qwen2-0.5B-Instruct", "llama3.1", "gemini"],
value="Qwen2-0.5B-Instruct",
label="Model",
interactive=True,
# visible=False
)
with gr.Accordion(label="Parameters", open=True):
slider_max_tokens = gr.Slider(minimum=1, maximum=config.MAX_SEQUENCE_LENGTH,
value=config.DEFAULT_MAX_TOKENS, step=1, label="Max tokens")
slider_temperature = gr.Slider(minimum=0.1, maximum=10.0,
value=config.DEFAULT_TEMPERATURE, step=0.1, label="Temperature",
info="Larger temperature increase the randomness")
slider_top_p = gr.Slider(
minimum=0.1,
maximum=1.0,
value=config.DEFAULT_TOP_P,
step=0.05,
label="Top-p (nucleus sampling)",
)
slider_top_k = gr.Slider(
minimum=1,
maximum=200,
value=config.DEFAULT_TOP_K,
step=1,
label="Top-k",
)
########
history = gr.State([{"role": "system", "content": system_list[0]}]) # 有用信息只有个system，其他和chatbot内容重叠
system.change(reset_state, inputs=[system], outputs=[chatbot, history])
clear_btn.click(reset_state, inputs=[system], outputs=[chatbot, history])
generate_btn.click(generate, [chatbot, history], outputs=[generated_text_1, chatbot, history],
show_progress="full")
retry_btn.click(undo_generate, [chatbot, history], outputs=[generated_text_1, chatbot, history]) \
.then(generate, [chatbot, history], outputs=[generated_text_1, chatbot, history],
show_progress="full")
undo_btn.click(undo_generate, [chatbot, history], outputs=[generated_text_1, chatbot, history])
slider_max_tokens.change(set_max_tokens, inputs=[slider_max_tokens])
slider_temperature.change(set_temperature, inputs=[slider_temperature])
slider_top_p.change(set_top_p, inputs=[slider_top_p])
slider_top_k.change(set_top_k, inputs=[slider_top_k])
# demo.queue().launch(share=False, server_name="0.0.0.0")
# demo.queue().launch(concurrency_count=1, max_size=5)
demo.queue().launch()
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