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ui.py
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from functools import partial
import datetime
import random
import time
import os
import uuid
import gradio as gr
from log_utils import build_logger, store_data_in_hub
LOGDIR = os.getenv("LOGDIR", "./MTEB-Arena-logs/vote_log")
DEFAULT_MODEL_A = "GritLM/GritLM-7B"
DEFAULT_MODEL_B = "BAAI/bge-large-en-v1.5"
info_txt = "🎉 Thanks for voting! Your vote shapes the leaderboard, please vote RESPONSIBLY."
# loggers for side-by-side and battle
retrieval_logger = build_logger("gradio_retrieval", "gradio_retrieval.log")
clustering_logger = build_logger("gradio_clustering", "gradio_clustering.log")
sts_logger = build_logger("gradio_sts", "gradio_sts.log")
def get_ip(request: gr.Request):
if request:
if "cf-connecting-ip" in request.headers:
ip = request.headers["cf-connecting-ip"] or request.client.host
else:
ip = request.client.host
else:
ip = ""
return ip
def clear_history(): return None, "", None
def clear_history_sts(): return None, "", "", "", None
def clear_history_clustering(): return None, "", 1, None
def clear_history_side_by_side(): return None, None, "", None, None
def clear_history_side_by_side_anon():
return None, None, "", None, None, gr.Markdown("", visible=False), gr.Markdown("", visible=False)
def clear_history_side_by_side_anon_sts():
return None, None, "", "", "", None, None, gr.Markdown("", visible=False), gr.Markdown("", visible=False)
def clear_history_side_by_side_anon_clustering():
return None, None, "", 1, None, None, gr.Markdown("", visible=False), gr.Markdown("", visible=False)
def disable_btns(i=6, visible=True): return (gr.update(interactive=False, visible=visible),) * i
def enable_btns(i=6, visible=True): return (gr.update(interactive=True, visible=visible),) * i
def enable_btns_clustering(state0):
if (hasattr(state0, "prompts")) and (len(state0.prompts) >= 3): return enable_btns(10)
return enable_btns(5) + disable_buttons_side_by_side(5)
def disable_buttons_side_by_side(i=6):
return tuple(gr.update(visible=i>=4, interactive=False) for i in range(i))
def vote_last_response(vote_type, state0, state1, model_selector0, model_selector1, request: gr.Request):
if vote_type != "share":
gr.Info(info_txt)
retrieval_logger.info(f"{vote_type} (named). ip: {get_ip(request)}")
data = {
"tstamp": round(time.time(), 4),
"task_type": "retrieval",
"type": vote_type,
"models": [model_selector0, model_selector1],
"ip": get_ip(request),
**state0.dict(prefix="0"),
**state1.dict(prefix="1")
}
# if models are anonymous, send to battle, otherwise side-by-side
if model_selector0 in ["", None] and model_selector1 in ["", None]:
store_data_in_hub(data, "retrieval_battle")
else:
store_data_in_hub(data, "retrieval_side_by_side")
if vote_type == "share": return
if model_selector0 == "":
return ("Press 🎲 New Round to start over 👇 (Note: Your vote shapes the leaderboard, please vote RESPONSIBLY!)",) + disable_btns(4) + (gr.Markdown(f"### Model A: {state0.model_name}", visible=True), gr.Markdown(f"### Model B: {state1.model_name}", visible=True))
return ("Press 🎲 New Round to start over 👇 (Note: Your vote shapes the leaderboard, please vote RESPONSIBLY!)",) + disable_btns(4) + (gr.Markdown(state0.model_name, visible=True), gr.Markdown(state1.model_name, visible=True))
def vote_last_response_sts(vote_type, state0, state1, model_selector0, model_selector1, request: gr.Request):
if vote_type != "share":
gr.Info(info_txt)
sts_logger.info(f"{vote_type} (named). ip: {get_ip(request)}")
data = {
"tstamp": round(time.time(), 4),
"task_type": "sts",
"type": vote_type,
"models": [model_selector0, model_selector1],
"ip": get_ip(request),
**state0.dict(prefix="0"),
**state1.dict(prefix="1")
}
# if models are anonymous, send to battle, otherwise side-by-side
if model_selector0 in ["", None] and model_selector1 in ["", None]:
store_data_in_hub(data, "sts_battle")
else:
store_data_in_hub(data, "sts_side_by_side")
if vote_type == "share": return
if model_selector0 == "":
return disable_btns(4) + (gr.Markdown(f"### Model A: {state0.model_name}", visible=True), gr.Markdown(f"### Model B: {state1.model_name}", visible=True))
return disable_btns(4) + (gr.Markdown(state0.model_name, visible=True), gr.Markdown(state1.model_name, visible=True))
def vote_last_response_clustering(vote_type, state0, state1, model_selector0, model_selector1, request: gr.Request):
if vote_type != "share":
gr.Info(info_txt)
clustering_logger.info(f"{vote_type} (named). ip: {get_ip(request)}")
data = {
"tstamp": round(time.time(), 4),
"task_type": "clustering",
"type": vote_type,
"models": [model_selector0, model_selector1],
"ip": get_ip(request),
**state0.dict(prefix="0"),
**state1.dict(prefix="1")
}
# if models are anonymous, send to battle, otherwise side-by-side
if model_selector0 in ["", None] and model_selector1 in ["", None]:
store_data_in_hub(data, "clustering_battle")
else:
store_data_in_hub(data, "clustering_side_by_side")
if vote_type == "share": return
if model_selector0 == "":
return disable_btns(5, visible=False) + disable_btns(4) + (gr.Markdown(f"### Model A: {state0.model_name}", visible=True), gr.Markdown(f"### Model B: {state1.model_name}", visible=True))
return disable_btns(5, visible=False) + disable_btns(4) + (gr.Markdown(state0.model_name, visible=True), gr.Markdown(state1.model_name, visible=True))
def vote_last_response_single(vote_type, state, model_selector, request: gr.Request):
gr.Info(info_txt)
retrieval_logger.info(f"{vote_type} (named). ip: {get_ip(request)}")
data = {
"tstamp": round(time.time(), 4),
"task_type": "retrieval",
"type": vote_type,
"models": model_selector,
"ip": get_ip(request),
**state.dict()
}
store_data_in_hub(data, "retrieval_single_choice")
return ("",) + disable_btns(3)
def vote_last_response_single_sts(vote_type, state, model_selector, request: gr.Request):
gr.Info(info_txt)
sts_logger.info(f"{vote_type} (named). ip: {get_ip(request)}")
data = {
"tstamp": round(time.time(), 4),
"task_type": "sts",
"type": vote_type,
"models": model_selector,
"ip": get_ip(request),
**state.dict()
}
store_data_in_hub(data, "sts_single_choice")
return disable_btns(3)
def vote_last_response_single_clustering(vote_type, state, model_selector, request: gr.Request):
gr.Info(info_txt)
clustering_logger.info(f"{vote_type} (named). ip: {get_ip(request)}")
data = {
"tstamp": round(time.time(), 4),
"task_type": "clustering",
"type": vote_type,
"models": model_selector,
"ip": get_ip(request),
**state.dict()
}
store_data_in_hub(data, "clustering_single_choice")
return disable_btns(3)
#return disable_btns(5, visible=False) + disable_btns(3)
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
class RetrievalState:
def __init__(self, model_name):
self.conv_id = uuid.uuid4().hex
self.model_name = model_name
self.prompt = ""
self.corpus = ""
self.output = ""
def dict(self, prefix: str = None):
if prefix is None:
return {"conv_id": self.conv_id, "model_name": self.model_name, "prompt": self.prompt, "output": self.output, "corpus": self.corpus}
else:
return {f"{prefix}_conv_id": self.conv_id, f"{prefix}_model_name": self.model_name, f"{prefix}_prompt": self.prompt, f"{prefix}_output": self.output, f"{prefix}_corpus": self.corpus}
def retrieve_side_by_side(gen_func, state0, state1, text, corpus, model_name0, model_name1, request: gr.Request):
if not text: raise gr.Warning("Query cannot be empty.")
state0, state1 = RetrievalState(model_name0), RetrievalState(model_name1)
ip = get_ip(request)
retrieval_logger.info(f"Retrieval. ip: {ip}")
start_tstamp = time.time()
retrieved_txt0, retrieved_txt1, model_name0, model_name1 = gen_func(text, corpus, model_name0, model_name1)
state0.prompt, state1.prompt = text, text
state0.corpus, state1.corpus = corpus, corpus
state0.output, state1.output = retrieved_txt0, retrieved_txt1
state0.model_name, state1.model_name = model_name0, model_name1
yield state0, state1, retrieved_txt0, retrieved_txt1, \
gr.Markdown(f"### Model A: {model_name0}", visible=False), gr.Markdown(f"### Model B: {model_name1}", visible=False)
finish_tstamp = time.time()
data = {
"tstamp": round(finish_tstamp, 4),
"task_type": "retrieval",
"type": "chat",
"model": model_name0,
"gen_params": {},
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"ip": get_ip(request),
**state0.dict()
}
store_data_in_hub(data, "retrieval_individual")
data = {
"tstamp": round(finish_tstamp, 4),
"task_type": "retrieval",
"type": "chat",
"model": model_name1,
"gen_params": {},
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"ip": get_ip(request),
**state1.dict()
}
store_data_in_hub(data, "retrieval_individual")
def retrieve(gen_func, state, text, corpus, model_name, request: gr.Request):
if not text: raise gr.Warning("Query cannot be empty.")
if not model_name: raise gr.Warning("Model name cannot be empty.")
state = RetrievalState(model_name)
ip = get_ip(request)
retrieval_logger.info(f"Retrieval. ip: {ip}")
start_tstamp = time.time()
retrieved_txt = gen_func(text, corpus, model_name)
state.prompt = text
state.corpus = corpus
state.output = retrieved_txt
state.model_name = model_name
yield state, retrieved_txt
finish_tstamp = time.time()
data = {
"tstamp": round(finish_tstamp, 4),
"task_type": "retrieval",
"type": "chat",
"model": model_name,
"gen_params": {},
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"ip": get_ip(request),
**state.dict()
}
store_data_in_hub(data, "retrieval_individual")
def check_input_retrieval(txt):
if not(txt): raise gr.Warning("Query cannot be empty.")
def build_side_by_side_ui_anon(models):
notice_markdown = """
# ⚔️ MTEB Arena: Retrieval 🔎
## 📜 Rules
- Send any query to two anonymous models and vote which retrieves the better document.
- You can choose a corpus for the models to retrieve from: [Wikipedia](https://huggingface.co/datasets/mteb/arena-wikipedia-7-15-24), [arXiv](https://huggingface.co/datasets/mteb/arena-arxiv-7-2-24) or [Stack Exchange](https://huggingface.co/datasets/mteb/arena-stackexchange).
- Stack Exchange is currently in beta testing with limited models.
## 👇 Vote now!
"""
state0 = gr.State()
state1 = gr.State()
gen_func = partial(retrieve_side_by_side, models.retrieve_parallel)
gr.Markdown(notice_markdown, elem_id="notice_markdown")
with gr.Group(elem_id="share-region-anon"):
with gr.Accordion("🔍 Expand to see all Arena players", open=False):
model_description_md = models.get_model_description_md()
gr.Markdown(model_description_md, elem_id="model_description_markdown")
with gr.Row():
with gr.Column():
chatbot_left = gr.Chatbot(
label="Model A",
elem_id="chatbot",
height=550,
show_copy_button=True,
)
with gr.Column():
chatbot_right = gr.Chatbot(
label="Model B",
elem_id="chatbot",
height=550,
show_copy_button=True,
)
with gr.Row():
with gr.Column():
model_selector_left = gr.Markdown("", visible=False)
with gr.Column():
model_selector_right = gr.Markdown("", visible=False)
with gr.Row():
leftvote_btn = gr.Button(
value="👈 A is better", visible=False, interactive=False
)
rightvote_btn = gr.Button(
value="👉 B is better", visible=False, interactive=False
)
tie_btn = gr.Button(value="🤝 Tie", visible=False, interactive=False)
bothbad_btn = gr.Button(
value="👎 Both are bad", visible=False, interactive=False
)
with gr.Row():
textbox = gr.Textbox(
label="Query",
show_label=True,
placeholder="👉 Enter text and press ENTER",
container=True,
elem_id="input_box",
)
corpus = gr.Dropdown(
label="Corpus",
choices=["wikipedia", "arxiv", "stackexchange"],
value="wikipedia",
interactive=True,
show_label=True,
container=True,
scale=0,
)
send_btn = gr.Button(value="Send", variant="primary", scale=0)
draw_btn = gr.Button(value="🎲 Random sample", variant="primary", scale=0)
with gr.Row():
clear_btn = gr.Button(value="🎲 New Round", interactive=False)
share_btn = gr.Button(value="📷 Share")
gr.Examples(
examples=[
["Which test was devised to determine whether robots can think?", "wikipedia"],
["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "arxiv"],
["What's the purpose of `if __name__ == '__main__'`?", "stackexchange"],
["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "wikipedia"],
["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "arxiv"],
["In English when to use –, — vs -?", "stackexchange"],
["Who wrote about a guide more popular than the Encyclopedia Galactica?", "wikipedia"],
["Please find me the paper on training code large language models to follow instructions via git commits.", "arxiv"],
["Why did the machines in The Matrix keep humans around?", "stackexchange"],
],
inputs=[textbox, corpus],
)
btn_list = [leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, clear_btn]
draw_btn.click(
models.retrieve_draw,
inputs=None,
outputs=[textbox, corpus],
api_name="draw_btn_anon"
)
textbox.submit(
check_input_retrieval,
inputs=textbox,
outputs=None,
).success(
partial(disable_buttons_side_by_side, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
).then(
gen_func,
inputs=[state0, state1, textbox, corpus, model_selector_left, model_selector_right],
outputs=[state0, state1, chatbot_left, chatbot_right, model_selector_left, model_selector_right],
api_name="submit_btn_anon"
).then(
enable_btns,
inputs=None,
outputs=btn_list
)
send_btn.click(
check_input_retrieval,
inputs=textbox,
outputs=None,
).success(
partial(disable_buttons_side_by_side, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
).then(
gen_func,
inputs=[state0, state1, textbox, corpus, model_selector_left, model_selector_right],
outputs=[state0, state1, chatbot_left, chatbot_right, model_selector_left, model_selector_right],
api_name="send_btn_anon"
).then(
enable_btns,
inputs=None,
outputs=btn_list
)
clear_btn.click(
clear_history_side_by_side_anon,
inputs=None,
outputs=[state0, state1, textbox, chatbot_left, chatbot_right, model_selector_left, model_selector_right],
api_name="clear_btn_anon"
).then(
disable_buttons_side_by_side,
inputs=None,
outputs=btn_list
).then(
partial(enable_btns, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
)
dummy_left_model = gr.State("")
dummy_right_model = gr.State("")
leftvote_btn.click(
partial(vote_last_response, "leftvote"),
inputs=[state0, state1, dummy_left_model, dummy_right_model],
outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, model_selector_left, model_selector_right]
)
rightvote_btn.click(
partial(vote_last_response, "rightvote"),
inputs=[state0, state1, dummy_left_model, dummy_right_model],
outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, model_selector_left, model_selector_right]
)
tie_btn.click(
partial(vote_last_response, "tievote"),
inputs=[state0, state1, dummy_left_model, dummy_right_model],
outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, model_selector_left, model_selector_right]
)
bothbad_btn.click(
partial(vote_last_response, "bothbadvote"),
inputs=[state0, state1, dummy_left_model, dummy_right_model],
outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, model_selector_left, model_selector_right]
)
share_js = """
function (a, b, c, d) {
const captureElement = document.querySelector('#share-region-anon');
html2canvas(captureElement)
.then(canvas => {
canvas.style.display = 'none'
document.body.appendChild(canvas)
return canvas
})
.then(canvas => {
const image = canvas.toDataURL('image/png')
const a = document.createElement('a')
a.setAttribute('download', 'mteb-arena.png')
a.setAttribute('href', image)
a.click()
canvas.remove()
});
return [a, b, c, d];
}
"""
share_btn.click(
partial(vote_last_response, "share"),
inputs=[state0, state1, model_selector_left, model_selector_right],
outputs=[],
js=share_js
)
def build_side_by_side_ui_named(models):
notice_markdown = """
# ⚔️ MTEB Arena: Retrieval 🔎
## 📜 Rules
- Send any query to two anonymous models and vote which retrieves the better document.
- You can choose a corpus for the models to retrieve from: [Wikipedia](https://huggingface.co/datasets/mteb/arena-wikipedia-7-15-24) or [arXiv](https://huggingface.co/datasets/mteb/arena-arxiv-7-2-24). ([Stack Exchange](https://huggingface.co/datasets/mteb/arena-stackexchange) coming soon!)
## 👇 Choose two models & vote now!
"""
model_list = models.models_retrieval
state0 = gr.State()
state1 = gr.State()
gen_func = partial(retrieve_side_by_side, models.retrieve_parallel)
gr.Markdown(notice_markdown, elem_id="notice_markdown")
with gr.Group(elem_id="share-region-named"):
with gr.Row():
with gr.Column():
model_selector_left = gr.Dropdown(
choices=model_list,
value=DEFAULT_MODEL_A,
interactive=True,
show_label=False,
container=False,
)
with gr.Column():
model_selector_right = gr.Dropdown(
choices=model_list,
value=DEFAULT_MODEL_B,
interactive=True,
show_label=False,
container=False,
)
with gr.Row():
with gr.Accordion("🔍 Expand to see all model descriptions", open=False):
model_description_md = models.get_model_description_md()
gr.Markdown(model_description_md, elem_id="model_description_markdown")
with gr.Row():
with gr.Column():
chatbot_left = gr.Chatbot(
label="Model A",
elem_id="chatbot",
height=550,
show_copy_button=True,
)
with gr.Column():
chatbot_right = gr.Chatbot(
label="Model B",
elem_id="chatbot",
height=550,
show_copy_button=True,
)
with gr.Row():
leftvote_btn = gr.Button(
value="👈 A is better", visible=False, interactive=False
)
rightvote_btn = gr.Button(
value="👉 B is better", visible=False, interactive=False
)
tie_btn = gr.Button(value="🤝 Tie", visible=False, interactive=False)
bothbad_btn = gr.Button(
value="👎 Both are bad", visible=False, interactive=False
)
with gr.Row():
textbox = gr.Textbox(
label="Query",
show_label=True,
placeholder="👉 Enter text and press ENTER",
container=True,
elem_id="input_box",
)
corpus = gr.Dropdown(
label="Corpus",
choices=["wikipedia", "arxiv"], # "stackexchange"
value="wikipedia",
interactive=True,
show_label=True,
container=True,
scale=0,
)
send_btn = gr.Button(value="Send", variant="primary", scale=0)
draw_btn = gr.Button(value="🎲 Random sample", variant="primary", scale=0)
with gr.Row():
clear_btn = gr.Button(value="🗑️ Clear history", interactive=False)
share_btn = gr.Button(value="📷 Share")
gr.Examples(
examples=[
["Which test was devised to determine whether robots can think?", "wikipedia"],
["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "arxiv"],
# ["What's the purpose of `if __name__ == '__main__'`?", "stackexchange"],
["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "wikipedia"],
["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "arxiv"],
# ["In English when to use –, — vs -?", "stackexchange"],
["Who wrote about a guide more popular than the Encyclopedia Galactica?", "wikipedia"],
["Please find me the paper on training code large language models to follow instructions via git commits.", "arxiv"],
# ["Why did the machines in The Matrix keep humans around?", "stackexchange"],
],
inputs=[textbox, corpus],
)
btn_list = [leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, clear_btn]
draw_btn.click(
partial(models.retrieve_draw, ["wikipedia", "arxiv"]),
inputs=None,
outputs=[textbox, corpus],
api_name="draw_btn_anon"
)
textbox.submit(
check_input_retrieval,
inputs=textbox,
outputs=None,
).success(
partial(disable_buttons_side_by_side, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
).then(
gen_func,
inputs=[state0, state1, textbox, corpus, model_selector_left, model_selector_right],
outputs=[state0, state1, chatbot_left, chatbot_right],
api_name="textbox_side_by_side"
).then(
enable_btns,
inputs=None,
outputs=btn_list
)
send_btn.click(
check_input_retrieval,
inputs=textbox,
outputs=None,
).success(
partial(disable_buttons_side_by_side, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
).then(
gen_func,
inputs=[state0, state1, textbox, corpus, model_selector_left, model_selector_right],
outputs=[state0, state1, chatbot_left, chatbot_right],
api_name="send_side_by_side"
).then(
enable_btns,
inputs=None,
outputs=btn_list
)
clear_btn.click(
clear_history_side_by_side,
inputs=None,
outputs=[state0, state1, textbox, chatbot_left, chatbot_right],
api_name="clear_btn_side_by_side"
).then(
disable_buttons_side_by_side,
inputs=None,
outputs=btn_list
).then(
partial(enable_btns, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
)
leftvote_btn.click(
partial(vote_last_response, "leftvote"),
inputs=[state0, state1, model_selector_left, model_selector_right],
outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, model_selector_left, model_selector_right]
)
rightvote_btn.click(
partial(vote_last_response, "rightvote"),
inputs=[state0, state1, model_selector_left, model_selector_right],
outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, model_selector_left, model_selector_right]
)
tie_btn.click(
partial(vote_last_response, "tievote"),
inputs=[state0, state1, model_selector_left, model_selector_right],
outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, model_selector_left, model_selector_right]
)
bothbad_btn.click(
partial(vote_last_response, "bothbadvote"),
inputs=[state0, state1, model_selector_left, model_selector_right],
outputs=[textbox, leftvote_btn, rightvote_btn, tie_btn, bothbad_btn, model_selector_left, model_selector_right]
)
share_js = """
function (a, b, c, d) {
const captureElement = document.querySelector('#share-region-named');
html2canvas(captureElement)
.then(canvas => {
canvas.style.display = 'none'
document.body.appendChild(canvas)
return canvas
})
.then(canvas => {
const image = canvas.toDataURL('image/png')
const a = document.createElement('a')
a.setAttribute('download', 'mteb-arena.png')
a.setAttribute('href', image)
a.click()
canvas.remove()
});
return [a, b, c, d];
}
"""
share_btn.click(
partial(vote_last_response, "share"),
inputs=[state0, state1, model_selector_left, model_selector_right],
outputs=[],
js=share_js
)
def build_single_model_ui(models):
notice_markdown = f"""
# 💧 MTEB Arena Single Model: Retrieval 🔎
"""
#| [GitHub](https://github.com/embeddings-benchmark) |
### 🤖 Choose any retriever
state = gr.State()
gen_func = partial(retrieve, models.retrieve)
gr.Markdown(notice_markdown, elem_id="notice_markdown")
model_list = models.models_retrieval
with gr.Row(elem_id="model_selector_row"):
model_selector = gr.Dropdown(
choices=model_list,
value=DEFAULT_MODEL_A,
interactive=True,
show_label=False
)
with gr.Row():
with gr.Accordion(
"🔍 Expand to see all model descriptions",
open=False,
elem_id="model_description_accordion",
):
model_description_md = models.get_model_description_md()
gr.Markdown(model_description_md, elem_id="model_description_markdown")
with gr.Row():
chatbot = gr.Chatbot(
label="Model",
elem_id="chatbot",
height=550,
show_copy_button=True,
)
with gr.Row():
textbox = gr.Textbox(
label="Query",
show_label=True,
placeholder="👉 Enter text and press ENTER",
container=True,
elem_id="input_box",
)
corpus = gr.Dropdown(
label="Corpus",
choices=["wikipedia", "arxiv"],#, "stackexchange"],
value="wikipedia",
interactive=True,
show_label=True,
container=True,
scale=0,
)
send_btn = gr.Button(value="Send", variant="primary", scale=0)
draw_btn = gr.Button(value="🎲 Random sample", variant="primary", scale=0)
with gr.Row() 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)
clear_btn = gr.Button(value="🗑️ Clear history", interactive=False)
gr.Examples(
examples=[
["Which test was devised to determine whether robots can think?", "wikipedia"],
["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "arxiv"],
# ["What's the purpose of `if __name__ == '__main__'`?", "stackexchange"],
["Find me a planet near α 23h 06m 29.368s and δ −05° 02′ 29.04″ that could be habitable.", "wikipedia"],
["I am searching for a good and large-scale benchmark for testing the performance of text embeddings.", "arxiv"],
# ["In English when to use –, — vs -?", "stackexchange"],
["Who wrote about a guide more popular than the Encyclopedia Galactica?", "wikipedia"],
["Please find me the paper on training code large language models to follow instructions via git commits.", "arxiv"],
# ["Why did the machines in The Matrix keep humans around?", "stackexchange"],
],
inputs=[textbox, corpus],
)
btn_list = [upvote_btn, downvote_btn, flag_btn, clear_btn]
draw_btn.click(
partial(models.retrieve_draw, ["wikipedia", "arxiv"]),
inputs=None,
outputs=[textbox, corpus],
api_name="draw_btn_single"
)
model_selector.change(
clear_history,
inputs=None,
outputs=[state, textbox, chatbot],
api_name="model_selector_single"
).then(
partial(disable_btns, 4),
inputs=None,
outputs=btn_list
).then(
partial(enable_btns, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
)
textbox.submit(
check_input_retrieval,
inputs=textbox,
outputs=None,
).success(
partial(disable_buttons_side_by_side, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
).then(
gen_func,
inputs=[state, textbox, corpus, model_selector],
outputs=[state, chatbot],
api_name="submit_btn_single",
show_progress = "full"
).success(
partial(enable_btns, 4),
inputs=None,
outputs=btn_list
)
send_btn.click(
check_input_retrieval,
inputs=textbox,
outputs=None,
).success(
partial(disable_buttons_side_by_side, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
).then(
gen_func,
inputs=[state, textbox, corpus, model_selector],
outputs=[state, chatbot],
api_name="send_btn_single",
show_progress = "full"
).success(
partial(enable_btns, 4),
inputs=None,
outputs=btn_list
)
upvote_btn.click(
partial(vote_last_response_single, "upvote"),
inputs=[state, model_selector],
outputs=[textbox, upvote_btn, downvote_btn, flag_btn]
)
downvote_btn.click(
partial(vote_last_response_single, "downvote"),
inputs=[state, model_selector],
outputs=[textbox, upvote_btn, downvote_btn, flag_btn]
)
flag_btn.click(
partial(vote_last_response_single, "flag"),
inputs=[state, model_selector],
outputs=[textbox, upvote_btn, downvote_btn, flag_btn]
)
clear_btn.click(
clear_history,
inputs=None,
outputs=[state, textbox, chatbot],
api_name="clear_history_single",
show_progress="full"
).then(
partial(disable_btns, 4),
inputs=None,
outputs=btn_list
).then(
partial(enable_btns, 4),
inputs=None,
outputs=[textbox, corpus, send_btn, draw_btn],
)
### Clustering ###
def check_input_clustering(state, txt):
if not(txt): raise gr.Warning("Text cannot be empty.")
if (state) and (hasattr(state, "prompts")) and (txt in state.prompts): raise gr.Warning("Text already added.")
# https://github.com/lm-sys/FastChat/blob/73936244535664c7e4c9bc1a419aa7f77b2da88e/fastchat/serve/gradio_web_server.py#L100
# https://github.com/lm-sys/FastChat/blob/73936244535664c7e4c9bc1a419aa7f77b2da88e/fastchat/serve/gradio_block_arena_named.py#L165
class ClusteringState:
def __init__(self, model_name):
self.conv_id = uuid.uuid4().hex
self.model_name = model_name
self.prompts = []
self.output = ""
self.ncluster = 1
self.ndim = "3D"
self.dim_method = "PCA"
self.clustering_method = "KMeans"
def dict(self, prefix: str = None):
if prefix is None:
return {"conv_id": self.conv_id, "model_name": self.model_name, "prompt": self.prompts, "ncluster": self.ncluster, "output": self.output, "ndim": self.ndim, "dim_method": self.dim_method, "clustering_method": self.clustering_method}
else:
return {f"{prefix}_conv_id": self.conv_id, f"{prefix}_model_name": self.model_name, f"{prefix}_prompt": self.prompts, f"{prefix}_ncluster": self.ncluster, f"{prefix}_output": self.output, f"{prefix}_ndim": self.ndim, f"{prefix}_dim_method": self.dim_method, f"{prefix}_clustering_method": self.clustering_method}
def clustering_side_by_side(gen_func, state0, state1, txt, ncluster, ndim, dim_method, clustering_method, model_name0, model_name1, request: gr.Request):
if state0 is None:
state0 = ClusteringState(model_name1)
if state1 is None:
state1 = ClusteringState(model_name0)
# txt may be None if only changing the dim
if txt:
if "<|SEP|>" in txt:
state0.prompts.extend(txt.split("<|SEP|>"))
state1.prompts.extend(txt.split("<|SEP|>"))
else:
state0.prompts.append(txt)
state1.prompts.append(txt)
state0.ncluster = ncluster
state1.ncluster = ncluster
state0.ndim = ndim
state1.ndim = ndim
state0.dim_method = dim_method
state1.dim_method = dim_method
state0.clustering_method = clustering_method
state1.clustering_method = clustering_method
ip = get_ip(request)
clustering_logger.info(f"Clustering. ip: {ip}")
start_tstamp = time.time()
# Remove prefixes in case it is already beyoned the 1st round.
model_name0, model_name1 = model_name0.replace("### Model A: ", ""), model_name1.replace("### Model B: ", "")
generated_image0, generated_image1, model_name0, model_name1 = gen_func(state0.prompts, model_name0, model_name1, ncluster, ndim=ndim.split(" ")[0], dim_method=dim_method, clustering_method=clustering_method)
state0.model_name, state1.model_name = model_name0, model_name1
yield state0, state1, generated_image0, generated_image1, None, \
gr.Markdown(f"### Model A: {model_name0}", visible=False), gr.Markdown(f"### Model B: {model_name1}", visible=False)
finish_tstamp = time.time()
data = {
"tstamp": round(finish_tstamp, 4),
"task_type": "clustering",
"type": "chat",
"model": model_name0,
"gen_params": {},
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"ip": get_ip(request),
**state0.dict()
}
store_data_in_hub(data, "clustering_individual")
data = {
"tstamp": round(finish_tstamp, 4),
"task_type": "clustering",
"type": "chat",
"model": model_name1,
"gen_params": {},
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"ip": get_ip(request),
**state1.dict()
}
store_data_in_hub(data, "clustering_individual")
def clustering(gen_func, state, txt, ncluster, ndim, dim_method, clustering_method, model_name, request: gr.Request):
if not model_name: raise gr.Warning("Model name cannot be empty.")
if state is None:
state = ClusteringState(model_name)
ip = get_ip(request)
clustering_logger.info(f"Clustering. ip: {ip}")
start_tstamp = time.time()
# txt may be None if only changing the dim
if txt:
if "<|SEP|>" in txt:
state.prompts.extend(txt.split("<|SEP|>"))
else:
state.prompts.append(txt)
state.ncluster = ncluster
generated_img = gen_func(state.prompts, model_name, state.ncluster, ndim=ndim.split(" ")[0], dim_method=dim_method, clustering_method=clustering_method)
state.model_name = model_name
yield state, generated_img, None
finish_tstamp = time.time()
data = {
"tstamp": round(finish_tstamp, 4),
"task_type": "clustering",
"type": "chat",
"model": model_name,
"gen_params": {},
"start": round(start_tstamp, 4),
"finish": round(finish_tstamp, 4),
"ip": get_ip(request),
**state.dict()
}
store_data_in_hub(data, "clustering_individual")
def toggle_btn(btn):
if btn == "3D (press for 2D)": return gr.update(value="2D (press for 3D)", variant="primary")
else: return gr.update(value="3D (press for 2D)", variant="primary")