diff --git a/fastchat/serve/gradio_web_server_multi.py b/fastchat/serve/gradio_web_server_multi.py
index 3d3ffc09c..f3abd02d6 100644
--- a/fastchat/serve/gradio_web_server_multi.py
+++ b/fastchat/serve/gradio_web_server_multi.py
@@ -7,8 +7,6 @@
import pickle
import time
from typing import List
-import plotly.express as px
-
import gradio as gr
from fastchat.serve.gradio_block_arena_anony import (
@@ -92,33 +90,25 @@ def build_visualizer():
with gr.Tab("Price Analysis", id=1):
price_markdown = """
- ## *Price Analysis Visualizations*
- Below is a scatterplot depicting a model’s arena score against its cost effectiveness. Start exploring and discover some interesting trends in the data!
- """
- gr.Markdown(price_markdown)
- model_keys = ['chatgpt-4o-latest', 'gemini-1.5-pro-exp-0827','gpt-4o-mini-2024-07-18','claude-3-5-sonnet-20240620','gemini-1.5-flash-exp-0827','llama-3.1-405b-instruct','gemini-1.5-pro-api-0514','mistral-large-2407','reka-core-20240722','gemini-1.5-flash-api-0514', 'deepseek-coder-v2-0724','yi-large','llama-3-70b-instruct','qwen2-72b-instruct','claude-3-haiku-20240307','llama-3.1-8b-instruct','mistral-large-2402','command-r','mixtral-8x22b-instruct-v0.1','gpt-3.5-turbo-0613']
- output_tokens_per_USD = [66.66666667000001,200.0,1666.666667,66.66666667000001,3333.333333,333.3333333,200.0,166.6666667,166.6666667,3333.333333,3333.333333,333.3333333,1265.8227849999998,1111.111111,800.0,11111.11111,166.6666667,666.6666667,166.6666667,500.0]
- score=[1316.1559008799543,1300.8583398843484,1273.6004783067303,1270.113546648134,1270.530573909608,1266.244657076764,1259.2844314017723,1249.8268751367714,1229.2148108171098,1226.8769924152105,1214.5634252743123,1212.4668382698005,1206.3236747009742,1186.7832147344182,1178.5484948812955,1167.8793593807711,1157.271872307139,1148.6665817312062,1147.0325504217642,1117.0289441863001]
- fig = px.scatter(x=output_tokens_per_USD, y=score, title="Quality vs. Cost Effectiveness", labels={
- "output_tokens_per_USD": "# of output tokens per USD (in thousands)",
- "score": "Arena Score"}, log_x=True, text=model_keys)
- fig.update_traces(
- textposition="bottom center",
- textfont=dict(size=16),
- texttemplate='%{text}',
- marker=dict(size=8),
- hovertemplate=(
- 'Model: %{text}
' # Show the model name
- 'Output Tokens Per USD: %{x}
' # Show the x value (Output Price)
- 'Arena Score: %{y}
' # Show the y value (Arena Score)
- )
- )
- fig.update_xaxes(range=[1,4.5])
- fig.update_yaxes(range=[1100,1320])
- fig.update_layout(autosize=True, height=850, width=None, xaxis_title="# of output tokens per USD (in thousands)", yaxis_title= "Arena Score")
+ ## *Welcome to the Price Explorer*
+ This scatterplot displays a selection of the arena's models, showing their scores plotted against their cost-effectiveness. Using the plot, you can easily explore the model's price and compare it with their arena score.
+ ### How to Use:
+ - Hover Over Points: View the model's price, arena score, and organization.
+ - Click to Explore:
+ - Double-click a legend point to show only that organization's models on the scatterplot.
+ - Single-click a legend point to hide that organization's models from the scatterplot.
+
+ Start exploring and discover interesting trends in the data!
+ """
- gr.Plot(fig, elem_id="plotly-graph")
+ gr.Markdown(price_markdown)
+ frame = """
+
+ """
+ gr.HTML(frame)