update price scatterplot

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}<br>'  # Show the model name
-                'Output Tokens Per USD: %{x}<br>'  # Show the x value (Output Price)
-                'Arena Score: %{y}<br>'  # 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 = """
+                <iframe width="100%" scrolling="no" style="height: 1050px; border: 1px solid lightgrey; border-radius: 10px;" 
+                        src="https://storage.googleapis.com/public-arena-no-cors/2024-12-20-figure.html">
+                </iframe>
+            """
+            gr.HTML(frame)