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import gradio as gr
import pandas as pd
from tabs.trades import (
prepare_trades,
get_overall_trades,
get_overall_winning_trades,
plot_trades_by_week,
plot_winning_trades_by_week,
plot_trade_details
)
from tabs.tool_win import (
get_tool_winning_rate,
get_overall_winning_rate,
plot_winning_trades,
plot_winning_plot_by_tool
)
from tabs.error import (
get_error_data,
get_error_data_overall,
plot_error_data,
plot_tool_error_data,
plot_week_error_data
)
tools_df = pd.read_csv("./data/tools.csv")
trades_df = pd.read_csv("./data/all_trades_profitability.csv")
trades_df = prepare_trades(trades_df)
demo = gr.Blocks()
INC_TOOLS = [
'prediction-online',
'prediction-offline',
'claude-prediction-online',
'claude-prediction-offline',
'prediction-offline-sme',
'prediction-online-sme',
'prediction-request-rag',
'prediction-request-reasoning',
'prediction-url-cot-claude',
'prediction-request-rag-claude',
'prediction-request-reasoning-claude'
]
# TOOLS DATA
error_df = get_error_data(
tools_df=tools_df,
inc_tools=INC_TOOLS
)
error_overall_df = get_error_data_overall(
error_df=error_df
)
winning_rate_df = get_tool_winning_rate(
tools_df=tools_df,
inc_tools=INC_TOOLS
)
winning_rate_all_df = get_overall_winning_rate(
wins_df=winning_rate_df
)
trades_count_df = get_overall_trades(
trades_df=trades_df
)
trades_winning_rate_df = get_overall_winning_trades(
trades_df=trades_df
)
with demo:
gr.HTML("<h1>Olas Predict Actual Performance</h1>")
gr.Markdown("This app shows the actual performance of Olas Predict tools on the live market.")
with gr.Tabs():
with gr.TabItem("🔥Trades Dashboard"):
with gr.Row():
gr.Markdown("# Plot of number of trades by week")
with gr.Row():
plot_trades_by_week = plot_trades_by_week(
trades_df=trades_count_df
)
with gr.Row():
gr.Markdown("# Plot of winning trades by week")
with gr.Row():
plot_winning_trades_by_week = plot_winning_trades_by_week(
trades_df=trades_winning_rate_df
)
with gr.Row():
gr.Markdown("# Plot of trade details")
with gr.Row():
trade_details_selector = gr.Dropdown(
label="Select a trade",
choices=[
"mech calls",
"collateral amount",
"earnings",
"net earnings",
"ROI"
],
value="mech calls"
)
with gr.Row():
trade_details_plot = plot_trade_details(
trade_detail="mech calls",
trades_df=trades_df
)
def update_trade_details(trade_detail):
return plot_trade_details(
trade_detail=trade_detail,
trades_df=trades_df
)
trade_details_selector.change(
update_trade_details,
inputs=trade_details_selector,
outputs=trade_details_plot
)
with gr.Row():
trade_details_selector
with gr.Row():
trade_details_plot
with gr.TabItem("🚀 Tool Winning Dashboard"):
with gr.Row():
gr.Markdown("# Plot showing overall winning rate")
with gr.Row():
winning_selector = gr.Dropdown(
label="Select Metric",
choices=['losses', 'wins', 'total_request', 'win_perc'],
value='win_perc',
)
with gr.Row():
winning_plot = plot_winning_trades(
wins_df=winning_rate_all_df,
winning_selector="win_perc"
)
def update_winning_plot(winning_selector):
return plot_winning_trades(
wins_df=winning_rate_all_df,
winning_selector=winning_selector
)
winning_selector.change(
plot_winning_trades,
inputs=winning_selector,
outputs=winning_plot
)
with gr.Row():
winning_selector
with gr.Row():
winning_plot
with gr.Row():
gr.Markdown("# Plot showing winning rate by tool")
with gr.Row():
sel_tool = gr.Dropdown(
label="Select a tool",
choices=INC_TOOLS,
value=INC_TOOLS[0]
)
with gr.Row():
plot_tool_win_rate = plot_winning_plot_by_tool(
wins_df=winning_rate_df,
tool=INC_TOOLS[0]
)
def update_tool_win_plot(tool):
return plot_winning_plot_by_tool(
wins_df=winning_rate_df,
tool=tool
)
sel_tool.change(
update_tool_win_plot,
inputs=sel_tool,
outputs=plot_tool_win_rate
)
with gr.Row():
sel_tool
with gr.Row():
plot_tool_win_rate
with gr.TabItem("🏥 Tool Error Dashboard"):
with gr.Row():
gr.Markdown("# Plot showing overall error")
with gr.Row():
plot_error_data(
error_all_df=error_overall_df
)
with gr.Row():
gr.Markdown("# Plot showing error by tool")
with gr.Row():
sel_tool = gr.Dropdown(
label="Select a tool",
choices=INC_TOOLS,
value=INC_TOOLS[0]
)
with gr.Row():
plot_tool_error = plot_tool_error_data(
error_df=error_df,
tool=INC_TOOLS[0]
)
def update_tool_error_plot(tool):
return plot_tool_error_data(
error_df=error_df,
tool=tool
)
sel_tool.change(
update_tool_error_plot,
inputs=sel_tool,
outputs=plot_tool_error
)
with gr.Row():
sel_tool
with gr.Row():
plot_tool_error
with gr.Row():
gr.Markdown("# Plot showing error by week")
with gr.Row():
choices = error_overall_df['request_month_year_week'].unique().tolist()
# sort the choices by the latest week to be on the top
choices = sorted(choices)
sel_week = gr.Dropdown(
label="Select a week",
choices=choices,
value=choices[-1]
)
with gr.Row():
plot_week_error = plot_week_error_data(
error_df=error_df,
week=choices[-1]
)
def update_week_error_plot(selected_week):
return plot_week_error_data(
error_df=error_df,
week=selected_week
)
sel_tool.change(update_tool_error_plot, inputs=sel_tool, outputs=plot_tool_error)
sel_week.change(update_week_error_plot, inputs=sel_week, outputs=plot_week_error)
with gr.Row():
sel_tool
with gr.Row():
plot_tool_error
with gr.Row():
sel_week
with gr.Row():
plot_week_error
with gr.TabItem("ℹ️ About"):
with gr.Accordion("About the Benchmark"):
gr.Markdown("This app shows the actual performance of Olas Predict tools on the live market.")
demo.queue(default_concurrency_limit=40).launch()
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