app.py

import subprocess
import logging
from datetime import datetime
import gradio as gr
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.triggers.cron import CronTrigger
from pytz import utc
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_tool_winnings_overall,
    plot_tool_winnings_by_tool
)
from tabs.error import (
    get_error_data, 
    get_error_data_overall,
    plot_error_data,
    plot_tool_error_data,
    plot_week_error_data
)
from tabs.about import about_olas_predict
import psutil

def log_ram_usage():
    process = psutil.Process()
    mem_info = process.memory_info()
    # Convert memory usage to GB
    rss_gb = mem_info.rss / (1024 ** 3)
    vms_gb = mem_info.vms / (1024 ** 3)
    logging.info(f"RAM Usage: RSS={rss_gb:.2f} GB, VMS={vms_gb:.2f} GB")
    print(f"RAM Usage: RSS={rss_gb:.2f} GB, VMS={vms_gb:.2f} GB")

tools_df = pd.read_parquet("./data/tools.parquet")
trades_df = pd.read_parquet("./data/all_trades_profitability.parquet")
trades_df = prepare_trades(trades_df)

log_ram_usage()

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'
]


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_overall_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():
                trades_by_week_plot = plot_trades_by_week(
                    trades_df=trades_count_df
                )
            with gr.Row():
                gr.Markdown("# Plot of winning trades by week")
            with gr.Row():
                winning_trades_by_week_plot = 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_tool_winnings_overall(
                    wins_df=winning_rate_overall_df,
                    winning_selector="win_perc"
                )

            def update_tool_winnings_overall_plot(winning_selector):
                return plot_tool_winnings_overall(
                    wins_df=winning_rate_overall_df,
                    winning_selector=winning_selector
                )

            winning_selector.change(
                update_tool_winnings_overall_plot,
                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():
                tool_winnings_by_tool_plot = plot_tool_winnings_by_tool(
                    wins_df=winning_rate_df,
                    tool=INC_TOOLS[0]
                )

            def update_tool_winnings_by_tool_plot(tool):
                return plot_tool_winnings_by_tool(
                    wins_df=winning_rate_df,
                    tool=tool
                )

            sel_tool.change(
                update_tool_winnings_by_tool_plot,
                inputs=sel_tool, 
                outputs=tool_winnings_by_tool_plot
            )

            with gr.Row():
                sel_tool
            with gr.Row():
                tool_winnings_by_tool_plot

        with gr.TabItem("🏥 Tool Error Dashboard"):
            with gr.Row():
                gr.Markdown("# Plot showing overall error")
            with gr.Row():
                error_overall_plot = 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():
                tool_error_plot = 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=tool_error_plot
            )
            with gr.Row():
                sel_tool
            with gr.Row():
                tool_error_plot

            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():
                week_error_plot = 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=tool_error_plot)
            sel_week.change(update_week_error_plot, inputs=sel_week, outputs=week_error_plot)

            with gr.Row():
                sel_tool
            with gr.Row():
                tool_error_plot
            with gr.Row():
                sel_week
            with gr.Row():
                week_error_plot
            
        with gr.TabItem("ℹ️ About"):
            with gr.Accordion("About Olas Predict"):
                gr.Markdown(about_olas_predict)

demo.queue(default_concurrency_limit=40).launch()