from datetime import datetime, timedelta
import gradio as gr
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import duckdb
import logging
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, about_this_dashboard

from scripts.utils import INC_TOOLS


def get_logger():
    logger = logging.getLogger(__name__)
    logger.setLevel(logging.DEBUG)
    # stream handler and formatter
    stream_handler = logging.StreamHandler()
    stream_handler.setLevel(logging.DEBUG)
    formatter = logging.Formatter(
        "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
    )
    stream_handler.setFormatter(formatter)
    logger.addHandler(stream_handler)
    return logger


logger = get_logger()


def get_last_one_month_data():
    """
    Get the last one month data from the tools.parquet file
    """
    logger.info("Getting last one month data")
    con = duckdb.connect(":memory:")
    one_months_ago = (datetime.now() - timedelta(days=60)).strftime("%Y-%m-%d")

    # Query to fetch data from all_trades_profitability.parquet
    query2 = f"""
    SELECT *
    FROM read_parquet('./data/all_trades_profitability.parquet')
    WHERE creation_timestamp >= '{one_months_ago}'
    """
    df2 = con.execute(query2).fetchdf()
    logger.info("Got last one month data from all_trades_profitability.parquet")

    query1 = f"""
    SELECT *
    FROM read_parquet('./data/tools.parquet')
    WHERE request_time >= '{one_months_ago}'
    """
    df1 = con.execute(query1).fetchdf()
    logger.info("Got last one month data from tools.parquet")

    con.close()

    return df1, df2


def get_all_data():
    """
    Get all data from the tools.parquet and all_trades_profitability.parquet files
    """
    logger.info("Getting all data")
    con = duckdb.connect(":memory:")

    # Query to fetch tools accuracy data
    query3 = f"""
    SELECT *
    FROM read_csv('./data/tools_accuracy.csv')
    """
    df3 = con.execute(query3).fetchdf()

    # Query to fetch data from all_trades_profitability.parquet
    query2 = f"""
    SELECT *
    FROM read_parquet('./data/all_trades_profitability.parquet')
    """
    df2 = con.execute(query2).fetchdf()
    logger.info("Got all data from all_trades_profitability.parquet")

    query1 = f"""
    SELECT *
    FROM read_parquet('./data/tools.parquet')
    """
    df1 = con.execute(query1).fetchdf()
    logger.info("Got all data from tools.parquet")

    con.close()

    return df1, df2, df3


def get_weighted_accuracy(row, global_requests: int):
    """Function to compute the weighted accuracy of a tool"""
    return row["tool_accuracy"] * (row["total_requests"] / global_requests)


def compute_weighted_accuracy(tools_accuracy: pd.DataFrame):
    global_requests = tools_accuracy.total_requests.sum()
    tools_accuracy["weighted_accuracy"] = tools_accuracy.apply(
        lambda x: get_weighted_accuracy(x, global_requests), axis=1
    )
    return tools_accuracy


def prepare_data():
    """
    Prepare the data for the dashboard
    """
    tools_df, trades_df, tools_accuracy_info = get_all_data()

    tools_df["request_time"] = pd.to_datetime(tools_df["request_time"])
    trades_df["creation_timestamp"] = pd.to_datetime(trades_df["creation_timestamp"])

    trades_df = prepare_trades(trades_df)

    tools_accuracy_info = compute_weighted_accuracy(tools_accuracy_info)

    return tools_df, trades_df, tools_accuracy_info


tools_df, trades_df, tools_accuracy_info = prepare_data()


demo = gr.Blocks()

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("# Number of trades per week")
            with gr.Row():
                trades_by_week_plot = plot_trades_by_week(trades_df=trades_count_df)
            with gr.Row():
                gr.Markdown("# Percentage of winning trades per 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("# Trading metrics")
            with gr.Row():
                trade_details_selector = gr.Dropdown(
                    label="Select a trade metric",
                    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("# All tools winning performance")

            with gr.Row():
                winning_selector = gr.Dropdown(
                    label="Select the tool 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("# Winning performance by each 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 Accuracy Dashboard"):
            with gr.Row():
                gr.Markdown("# Tools accuracy ranking")
                gr.Markdown("")
                gr.Markdown(
                    "The data used for this metric is from the past two months. This accuracy is computed based on right answers from the total requests received."
                )

            with gr.Row():
                tools_accuracy_info = tools_accuracy_info.sort_values(
                    by="tool_accuracy", ascending=False
                )
                plt.figure(figsize=(25, 10))
                plot = sns.barplot(
                    tools_accuracy_info,
                    x="tool_accuracy",
                    y="tool",
                    hue="tool",
                    dodge=False,
                    palette="viridis",
                )
                gr.Plot(value=plot.get_figure())
            with gr.Row():
                gr.Markdown("# Weighted accuracy ranking per tool")
                gr.Markdown("")
                gr.Markdown(
                    "The data used for this metric is from the past two months. This metric is computed using both the tool accuracy and the volume of requests received by the tool"
                )
            with gr.Row():
                tools_accuracy_info = tools_accuracy_info.sort_values(
                    by="weighted_accuracy", ascending=False
                )
                # Create the Seaborn bar plot
                sns.set_theme(palette="viridis")
                plt.figure(figsize=(25, 10))
                plot = sns.barplot(
                    tools_accuracy_info,
                    x="weighted_accuracy",
                    y="tool",
                    hue="tool",
                    dodge=False,
                )
                # Display the plot using gr.Plot
                gr.Plot(value=plot.get_figure())

        with gr.TabItem("🏥 Tool Error Dashboard"):
            with gr.Row():
                gr.Markdown("# All tools errors")
            with gr.Row():
                error_overall_plot = plot_error_data(error_all_df=error_overall_df)
            with gr.Row():
                gr.Markdown("# Error percentage per 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("# Tools distribution of errors per 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)

            with gr.Accordion("About this dashboard"):
                gr.Markdown(about_this_dashboard)

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