import gradio as gr
import pandas as pd
import plotly.express as px

trader_metric_choices = [
    "mech calls",
    "bet amount",
    "earnings",
    "net earnings",
    "ROI",
]
default_trader_metric = "ROI"


def get_metrics_text() -> gr.Markdown:
    metric_text = """ 
        ## Metrics at the graph
        These metrics are computed weekly. The statistical measures are:
        * min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
        * the upper and lower fences to delimit possible outliers
        * the average values as the dotted lines
        """

    return gr.Markdown(metric_text)


def get_interpretation_text() -> gr.Markdown:
    interpretation_text = """
        ## Meaning of KL-divergence values
            * Y = 0.05129
                * Market accuracy off by 5%
            * Y = 0.1053
                * Market accuracy off by 10%
            * Y = 0.2876
                * Market accuracy off by 25%
            * Y = 0.5108
                * Market accuracy off by 40%
            * Y = 1.2040
                * Market accuracy off by 70%
            * Y = 2.3026
                * Market accuracy off by 90%
    """
    return gr.Markdown(interpretation_text)


def plot_trader_metrics_by_market_creator(
    metric_name: str, traders_df: pd.DataFrame
) -> gr.Plot:
    """Plots the weekly trader metrics."""

    if metric_name == "mech calls":
        metric_name = "mech_calls"
        column_name = "nr_mech_calls"
        yaxis_title = "Total nr of mech calls per trader"
    elif metric_name == "ROI":
        column_name = "roi"
        yaxis_title = "Total ROI (net profit/cost)"
    elif metric_name == "bet amount":
        metric_name = "bet_amount"
        column_name = metric_name
        yaxis_title = "Total bet amount per trader (xDAI)"
    elif metric_name == "net earnings":
        metric_name = "net_earnings"
        column_name = metric_name
        yaxis_title = "Total net profit per trader (xDAI)"
    else:  # earnings
        column_name = metric_name
        yaxis_title = "Total gross profit per trader (xDAI)"

    traders_filtered = traders_df[["month_year_week", "market_creator", column_name]]

    fig = px.box(
        traders_filtered,
        x="month_year_week",
        y=column_name,
        color="market_creator",
        color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
        category_orders={"market_creator": ["pearl", "quickstart", "all"]},
    )
    fig.update_traces(boxmean=True)
    fig.update_layout(
        xaxis_title="Week",
        yaxis_title=yaxis_title,
        legend=dict(yanchor="top", y=0.5),
    )
    fig.update_xaxes(tickformat="%b %d\n%Y")

    return gr.Plot(
        value=fig,
    )


def plot_trader_metrics_by_trader_type(metric_name: str, traders_df: pd.DataFrame):
    """Plots the weekly trader metrics."""

    if metric_name == "mech calls":
        metric_name = "mech_calls"
        column_name = "nr_mech_calls"
        yaxis_title = "Total nr of mech calls per trader"
    elif metric_name == "ROI":
        column_name = "roi"
        yaxis_title = "Total ROI (net profit/cost)"
    elif metric_name == "bet amount":
        metric_name = "bet_amount"
        column_name = metric_name
        yaxis_title = "Total bet amount per trader (xDAI)"
    elif metric_name == "net earnings":
        metric_name = "net_earnings"
        column_name = metric_name
        yaxis_title = "Total net profit per trader (xDAI)"
    else:  # earnings
        column_name = metric_name
        yaxis_title = "Total gross profit per trader (xDAI)"

    traders_filtered = traders_df[["month_year_week", "trader_type", column_name]]

    fig = px.box(
        traders_filtered,
        x="month_year_week",
        y=column_name,
        color="trader_type",
        color_discrete_sequence=["gray", "orange", "darkblue"],
        category_orders={"trader_type": ["singlebet", "multibet", "all"]},
    )
    fig.update_traces(boxmean=True)
    fig.update_layout(
        xaxis_title="Week",
        yaxis_title=yaxis_title,
        legend=dict(yanchor="top", y=0.5),
    )
    fig.update_xaxes(tickformat="%b %d\n%Y")

    return gr.Plot(
        value=fig,
    )


def plot_winning_metric_per_trader(traders_winning_df: pd.DataFrame) -> gr.Plot:
    fig = px.box(
        traders_winning_df,
        x="month_year_week",
        y="winning_perc",
        color="market_creator",
        color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
        category_orders={"market_creator": ["pearl", "quickstart", "all"]},
    )
    fig.update_traces(boxmean=True)
    fig.update_layout(
        xaxis_title="Week",
        yaxis_title="Weekly winning percentage %",
        legend=dict(yanchor="top", y=0.5),
        width=1000,  # Adjusted for better fit on laptop screens
        height=600,  # Adjusted for better fit on laptop screens
    )
    fig.update_xaxes(tickformat="%b %d\n%Y")

    return gr.Plot(
        value=fig,
    )