import pandas as pd
import gradio as gr
import matplotlib.pyplot as plt
import seaborn as sns
from seaborn import FacetGrid
import plotly.express as px


HEIGHT = 600
WIDTH = 1000


def plot_daily_invalid_trades_plotly(invalid_trades: pd.DataFrame):
    fig = px.histogram(invalid_trades, x="creation_date")
    return gr.Plot(value=fig)


def plot_daily_dist_invalid_trades(invalid_trades: pd.DataFrame):
    """Function to paint the distribution of daily invalid trades, no matter which market"""
    sns.set_theme(palette="viridis")
    plt.figure(figsize=(25, 10))
    plot2 = sns.histplot(data=invalid_trades, x="creation_date", kde=True)
    plt.xlabel("Creation date")
    plt.ylabel("Daily number of invalid trades")
    plt.xticks(rotation=45, ha="right")
    daily_trades_fig = plot2.get_figure()
    return gr.Plot(value=daily_trades_fig)


def plot_daily_nr_invalid_markets(invalid_trades: pd.DataFrame):
    """Function to paint the number of invalid markets over time"""
    daily_invalid_markets = (
        invalid_trades.groupby("creation_date")
        .agg(trades_count=("title", "count"), nr_markets=("title", "nunique"))
        .reset_index()
    )
    daily_invalid_markets["creation_date"] = daily_invalid_markets[
        "creation_date"
    ].astype(str)
    daily_invalid_markets.columns = daily_invalid_markets.columns.astype(str)

    return gr.LinePlot(
        value=daily_invalid_markets,
        x="creation_date",
        y="nr_markets",
        y_title="nr_markets",
        interactive=True,
        show_actions_button=True,
        tooltip=["creation_date", "nr_markets", "trades_count"],
        height=HEIGHT,
        width=WIDTH,
    )


def plot_ratio_invalid_trades_per_market(invalid_trades: pd.DataFrame):
    """Function to paint the number of invalid trades that the same market accummulates"""
    cat = invalid_trades["title"]
    codes, uniques = pd.factorize(cat)

    # add the IDs as a new column to the original dataframe
    invalid_trades["title_id"] = codes
    plot: FacetGrid = sns.displot(invalid_trades, x="title_id")
    plt.xlabel("market id")
    plt.ylabel("Total number of invalid trades by market")
    plt.title("Distribution of invalid trades per market")
    return gr.Plot(value=plot.figure)


def plot_top_invalid_markets(invalid_trades: pd.DataFrame):
    """Function to paint the top markets with the highest number of invalid trades"""
    top_invalid_markets: pd.DataFrame = (
        invalid_trades.title.value_counts().reset_index()
    )
    print(top_invalid_markets.head(5))
    top_invalid_markets = top_invalid_markets.head(5)
    top_invalid_markets.rename(columns={"count": "nr_invalid_trades"}, inplace=True)
    return gr.DataFrame(top_invalid_markets)