import pandas as pd

import os

import panel as pn
import hvplot as hv

pn.extension("bokeh", template="bootstrap")
import hvplot.pandas

from datetime import datetime
from datetime import date, timedelta

from DescriptionDict import DescriptionDict

# from dotenv import load_dotenv

# from datasets import load_dataset
from utils import load_hf_dataset  # ,upload_to_hf_dataset, download_from_hf_dataset

# Load environment variables from .env file
# load_dotenv()

# Get the Hugging Face API token from the environment; either set in .env file or in the environment directly in GitHub
HF_TOKEN = os.getenv("HF_TOKEN")

# Get the name of the  dataset for GuruFocus to read from
dataset_name_GuruFocus_input = os.getenv("dataset_name_GuruFocus_input")

@pn.cache()
def get_gurufocus(current_datetime):
    # Load lastest GuruFocus DataSet from HuggingFace Dataset which is always GuruFocus_merged.csv
    # daily_gurufocus_DF = load_dataset(dataset_name_GuruFocus_output , data_files="GuruFocus_merged.csv", split="train" , token=HF_TOKEN ).to_pandas()
    return load_hf_dataset(
        f"GuruFocus_merged_{current_datetime}.csv", HF_TOKEN, dataset_name_GuruFocus_input
    )

todaydate = pn.widgets.DatePicker(
    name="End Date",  # value=datetime(2000, 1, 1),
    value=date.today() - timedelta(days=1),
    description="Select a Date",
    end=date.today(),  # date(2023, 9, 1)
)

todaydate_readcols = datetime.today().strftime("%Y-%m-%d")
df = get_gurufocus(todaydate_readcols)
selectedcol = pn.widgets.Select(
    name="Select Ratio", value="PEG Ratio", options=list(df.columns)
)
selecteditem = pn.widgets.Select(
    name="Select Item", value="Sector", options=["Industry", "Sector"]
)
selectedmethod = pn.widgets.Select(
    name="Select Method", value="Mean", options=["Mean", "Min", "Max"]
)

ticker = pn.widgets.AutocompleteInput(
    name="Ticker", options=list(df.Ticker), placeholder="Write Ticker here همین جا"
)

ticker.value = "AAPL"

selectedhover = "Ticker"
hv.extension("bokeh")

def create_plot(selectedcol, selecteditem, todaydate, selectedmethod, ticker):
    df = get_gurufocus(todaydate)
    if selectedmethod == "Mean":
        group_them = df.groupby(selecteditem)[selectedcol].mean()
    if selectedmethod == "Min":
        group_them = df.groupby(selecteditem)[selectedcol].min()
    if selectedmethod == "Max":
        group_them = df.groupby(selecteditem)[selectedcol].max()
    df2 = df.merge(
        group_them,
        left_on=selecteditem,
        right_index=True,
        suffixes=("", f"_{selecteditem}_{selectedmethod}"),
    )
    df3 = df.query("Ticker == @ticker")
    return df2.hvplot.bar(
        x=selecteditem,
        y=f"{selectedcol}_{selecteditem}_{selectedmethod}",
        hover_cols=selectedhover,
        height=800,
        width=1800,
    ).opts(xrotation=90, fontsize={"xticks": 10}).opts(show_grid=True) * df3.hvplot.bar(
        x=selecteditem,
        y=f"{selectedcol}",
        hover_cols=selectedhover,
    )


def create_alert(selectedcol):
    text = f"### {selectedcol} \n {DescriptionDict[selectedcol]}"
    return pn.pane.Alert(text, alert_type="warning")

bound_plot = pn.bind(
    create_plot,
    selectedcol=selectedcol,
    selecteditem=selecteditem,
    todaydate=todaydate,
    selectedmethod=selectedmethod,
    ticker=ticker,
)

bound_alert = pn.bind(create_alert, selectedcol=selectedcol)
pn.Column(
    pn.Row(selectedcol, selecteditem, todaydate, selectedmethod, ticker),
    bound_plot,
    bound_alert,
).servable(title="Financial Sector Ratios Navigator")