import datetime

import hopsworks
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import gradio as gr


def get_price():

    project = hopsworks.login()
    fs = project.get_feature_store()

    price_pred_fg = fs.get_feature_group(name="price_predictions", version=1).read()

    dates = [
        (datetime.datetime.now() + datetime.timedelta(days=i)).strftime("%Y-%m-%d")
        for i in range(1, 8)
    ]
    days_ahead = list(range(1, 8))

    prices = [
        price_pred_fg.loc[
            (price_pred_fg["date"] == dates[i])
            & (price_pred_fg["days_ahead"] == days_ahead[i])
        ]["predicted_price"].values[0]
        for i in range(0, 7)
    ]
    price_predictions = pd.DataFrame()
    price_predictions["date"] = dates
    price_predictions["price"] = prices

    fig = plt.figure()
    price_predictions.plot(kind="line", x="date", y="price")

    # print(price_predictions)
    return fig


demo = gr.Interface(
    fn=get_price,
    title="Energy Price Prediction",
    description="Predicted daily average energy prices over the coming 7 days",
    allow_flagging="never",
    inputs=[],
    outputs=["plot"],
)

demo.launch(share=True)