import gradio as gr
import numpy as np
import datetime
import pandas as pd
import matplotlib.pyplot as plt

import hopsworks

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()