from flask import Flask, render_template, request
import sqlite3
import pandas as pd
from config import DB_PATH
import math

app = Flask(__name__)

def load_data():
    # 1) grab raw
    conn = sqlite3.connect(DB_PATH)
    df = pd.read_sql("SELECT * FROM speed_tests", conn)
    conn.close()

    # 2) parse epoch→UTC datetimes, then shift to Europe/Berlin
    df['datetime'] = (
        pd.to_datetime(df['timestamp'], unit='s', utc=True)
          .dt.tz_convert('Europe/Berlin')
    )

    # 3) for your table display, format as naive strings
    df['recorded_at'] = df['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S')

    return df


def get_aggregated_data(df: pd.DataFrame, interval: str = '5min'):
    # ensure 'datetime' is the index
    df = df.set_index('datetime')

    # 1) resample into N-minute bins, mean
    agg = (
        df
        .resample(interval)
        .agg({
            'down_90th': 'mean',
            'up_90th':   'mean'
        })
        .reset_index()
    )

    # 3) output for Chart.js
    return {
        "times":     agg['recorded_at'].tolist(),
        "down_90th": agg['down_90th'].round(2).tolist(),
        "up_90th":   agg['up_90th'].round(2).tolist()
    }


@app.route('/')
def index():
    df = load_data()
    agg = request.args.get("agg", "5min")
    chart_data = get_aggregated_data(df, agg)

    print(f"down last 10: {chart_data['down_90th'][-10:]}")
    print(f"down last 10: {chart_data['up_90th'][-10:]}")

    print(math.isnan(chart_data['down_90th'][-1]) or math.isnan(chart_data['up_90th'][-1]))

    #if math.isnan(chart_data['down_90th'][-1]) or math.isnan(chart_data['up_90th'][-1]):
        #return render_template('local.html', data=df.to_dict(orient='records'), chart_data=chart_data)

    return render_template('index.html', aggregation=agg, data=df.to_dict(orient='records'), chart_data=chart_data)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5001)