app.py

# -*- coding: utf-8 -*-
"""
Created on Tue Apr  8 14:53:45 2025

@author: mritchey
"""

# streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\weather api\app.py"
import glob
import os
import numpy as np
import pandas as pd
import plotly.express as px
import streamlit as st
from geopy.extra.rate_limiter import RateLimiter
from geopy.geocoders import Nominatim


def geocode(address):
    try:
        address2 = address.replace(' ', '+').replace(',', '%2C')
        df = pd.read_json(
            f'https://geocoding.geo.census.gov/geocoder/locations/onelineaddress?address={address2}&benchmark=2020&format=json')
        results = df.iloc[:1, 0][0][0]['coordinates']
        lat, lon = results['y'], results['x']
    except:
        geolocator = Nominatim(user_agent="GTA Lookup")
        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)
        location = geolocator.geocode(address)
        lat, lon = location.latitude, location.longitude
    return lat, lon



@st.cache_data
def get_weather_data(lat, lon, start_date, end_date):

    url = f'https://archive-api.open-meteo.com/v1/archive?latitude={lat}&longitude={lon}&start_date={start_date}&end_date={end_date}&hourly=temperature_2m,precipitation,windspeed_10m,windgusts_10m&models=best_match&temperature_unit=fahrenheit&windspeed_unit=mph&precipitation_unit=inch'
    df = pd.read_json(url).reset_index()
    data = pd.DataFrame({c['index']: c['hourly'] for r, c in df.iterrows()})
    data['time'] = pd.to_datetime(data['time'])
    data['date'] = pd.to_datetime(data['time'].dt.date)
    data = data.query("temperature_2m==temperature_2m")

    data_agg = data.groupby(['date']).agg({'temperature_2m': ['min', 'mean', 'max'],
                                           'precipitation': ['sum'],
                                           'windspeed_10m': ['min', 'mean', 'max'],
                                           'windgusts_10m': ['min', 'mean', 'max']
                                           })
    data_agg.columns = data_agg.columns.to_series().str.join('_')
    data_agg = data_agg.query("temperature_2m_min==temperature_2m_min")
    return data.drop(columns=['date']), data_agg


@st.cache_data
def convert_df(df):
    return df.to_csv(index=0).encode('utf-8')


st.set_page_config(layout="wide")
col1, col2 = st.columns((2))


address = st.sidebar.text_input(
    "Address", "1000 Main St, Cincinnati, OH 45202")
start_date = st.sidebar.date_input("Start Date",  pd.Timestamp(2022, 9, 28))
end_date = st.sidebar.date_input("End Date",  pd.Timestamp(2022, 9, 30))
type_var = st.sidebar.selectbox(
    'Type:', ('Gust', 'Wind', 'Temp', 'Precipitation'))
hourly_daily = st.sidebar.radio('Aggregate Data', ('Hourly', 'Daily'))


start_date = start_date.strftime("%Y-%m-%d")
end_date = end_date.strftime("%Y-%m-%d")

lat, lon = geocode(address)

df_all, df_all_agg = get_weather_data(lat, lon, start_date, end_date)

# Keys
var_key = {'Gust': 'i10fg', 'Wind': 'wind10',
           'Temp': 't2m', 'Precipitation': 'tp'}

variable = var_key[type_var]

unit_key = {'Gust': 'MPH', 'Wind': 'MPH',
            'Temp': 'F', 'Precipitation': 'In.'}
unit = unit_key[type_var]

cols_key = {'Gust': ['windgusts_10m'], 'Wind': ['windspeed_10m'], 'Temp': ['temperature_2m'],
            'Precipitation': ['precipitation']}

cols_key_agg = {'Gust': ['windgusts_10m_min', 'windgusts_10m_mean',
                         'windgusts_10m_max'],
                'Wind': ['windspeed_10m_min', 'windspeed_10m_mean',
                         'windspeed_10m_max'],
                'Temp': ['temperature_2m_min', 'temperature_2m_mean', 'temperature_2m_max'],
                'Precipitation': ['precipitation_sum']}

if hourly_daily == 'Hourly':
    cols = cols_key[type_var]
else:
    cols = cols_key_agg[type_var]

if hourly_daily == 'Hourly':
    fig = px.line(df_all, x="time", y=cols[0])
    df_downloald = df_all
else:
    fig = px.line(df_all_agg.reset_index(), x="date", y=cols[0])
    df_downloald = df_all_agg.reset_index()

with col1:
    st.title('Weather Data')
    st.plotly_chart(fig)

    csv = convert_df(df_downloald)

    st.download_button(
        label="Download data as CSV",
        data=csv,
        file_name=f'{start_date}.csv',
        mime='text/csv')


with col2:
    st.title('')


st.markdown(""" <style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style> """, unsafe_allow_html=True)