streamlit_app.py

import openmeteo_requests
import requests_cache
import polars as pl
from retry_requests import retry
import streamlit as st
from datetime import datetime, timedelta
import pytz
from lets_plot import *
from streamlit_letsplot import st_letsplot
import numpy as np 
#API Fetch
openmeteo = openmeteo_requests.Client()

#Empty Frames
locations = [
    {"name": "Rexburg, Idaho", "latitude": 43.8251, "longitude": -111.7924},
    {"name": "Provo, Utah", "latitude": 40.2338, "longitude": -111.6585},
    {"name": "Laie, Hawaii", "latitude": 21.6210, "longitude": -157.9753}
]
location_names = [location["name"] for location in locations]
filtered_forecasts = {}
filtered_histories = {}
historical_data = []
forecast_data = []
timezones = pytz.all_timezones
def find_max(df):
       daily_highs = df.group_by(by = 'Date').agg(pl.max("Temperature").alias('max'))
       return daily_highs, df


# Date Variables
today = datetime.today()
default = today - timedelta(days=14)

# Streamlit Variables
start_date = st.sidebar.date_input(
    'Select start date:',
    value = default,
    max_value= default
)

selected_timezone = st.sidebar.selectbox(
    'Choose a timezone:',
    timezones
)
end_date = start_date + timedelta(days=14)

temperature_option = st.sidebar.selectbox(
    'Select Temperature Type',
    ('Highest', 'Lowest')
)

city_option = st.sidebar.selectbox(
    'Select City',
    ('Rexburg, Idaho', 'Provo, Utah', 'Laie, Hawaii')
)
# Display the selected date range
st.sidebar.write(f"Date Range: {start_date} to {end_date}")

# Get Forecast Data
for location in locations:
    url = "https://api.open-meteo.com/v1/forecast"
    params = {
        "latitude": location["latitude"],
        "longitude": location["longitude"],
        "start_date": start_date.strftime('%Y-%m-%d'),
        "end_date": end_date.strftime('%Y-%m-%d'),
        "hourly": "temperature_2m"
    }

    # Fetch weather data
    responses = openmeteo.weather_api(url, params=params)
    response = responses[0]
    hourly = response.Hourly()
    hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()

    # Convert timestamp to datetime
    start = datetime.fromtimestamp(hourly.Time())
    end = datetime.fromtimestamp(hourly.TimeEnd())
    freq = timedelta(seconds=hourly.Interval())

    # Create Polars DataFrame
    hourly_data = pl.select(
  date = pl.datetime_range(start, end, freq, closed = "left"),
  temperature_2m = hourly_temperature_2m,
  location = [location["name"]])

    hourly_dataframe = pl.DataFrame(data = hourly_data)


    historical_data.append(hourly_dataframe)
# Concatenate all DataFrames
combined_historical = pl.DataFrame(pl.concat(historical_data)).explode('location')



# Get True Historical Data
for location in locations:
    url = "https://archive-api.open-meteo.com/v1/archive"
    params = {
        "latitude": location["latitude"],
        "longitude": location["longitude"],
        "start_date": start_date.strftime('%Y-%m-%d'),
        "end_date": end_date.strftime('%Y-%m-%d'),
        "hourly": "temperature_2m"
    }

    # Fetch weather data
    responses = openmeteo.weather_api(url, params=params)
    response = responses[0]
    hourly = response.Hourly()
    hourly_temperature_2m = hourly.Variables(0).ValuesAsNumpy()

    # Convert timestamp to datetime
    start = datetime.fromtimestamp(hourly.Time())
    end = datetime.fromtimestamp(hourly.TimeEnd())
    freq = timedelta(seconds=hourly.Interval())

    # Create Polars DataFrame
    hourly_data = pl.select(
  date = pl.datetime_range(start, end, freq, closed = "left"),
  temperature_2m = hourly_temperature_2m,
  location = [location["name"]])

    hourly_dataframe = pl.DataFrame(data = hourly_data)


    forecast_data.append(hourly_dataframe)

combined_forecast = pl.DataFrame(pl.concat(forecast_data)).explode('location')

for name in location_names:
    filtered_forecasts[name] = (combined_forecast.filter(pl.col("location") == name).drop('location').rename({'date': 'Date'}).rename({'temperature_2m': 'Temperature'}).with_columns(pl.col('Temperature') * 9/5 + 32))
    filtered_histories[name] = (combined_historical.filter(pl.col("location") == name).drop('location').rename({'date': 'Date'}).rename({'temperature_2m': 'Temperature'}).with_columns(pl.col('Temperature') * 9/5 + 32))


tab1, tab2, tab3 = st.tabs(["Data", "Visualisations", "KPIs"])

with tab1:
  st.title("Forecasted Weather vs Actual Weather by City")
  st.header('Forecasts')
  st.markdown("<h2 style='text-align: center; color: white;'>Rexburg</h2>", unsafe_allow_html=True)

# Create two columns for Rexburg content
  rexburg_col1, rexburg_col2 = st.columns(2)

# Rexburg content
  with rexburg_col1:
    st.write("Forecasts")
    st.dataframe(filtered_forecasts["Rexburg, Idaho"], use_container_width=True, hide_index=True)

  with rexburg_col2:
    st.write("Historical Data")
    st.dataframe(filtered_histories["Rexburg, Idaho"], use_container_width=True, hide_index=True)

# Provo Header
  st.markdown("<h2 style='text-align: center; color: white;'>Provo</h2>", unsafe_allow_html=True)

# Create two columns for Provo content
  provo_col1, provo_col2 = st.columns(2)

# Provo content
  with provo_col1:
    st.write("Forecasts")
    st.dataframe(filtered_forecasts["Provo, Utah"], use_container_width=True, hide_index=True)

  with provo_col2:
    st.write("Historical Data")
    st.dataframe(filtered_histories["Provo, Utah"], use_container_width=True, hide_index=True)

# Laie Header
  st.markdown("<h2 style='text-align: center; color: white;'>Laie</h2>", unsafe_allow_html=True)

# Create two columns for Laie content
  laie_col1, laie_col2 = st.columns(2)

# Laie content
  with laie_col1:
    st.write("Forecasts")
    st.dataframe(filtered_forecasts["Laie, Hawaii"], use_container_width=True, hide_index=True)


  with laie_col2:
    st.write("Historical Data")
    st.dataframe(filtered_histories["Laie, Hawaii"], use_container_width=True, hide_index=True)

with tab2: 
    st.header('Visualisations by City')



    st.subheader("Forecasted Data vs. Historical Data")
    
    combined_forecast = combined_forecast.rename({'date': 'Date'}).rename({'temperature_2m': 'Temperature'}).with_columns(pl.col('Temperature') * 9/5 + 32).with_columns(pl.col('Date').cast(datetime))
    combined_historical = combined_historical.rename({'date': 'Date'}).rename({'temperature_2m': 'Temperature'}).with_columns(pl.col('Temperature') * 9/5 + 32).with_columns(pl.col('Date').cast(datetime))

    reg_forecasted = ggplot(combined_forecast, aes(x='Date', y='Temperature', color = 'location')) \
        + geom_line() \
        + facet_wrap('location', ncol = 1) \
        + labs(title = f'Hourly Temperatures',
            x = 'Date', y = 'Temperature (°F)', color = 'City Name') + \
            guides(color="none") + \
            scale_x_datetime(format = '%m/%d')
    
    reg_historical = ggplot(combined_historical, aes(x='Date', y='Temperature', color = 'location')) \
        + geom_line() \
        + facet_wrap('location', ncol = 1) \
        + labs(title = f'Hourly Temperatures',
            x = 'Date', y = 'Temperature (°F)', color = 'City Name') + \
            guides(color="none") + \
            scale_x_datetime(format = '%m/%d')
    
    st.subheader('Forecasted')
    st_letsplot(reg_forecasted)
    st.subheader('Historical')
    st_letsplot(reg_historical)
    st.subheader('Boxplot of Average Hourly Temperature')
    box_plot = ggplot(combined_forecast, aes(x='location', y='Temperature', color = 'location')) + \
                geom_jitter(alpha = .65) + \
                geom_boxplot(alpha = .9) + \
                labs(title=f'Hourly Temperature Readings',
                 x='City', y='Temperature (°F)') + \
                 guides(color = "none")
    st_letsplot(box_plot)
    maxes = combined_historical.group_by('location').agg((pl.col('Temperature').max()))
    max_plot = ggplot(maxes, aes(x = 'location', y = 'Temperature', color = 'location')) + \
                geom_bar(stat='identity') + \
                labs(x = 'City Name', y = 'Max Temperature in Month (°F)')
    st.subheader('Maximum Temperature for Period')
    st_letsplot(max_plot)

with tab3:
   if temperature_option == 'Highest':

    highest_temp = combined_historical.filter(pl.col('location') == city_option).select(pl.max('Temperature')).item()
    st.metric(label=f"Highest Temperature in {city_option}", value=f"{round(highest_temp,2)} °F")
   else:
    # Get the lowest temperature and its corresponding date
    lowest_temp = combined_historical.filter(pl.col('location') == city_option).select(pl.min('Temperature')).item()
    st.metric(label=f"Highest Temperature in {city_option}", value=f"{round(lowest_temp,2)} °F")