app.py

import altair as alt
import pandas as pd
import plotly.graph_objects as go
import streamlit as st

from src.helper_functions import custom_metric_box, pollution_box
from src.predict import get_data_and_predictions

st.set_page_config(
    page_title="Utrecht Pollution Dashboard",
    page_icon="🌱",
    layout="wide",
    initial_sidebar_state="expanded",
)

alt.themes.enable("dark")

model_NO2, model_O3 = load_models()
week_data, predictions_O3, predictions_NO2 = get_data_and_predictions()
today = week_data.iloc[-1]
previous_day = week_data.iloc[-2]

dates_past = pd.date_range(end=pd.Timestamp.today(), periods=8).to_list()
dates_future = pd.date_range(
    start=pd.Timestamp.today() + pd.Timedelta(days=1), periods=3
).to_list()

# O3 and NO2 values for the past 7 days
o3_past_values = week_data["O3"]
no2_past_values = week_data["NO2"]
o3_future_values = pd.Series(predictions_O3[0].flatten())
no2_future_values = pd.Series(predictions_NO2[0].flatten())
o3_values = pd.concat([o3_past_values, o3_future_values], ignore_index=True)
no2_values = pd.concat([no2_past_values, no2_future_values], ignore_index=True)

dates = dates_past + dates_future
df = pd.DataFrame({"Date": dates, "O3": o3_values, "NO2": no2_values})

# App Title
st.title("Utrecht Pollution Dashboard🌱")

col1, col2 = st.columns((2, 3))
# Create a 3-column layout
with col1:
    st.subheader("Current Weather")
    subcol1, subcol2 = st.columns((1, 1))
    with subcol1:
        custom_metric_box(
            label="Temperature",
            value=f"{round(today['mean_temp'] * 0.1)} °C",
            delta=f"{round(today['mean_temp'] * 0.1) - round(previous_day['mean_temp'] * 0.1)} °C",
        )
        custom_metric_box(
            label="Humidity",
            value=f"{round(today['humidity'])} %",
            delta=f"{round(today['humidity']) - round(previous_day['humidity'])} %",
        )
        custom_metric_box(
            label="Pressure",
            value=f"{round(today['pressure'] * 0.1)} hPa",
            delta=f"{round(today['pressure'] * 0.1) - round(previous_day['pressure'] * 0.1)} hPa",
        )
    with subcol2:
        custom_metric_box(
            label="Precipitation",
            value=f"{round(today['percipitation'] * 0.1)} mm",
            delta=f"{round(today['percipitation'] * 0.1) - round(previous_day['percipitation'] * 0.1)} mm",
        )
        custom_metric_box(
            label="Solar Radiation",
            value=f"{round(today['global_radiation'])} J/m²",
            delta=f"{round(today['global_radiation']) - round(previous_day['global_radiation'])} J/m²",
        )
        custom_metric_box(
            label="Wind Speed",
            value=f"{round(today['wind_speed'] * 0.1, 1)} m/s",
            delta=f"{round(today['wind_speed'] * 0.1, 1) - round(previous_day['wind_speed'] * 0.1, 1)} m/s",
        )

with col2:
    st.subheader("Current Pollution Levels")
    sub1, sub2 = st.columns((1, 1))
    # Display the prediction
    # st.write(f'Predicted Pollution Level: {prediction[0]:.2f}')
    with sub1:
        pollution_box(
            label="O<sub>3</sub>",
            value=f"{round(today['O3'])} µg/m³",
            delta=f"{round(int(today['O3']) - int(previous_day['O3']))} µg/m³",
        )
        with st.expander("Learn more about O3", expanded=False):
            st.markdown(
                "*Ozone (O<sub>3</sub>)*: A harmful gas at ground level, contributing to respiratory issues and aggravating asthma.",
                unsafe_allow_html=True,
            )
    with sub2:
        pollution_box(
            label="NO<sub>2</sub>",
            value=f"{round(today['NO2'])} µg/m³",
            delta=f"{round(int(today['NO2']) - int(previous_day['NO2']))} µg/m³",
        )
        with st.expander("Learn more about O3", expanded=False):
            st.markdown(
                "*Wadeva particle (NO<sub>2</sub>)*: A harmful gas at ground level, contributing to respiratory issues and aggravating asthma.",
                unsafe_allow_html=True,
            )

    # Create two columns for two separate graphs
    # Plot O3 in the first subcolumn
    st.subheader("O3 and NO2 Prediction")
    # Plot NO2 in the second subcolumn
    fig_o3 = go.Figure()
    fig_o3.add_trace(
        go.Scatter(
            x=df["Date"],
            y=df["O3"],
            mode="lines+markers",
            name="O3",
            line=dict(color="rgb(0, 191, 255)", width=4),
            hovertemplate="%{x|%d-%b-%Y}<br> %{y} µg/m³<extra></extra>",
        )
    )
    fig_o3.add_shape(
        dict(
            type="line",
            x0=pd.Timestamp.today(),
            x1=pd.Timestamp.today(),
            y0=min(o3_values),
            y1=max(o3_values),
            line=dict(color="White", width=3, dash="dash"),
        )
    )
    fig_o3.update_layout(
        plot_bgcolor="rgba(0, 0, 0, 0)",
        paper_bgcolor="rgba(0, 0, 0, 0)",
        yaxis_title="O3 Concentration (µg/m³)",
        font=dict(size=14),
        hovermode="x",
        xaxis=dict(
            title="Date",
            type="date",
            tickmode="array",
            tickvals=df["Date"],
            tickformat="%d-%b",
            tickangle=-45,
            tickcolor="gray",
        ),
    )
    st.plotly_chart(fig_o3, key="fig_o3")

    fig_no2 = go.Figure()
    fig_no2.add_trace(
        go.Scatter(
            x=df["Date"],
            y=df["NO2"],
            mode="lines+markers",
            name="NO2",
            line=dict(color="rgb(255, 20, 147)", width=4),
        )
    )
    fig_no2.add_shape(
        dict(
            type="line",
            x0=pd.Timestamp.today(),
            x1=pd.Timestamp.today(),
            y0=min(no2_values),
            y1=max(no2_values),
            line=dict(color="gray", width=3, dash="dash"),
        )
    )
    fig_no2.update_layout(
        plot_bgcolor="rgba(0, 0, 0, 0)",
        paper_bgcolor="rgba(0, 0, 0, 0)",
        yaxis_title="NO<sub>2</sub> Concentration (µg/m³)",
        font=dict(size=14),
        hovermode="x",
        xaxis=dict(
            title="Date",
            type="date",
            tickmode="array",
            tickvals=df["Date"],
            tickformat="%d-%b",
            tickangle=-45,
            tickcolor="gray",
        ),
    )
    st.plotly_chart(fig_no2, key="fig_no2")