pages/3_Humidity.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import folium
from folium import LinearColormap
import requests
from datetime import datetime, timedelta
from streamlit_folium import st_folium

# Set page layout to wide
st.set_page_config(layout="wide", page_title="Real-Time Relative Humidity Data Dashboard")

# Function to load data
@st.cache_data(ttl=300)  # Cache data to avoid reloading every time
def load_data():
    with st.spinner("Loading data..."):
        response = requests.get("https://csdi.vercel.app/weather/rhum")
        data = response.json()
        features = data['features']
        df = pd.json_normalize(features)
        df.rename(columns={
            'properties.Relative Humidity(percent)': 'Relative Humidity (%)',
            'properties.Automatic Weather Station': 'Station Name',
            'geometry.coordinates': 'Coordinates'
        }, inplace=True)
        return df

# Check if the data has been loaded before
if 'last_run' not in st.session_state or (datetime.now() - st.session_state.last_run) > timedelta(minutes=5):
    st.session_state.df = load_data()
    st.session_state.last_run = datetime.now()

# Data
df = st.session_state.df

# Compute statistics
humidity_data = df['Relative Humidity (%)']
avg_humidity = humidity_data.mean()
max_humidity = humidity_data.max()
min_humidity = humidity_data.min()
std_humidity = humidity_data.std()

# Create three columns
col1, col2, col3 = st.columns([1.65, 2, 1.15])

# Column 1: Histogram and statistics
with col1:
    # Define colors for gradient
    color_scale = ['#58a0db', '#0033cc']

    # Create histogram
    fig = px.histogram(df, x='Relative Humidity (%)', nbins=20,
                       labels={'Relative Humidity (%)': 'Relative Humidity (%)'},
                       title='Relative Humidity Histogram',
                       color_discrete_sequence=color_scale)

    # Add average line
    fig.add_shape(
        go.layout.Shape(
            type="line",
            x0=avg_humidity,
            y0=0,
            x1=avg_humidity,
            y1=df['Relative Humidity (%)'].value_counts().max(),
            line=dict(color="red", width=2, dash="dash"),
        )
    )

    # Update layout
    fig.update_layout(
        xaxis_title='Relative Humidity (%)',
        yaxis_title='Count',
        title='Relative Humidity Distribution',
        bargap=0.2,
        title_font_size=20,
        xaxis_title_font_size=14,
        yaxis_title_font_size=14,
        height=350,
        shapes=[{
            'type': 'rect',
            'x0': min_humidity,
            'x1': max_humidity,
            'y0': 0,
            'y1': df['Relative Humidity (%)'].value_counts().max(),
            'fillcolor': 'rgba(0, 100, 255, 0.2)',
            'line': {
                'color': 'rgba(0, 100, 255, 0.2)',
                'width': 0
            },
            'opacity': 0.1
        }]
    )

    # Add annotations
    fig.add_annotation(
        x=avg_humidity,
        y=df['Relative Humidity (%)'].value_counts().max() * 0.9,
        text=f"Average: {avg_humidity:.2f}%",
        showarrow=True,
        arrowhead=1
    )

    st.plotly_chart(fig, use_container_width=True)

    # Display statistics
    col_1, col_2 = st.columns([1, 1])
    with col_1:
        st.metric(label="Average R.Humidity (%)", value=f"{avg_humidity:.2f}")
        st.metric(label="Minimum R.Humidity (%)", value=f"{min_humidity:.2f}")
    with col_2:
        st.metric(label="Maximum R.Humidity (%)", value=f"{max_humidity:.2f}")
        st.metric(label="Std. Dev (%)", value=f"{std_humidity:.2f}")

# Function to convert humidity to color based on gradient
def humidity_to_color(humidity, min_humidity, max_humidity):
    norm_humidity = (humidity - min_humidity) / (max_humidity - min_humidity)
    if norm_humidity < 0.5:
        r = int(173 + (0 - 173) * (2 * norm_humidity))
        g = int(216 + (0 - 216) * (2 * norm_humidity))
        b = int(230 + (255 - 230) * (2 * norm_humidity))
    else:
        r = int(0 + (0 - 0) * (2 * (norm_humidity - 0.5)))
        g = int(0 + (0 - 0) * (2 * (norm_humidity - 0.5)))
        b = int(255 + (0 - 255) * (2 * (norm_humidity - 0.5)))

    return f'rgb({r}, {g}, {b})'

# Column 2: Map
with col2:
    with st.spinner("Loading map..."):
        m = folium.Map(location=[22.3547, 114.1483], zoom_start=11, tiles='CartoDB positron')
        min_humidity = df['Relative Humidity (%)'].min()
        max_humidity = df['Relative Humidity (%)'].max()

        colormap = LinearColormap(
            colors=['#58a0db', 'blue'],
            index=[min_humidity, max_humidity],
            vmin=min_humidity,
            vmax=max_humidity,
            caption='Relative Humidity (%)'
        )
        colormap.add_to(m)

        for _, row in df.iterrows():
            humidity = row['Relative Humidity (%)']
            color = humidity_to_color(humidity, min_humidity, max_humidity)

            folium.Marker(
                location=[row['Coordinates'][1], row['Coordinates'][0]],
                popup=f"<p style='font-size: 12px; background-color: white; padding: 5px; border-radius: 5px;'>{row['Station Name']}: {humidity:.1f}%</p>",
                icon=folium.DivIcon(
                    html=f'<div style="font-size: 10pt; color: {color}; padding: 2px; border-radius: 5px;">'
                         f'<strong>{humidity:.1f}%</strong></div>'
                )
            ).add_to(m)

        st_folium(m, width=500, height=600)

# Column 3: Data Table
with col3:
    st.markdown(
        """
        <style>
        .dataframe-container {
            height: 600px;
            overflow-y: auto;
        }
        .dataframe th, .dataframe td {
            text-align: left;
            padding: 8px;
        }
        </style>
        """,
        unsafe_allow_html=True
    )

    # Rename column for display
    df_display = df[['Station Name', 'Relative Humidity (%)']].rename(columns={'Relative Humidity (%)': 'R.Humidity'})
    st.dataframe(df_display, height=600)

# Refresh Button
if st.button("Refresh Data"):
    with st.spinner("Refreshing data..."):
        st.session_state.df = load_data()
        st.session_state.last_run = datetime.now()
        st.experimental_rerun()