pages/dashboard.py

import pandas as pd
import streamlit as st
import sqlite3
import plotly.express as px

# Database connection
conn = sqlite3.connect("data/biodiv_sports.db")

# Queries
query_zone_catg_prop = """
SELECT
  CASE
    WHEN species_id IS NOT NULL THEN 'Espèces'
    ELSE 'Réglementaire'
  END AS zone_category,
  ROUND(COUNT(DISTINCT id)*1.0/(
          SELECT
            COUNT(DISTINCT id)
          FROM
            sensitive_areas
        ), 2) AS proportion
FROM
  sensitive_areas
GROUP BY
  zone_category;
"""

query_mon_wise_prop = """
SELECT
  months,
  species_id,
  ROUND(COUNT(DISTINCT id)*1.0/(
          SELECT
            COUNT(DISTINCT id)
          FROM
            sensitive_areas
        ), 2) AS proportion
FROM
  sensitive_areas
GROUP BY
  months
ORDER BY
  months;
"""

query_practice_mon_wise = """
SELECT
  practices,
  months,
  species_id,
  COUNT(DISTINCT id) AS nombre_zone
FROM
  sensitive_areas
GROUP BY
  1,
  2,
  3;
"""

query_zone_per_species = """
SELECT
  name,
  COUNT(DISTINCT id) AS number_of_zones
FROM
  sensitive_areas
WHERE
  species_id IS NOT NULL
GROUP BY
  1;
"""

# Read query results into Pandas DataFrames
df_zone_catg_prop = pd.read_sql_query(query_zone_catg_prop, conn)
df_mon_wise_prop = pd.read_sql_query(query_mon_wise_prop, conn)
df_practice_mon_wise = pd.read_sql_query(query_practice_mon_wise, conn)
df_zone_per_species = pd.read_sql_query(query_zone_per_species, conn)

# Map month names to their corresponding numbers
MONTHS = ['janvier', 'fevrier', 'mars', 'avril', 'mai', 'juin',
          'juillet', 'août', 'septembre', 'octobre', 'novembre', 'decembre']
MONTH_MAPPING = {k: v+1 for v, k in enumerate(MONTHS)}

# Add a numeric_months column based on MONTH_MAPPING
df_practice_mon_wise['numeric_months'] = df_practice_mon_wise['months'].map(
                                                            MONTH_MAPPING
                                                            )
df_mon_wise_prop['numeric_months'] = df_mon_wise_prop['months'].map(
                                                            MONTH_MAPPING
                                                            )

# Sort by numeric_months and reset index before dropping it
df_practice_mon_wise = df_practice_mon_wise.sort_values(['numeric_months',
                                                         'species_id']
                                                        ).reset_index(
                                                          drop=True
                                                          )
df_practice_mon_wise.drop('numeric_months', axis=1, inplace=True)

df_mon_wise_prop = df_mon_wise_prop.sort_values(['numeric_months',
                                                 'proportion']
                                                ).reset_index(
                                                  drop=True
                                                  )
df_mon_wise_prop.drop('numeric_months', axis=1, inplace=True)


st.markdown("""<h1 style='text-align: center'>Visualization of Biodiv-Sport
            Database</h1>""", unsafe_allow_html=True)

# Charts
fig_pie = px.pie(df_zone_catg_prop, values="proportion", names="zone_category")
fig_pie.update_layout(
    paper_bgcolor="#AFDAFC",
    plot_bgcolor="#FFFFFF",
    font_color="#2E2F2C",
    title={
        "text": "<b>Proportions of Zone Categories</b>",
        "x": 0.,
        "y": 1
    })

fig_bar = px.bar(df_zone_per_species, x="name", y="number_of_zones")
fig_bar.update_layout(
    paper_bgcolor="#AFDAFC",
    plot_bgcolor="#FFFFFF",
    font_color="#2E2F2C",
    title={
        "text": "<b>Total Number of Zones Per Species</b>",
        "x": 0.,
        "y": 1
    })

fig_line = px.line(df_mon_wise_prop, x="months",
                   y="proportion",
                   color="species_id")
fig_line.update_layout(
    paper_bgcolor="#AFDAFC",
    plot_bgcolor="#FFFFFF",
    font_color="#2E2F2C",
    title={
        "text": "<b>Monthly Proportion Distribution Across Year</b>",
        "x": 0.,
        "y": 1
    })


fig_heatmap = px.density_heatmap(df_practice_mon_wise,
                                 x="months",
                                 y="practices",
                                 z="nombre_zone")
fig_heatmap.update_layout(
    xaxis={
        "tickmode": "array",
        "tickvals": list(MONTH_MAPPING.keys()),
    },
    paper_bgcolor="#AFDAFC",
    plot_bgcolor="#FFFFFF",
    font_color="#e07903",
    title={
        "text": "Number of Zones According to Activity Practiced and By Month",
        "x": 0.,
        "y": 1.0
    }
)

st.plotly_chart(fig_pie)

st.plotly_chart(fig_bar)


st.plotly_chart(fig_line)

st.plotly_chart(fig_heatmap)