app.py

import base64
import plotly.express as px
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import streamlit as st

from swga import display_quarterly_gdp, display_sector_to_gdp_time_series_analysis
from rgdpvsi import display_realgdp_to_inflation, display_per_capita
from realgdp import real_gdp_growth
from gdp_rate import gdp_rate
from expenditure import expenditure_vs_GDP
from cpi_time_series import energy_vs_freshProducs_vs_general_index, localGoods_vs_importedGoods
from inflation_category import inflation_by_category
from exchange_rate import display_exchange_rate_trend

# Set Streamlit page configuration
st.set_page_config(
    page_title="GDP and CPI DashBoard",
    page_icon=":bar_chart:",
    layout="wide"
)

# Load GDP yearly data files
gdp_macro_economy = pd.read_excel(
    io="GDP_data.xlsx",
    engine="openpyxl",
    sheet_name="Table A"
)

gdp_expenditure = pd.read_excel(
    io="GDP_data.xlsx",
    engine="openpyxl",
    sheet_name="T3 GDP CY"
)
sector_gdp = pd.read_excel(
    io="GDP_data.xlsx",
    engine="openpyxl",
    sheet_name="CYGDP KP"
)

gdp_expenditure_percentage = pd.read_excel(
    io="GDP_data.xlsx",
    engine="openpyxl",
    sheet_name="T3A GDP XCY"
)


# Load GDP quarterly data files
quarterly_gdp = pd.read_excel(
    io="CovertGDP.xlsx",
    engine="openpyxl",
    sheet_name="QGDP KP"
)

# Loading CPI data file
cpi_urban = pd.read_excel(
    io="CleanedCPI.xlsx",
    engine="openpyxl",
    sheet_name="Urban"
)

cpi_other_indices = pd.read_excel(
    io="CleanedCPI.xlsx",
    engine="openpyxl",
    sheet_name="Other_Indices"
)


# Header and Footer Image
header_image_path = 'images/NISR_logo.png'

header_col1, header_col2, header_col3 = st.columns([4, 6, 1])
with header_col2:
    st.image(header_image_path, width=400)

# Display the title with emoji centered
st.markdown(
    '<div style="text-align: center;">'
    '<h1 style="display: inline-block; margin-bottom: 0;">&#x1F4CA; Rwanda Economic Dashboard: Insights into GDP & Inflation Dynamics</h1>'
    '<div style="height: 2px; background-color: #ccc; width: 80%; margin: 0 auto;"></div>'
    '</div>',
    unsafe_allow_html=True
)

# Get the lastest GDP, CPI, Population and Inflation values from the data frames
last_gdp_value = quarterly_gdp['GROSS DOMESTIC PRODUCT (GDP)'].iloc[-1]
last_gdp_quarter = quarterly_gdp['Quarters'].iloc[-1]
last_cpi_value = cpi_urban['GENERAL INDEX (CPI)'].iloc[-1]
inflation_rate = ((cpi_urban['GENERAL INDEX (CPI)'].iloc[-1] /
                  cpi_urban['GENERAL INDEX (CPI)'].iloc[-13]) - 1) * 100
last_population_value = gdp_macro_economy['Total population (millions)'].iloc[-1]

cards_data = [
    {
        'title': 'Gross Domestic Product',
        'subtitle': 'Constant 2017 prices, Billions RWF',
        'time': f'{last_gdp_quarter}',
        'value': f"{last_gdp_value:.2f}",
        'icon': '💼'
    },
    {
        'title': 'Consumer Price Index',
        'subtitle': 'February 2014 = 100',
        'time': 'October 2023',
        'value': f"{last_cpi_value:.1f}",
        'icon': '🛒'
    },
    {
        'title': 'Inflation Rate',
        'subtitle': 'Year-over-Year',
        'time': 'October 2023',
        'value': f"{inflation_rate:.2f}%",
        'icon': '📈'
    },
    {
        'title': 'Population Size',
        'subtitle': 'Number (millions)',
        'time': '2022',
        'value': f"{last_population_value:.2f}M",
        'icon': '👥'  # Replace with suitable emoji or image
    }
]

st.markdown("""
<style>
.card {
    background-color: #2258a9; /* Change the background color as needed */
    color: white; /* Text color */
    padding: 20px;
    border-radius: 10px; /* Adjust the border radius as needed */
    box-shadow: 2px 2px 10px rgba(0,0,0,0.15); /* Optional: Adds a shadow effect */
    transition: transform 0.2s; /* Optional: Adds a slight 'pop' effect on hover */
    margin: 10px;
}
.card:hover {
    transform: scale(1.05); /* Optional: Slight zoom effect on hover */
}
.card h1 {
    color: #ffff; /* Color for the icon and value */
}
.card h4 {
    color: black; /* Black title color */
    font-weight: bold; /* Make the title bolder */
    margin-bottom: 0.5rem; /* Space below the title */
}
</style>
""", unsafe_allow_html=True)

# Loop through the data and create two columns for each row
for i in range(0, len(cards_data), 2):
    cols = st.columns(2)
    for j in range(2):
        # Check if the card exists (for the last row if the number of cards is odd)
        if i + j < len(cards_data):
            card = cards_data[i + j]
            with cols[j]:
                # Use the "card" class for styling
                st.markdown(f"""
                <div class="card">
                    <h4>{card['title']}</h4>
                    <p>{card['subtitle']}</p>
                    <p>{card['time']}</p>
                    <h1>{card['icon']} {card['value']}</h1>
                </div>
                """, unsafe_allow_html=True)
# Sub header style
subheader_style = """
<style>
.subheader-container {
    background-color: #2258a9;
    color: white; 
    padding: 10px;
    max-width: 50%;
    font-size: 24px;
    text-align: center;
    border-radius: 4px;
    margin: 40px 0;
    margin-left: auto;
    margin-right: auto;
}
</style>
"""
st.markdown(subheader_style, unsafe_allow_html=True)
st.markdown(f'<div class="subheader-container">GDP Dynamics and Insights</div>',
            unsafe_allow_html=True)

# Create a grid of 2 boxes for Real GDP and its Growth rate visualization
cola, colb = st.columns(2)
with cola:
    real_gdp_growth()

with colb:
    gdp_rate()

colc, cold = st.columns(2)

with colc:
    expenditure_vs_GDP(gdp_expenditure_percentage)


with cold:
    display_sector_to_gdp_time_series_analysis()

colg, colh = st.columns(2)

with colg:
    display_quarterly_gdp()

with colh:
    display_per_capita()


st.markdown(subheader_style, unsafe_allow_html=True)
st.markdown(f'<div class="subheader-container">Insights On the Consumer Price Index</div>',
            unsafe_allow_html=True)

cole, colf = st.columns(2)

with cole:
    energy_vs_freshProducs_vs_general_index()


with colf:
    localGoods_vs_importedGoods()


inflation_by_category()


st.markdown(subheader_style, unsafe_allow_html=True)
st.markdown(f'<div class="subheader-container">Comparisons of GDP, Inflation and Per Capita Data</div>',
            unsafe_allow_html=True)

display_realgdp_to_inflation()
display_exchange_rate_trend()

# Footer


def get_image_as_base64(path):
    with open(path, "rb") as img_file:
        return base64.b64encode(img_file.read()).decode('utf-8')


footer_image_base64 = get_image_as_base64('images/NISR_logo.png')

# Use the base64 string as the source for the image
footer_html = f"""
<div style='text-align: center;'>
    <img src='data:image/png;base64,{footer_image_base64}' style='width: 100px; display: inline-block; margin-right: 10px;'/>
    <p style='display: inline-block; font-size:20px;'>© 2023 DataDynamos</p>
</div>
<div style='text-align:center;'>
    <p style='display: inline-block; font-size:20px;'>All rights reserved</p>
</div>
"""
st.write("---")
st.markdown(footer_html, unsafe_allow_html=True)