app.py

import requests
import pandas as pd
from io import StringIO
import streamlit as st
import os
import plotly.express as px
import plotly.graph_objects as go
import plotly.colors as pc
import numpy as np
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.stattools import acf
from statsmodels.graphics.tsaplots import plot_acf
import matplotlib.pyplot as plt
from datetime import datetime
import folium
import seaborn as sns
from streamlit_folium import st_folium
import datetime


def get_current_time():
    now = datetime.now()
    current_hour = now.hour
    current_minute = now.minute
    # Return the hour and a boolean indicating if it is after the 10th minute
    return current_hour, current_minute >= 10

##GET ALL FILES FROM GITHUB
@st.cache_data(show_spinner=False)
def load_GitHub(github_token, file_name, hour, after_10_min):
    url = f'https://raw.githubusercontent.com/margaridamascarenhas/Transparency_Data/main/{file_name}'
    headers = {'Authorization': f'token {github_token}'}

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        csv_content = StringIO(response.text)
        df = pd.read_csv(csv_content)
        if 'Date' in df.columns:
            df['Date'] = pd.to_datetime(df['Date'])  # Convert 'Date' column to datetime
            df.set_index('Date', inplace=True)  # Set 'Date' column as the index
            #df.to_csv(file_name) 
        return df
    else:
        print(f"Failed to download {file_name}. Status code: {response.status_code}")
        return None
        
@st.cache_data(show_spinner=False)
def load_forecast(github_token, hour, after_10_min):
    predictions_dict = {}
    for hour in range(24):
        file_name = f'Predictions_{hour}h.csv'
        df = load_GitHub(github_token, file_name, hour, after_10_min) 
        if df is not None:
            predictions_dict[file_name] = df
    return predictions_dict

def convert_European_time(data, time_zone):
    data.index = pd.to_datetime(data.index, utc=True)
    data.index = data.index.tz_convert(time_zone)
    data.index = data.index.tz_localize(None)
    return data

def simplify_model_names(df):
    # Define the mapping of complex names to simpler ones
    replacements = {
        r'\.LightGBMModel\.\dD\.TimeCov\.Temp\.Forecast_elia': '.LightGBM_with_Forecast_elia',
        r'\.LightGBMModel\.\dD\.TimeCov\.Temp': '.LightGBM',
        r'\.Naive\.\dD': '.Naive',
    }
    
    # Apply the replacements
    for original, simplified in replacements.items():
        df.columns = df.columns.str.replace(original, simplified, regex=True)
    
    return df

def simplify_model_names_in_index(df):
    # Define the mapping of complex names to simpler ones
    replacements = {
        r'\.LightGBMModel\.\dD\.TimeCov\.Temp\.Forecast_elia': '.LightGBM_with_Forecast_elia',
        r'\.LightGBMModel\.\dD\.TimeCov\.Temp': '.LightGBM',
        r'\.Naive\.\dD': '.Naive',
    }

    # Apply the replacements to the DataFrame index
    for original, simplified in replacements.items():
        df.index = df.index.str.replace(original, simplified, regex=True)
    
    return df

github_token = st.secrets["GitHub_Token_KUL_Margarida"]

if github_token:
    hour, after_10_min=get_current_time()
    forecast_dict = load_forecast(github_token, hour, after_10_min)

    historical_forecast=load_GitHub(github_token, 'Historical_forecast.csv', hour, after_10_min)

    Data_BE=load_GitHub(github_token, 'BE_Elia_Entsoe_UTC.csv', hour, after_10_min)
    Data_FR=load_GitHub(github_token, 'FR_Entsoe_UTC.csv', hour, after_10_min)
    Data_NL=load_GitHub(github_token, 'NL_Entsoe_UTC.csv', hour, after_10_min)
    Data_DE=load_GitHub(github_token, 'DE_Entsoe_UTC.csv', hour, after_10_min)
    
    Data_BE=convert_European_time(Data_BE, 'Europe/Brussels')
    Data_FR=convert_European_time(Data_FR, 'Europe/Paris')
    Data_NL=convert_European_time(Data_NL, 'Europe/Amsterdam')
    Data_DE=convert_European_time(Data_DE, 'Europe/Berlin')


else:
    print("Please enter your GitHub Personal Access Token to proceed.")


# Main layout of the app
col1, col2 = st.columns([5, 2])  # Adjust the ratio to better fit your layout needs
with col1:
    st.title("Transparency++")

with col2:
    upper_space = col2.empty()
    upper_space = col2.empty()
    col2_1, col2_2 = st.columns(2)  # Create two columns within the right column for side-by-side images
    with col2_1:
        st.image("KU_Leuven_logo.png", width=100)   # Adjust the path and width as needed
    with col2_2:
        st.image("energyville_logo.png", width=100) 

upper_space.markdown("""
   
   
""", unsafe_allow_html=True)



countries = {
    'Overall': 'Overall',
    'Netherlands': 'NL',
    'Germany': 'DE',
    'France': 'FR',
    'Belgium': 'BE',
}


st.sidebar.header('Filters')

st.sidebar.subheader("Select Country")
st.sidebar.caption("Choose the country for which you want to display data or forecasts.")

selected_country = st.sidebar.selectbox('Select Country', list(countries.keys()))

# Ensure the date range provides two dates


# Sidebar with radio buttons for different sections
if selected_country != 'Overall':
    st.sidebar.subheader("Section")
    st.sidebar.caption("Select the type of information you want to explore.")
    section = st.sidebar.radio('', ['Data Quality', 'Forecasts Quality', 'Insights'], index=1)
else:
    section = None  # No section is shown when "Overall" is selected

if selected_country == 'Overall':
    data = None  # You can set data to None or a specific dataset based on your logic
    section = None  # No section selected when "Overall" is chosen
else:
    country_code = countries[selected_country]
    if country_code == 'BE':
        data = Data_BE
        weather_columns = ['Temperature', 'Wind Speed Onshore', 'Wind Speed Offshore']
        data['Temperature'] = data['temperature_2m_8']
        data['Wind Speed Offshore'] = data['wind_speed_100m_4']
        data['Wind Speed Onshore'] = data['wind_speed_100m_8']

    elif country_code == 'DE':
        data = Data_DE
        weather_columns = ['Temperature', 'Wind Speed']
        data['Temperature'] = data['temperature_2m']
        data['Wind Speed'] = data['wind_speed_100m']

    elif country_code == 'NL':
        data = Data_NL
        weather_columns = ['Temperature', 'Wind Speed']
        data['Temperature'] = data['temperature_2m']
        data['Wind Speed'] = data['wind_speed_100m']

    elif country_code == 'FR':
        data = Data_FR
        weather_columns = ['Temperature', 'Wind Speed']
        data['Temperature'] = data['temperature_2m']
        data['Wind Speed'] = data['wind_speed_100m']

def add_feature(df2, df_main):
    #df_main.index = pd.to_datetime(df_main.index)
    #df2.index = pd.to_datetime(df2.index)
    df_combined = df_main.combine_first(df2)
    last_date_df1 = df_main.index.max()
    first_date_df2 = df2.index.min()
    if first_date_df2 == last_date_df1 + pd.Timedelta(hours=1):
        df_combined = pd.concat([df_main, df2[df2.index > last_date_df1]], axis=0)
    #df_combined.reset_index(inplace=True)
    return df_combined
#data.index = data.index.tz_localize('UTC')


forecast_columns = [
    'Load_entsoe','Load_forecast_entsoe','Wind_onshore_entsoe','Wind_onshore_forecast_entsoe','Wind_offshore_entsoe','Wind_offshore_forecast_entsoe','Solar_entsoe','Solar_forecast_entsoe']

if section == 'Data Quality':
   
    st.header('Data Quality')
    
    st.write('The table below presents the data quality metrics focusing on the percentage of missing values and the occurrence of extreme or nonsensical values for the selected country.')

    yesterday_midnight = pd.Timestamp(datetime.datetime.now().date() - pd.Timedelta(days=1)).replace(hour=23, minute=59, second=59)

    # Filter data until the end of yesterday (midnight)
    data_quality = data[data.index <= yesterday_midnight]

    # Report % of missing values
    missing_values = data_quality[forecast_columns].isna().mean() * 100
    missing_values = missing_values.round(2)

    installed_capacities = {
        'FR': { 'Solar': 17419, 'Wind Offshore': 1483, 'Wind Onshore': 22134},
        'DE': { 'Solar': 73821, 'Wind Offshore': 8386, 'Wind Onshore': 59915},
        'BE': { 'Solar': 8789, 'Wind Offshore': 2262, 'Wind Onshore': 3053},  
        'NL': { 'Solar': 22590, 'Wind Offshore': 3220, 'Wind Onshore': 6190},  
    }

    if country_code not in installed_capacities:
        st.error(f"Installed capacities not defined for country code '{country_code}'.")
        st.stop()


    # Report % of extreme, impossible values for the selected country
    capacities = installed_capacities[country_code]
    extreme_values = {}

    for col in forecast_columns:
            if 'Solar_entsoe' in col:
                extreme_values[col] = ((data_quality[col] < 0) | (data_quality[col] > capacities['Solar'])).mean() * 100
            elif 'Solar_forecast_entsoe' in col:
                extreme_values[col] = ((data_quality[col] < 0) | (data_quality[col] > capacities['Solar'])).mean() * 100
            elif 'Wind_onshore_entsoe' in col:
                extreme_values[col] = ((data_quality[col] < 0) | (data_quality[col] > capacities['Wind Onshore'])).mean() * 100
            elif 'Wind_onshore_forecast_entsoe' in col:
                extreme_values[col] = ((data_quality[col] < 0) | (data_quality[col] > capacities['Wind Onshore'])).mean() * 100
            elif 'Wind_offshore_entsoe' in col:
                extreme_values[col] = ((data_quality[col] < 0) | (data_quality[col] > capacities['Wind Offshore'])).mean() * 100
            elif 'Wind_offshore_forecast_entsoe' in col:
                extreme_values[col] = ((data_quality[col] < 0) | (data_quality[col] > capacities['Wind Offshore'])).mean() * 100
            elif 'Load_entsoe' in col:
                extreme_values[col] = ((data_quality[col] < 0)).mean() * 100
            elif 'Load_forecast_entsoe' in col:
                extreme_values[col] = ((data_quality[col] < 0)).mean() * 100


    extreme_values = pd.Series(extreme_values).round(2)

    # Combine all metrics into one DataFrame
    metrics_df = pd.DataFrame({
    'Missing Values (%)': missing_values,
    'Extreme/Nonsensical Values (%)': extreme_values,
    })

    st.markdown(
    """
    <style>
    .dataframe {font-size: 45px !important;}
    </style>
    """,
    unsafe_allow_html=True
    )

    st.dataframe(metrics_df)

    st.write('<b><u>Missing values (%)</u></b>: Percentage of missing values in the dataset', unsafe_allow_html=True)
    st.write('<b><u>Extreme/Nonsensical values (%)</u></b>: Values that are considered implausible such as negative or out-of-bound values i.e., (generation<0) or (generation>capacity)', unsafe_allow_html=True)

# Section 2: Forecasts
elif section == 'Forecasts Quality':
   
    st.header('Forecast Quality')
    
    # Time series for last 1 week
    last_week = data.loc[data.index >= (data.index[-1] - pd.Timedelta(days=7))]
    st.write('The below plot shows the time series of forecasts vs. observations provided by the ENTSO-E Transparency platform from the past week.')
    
    # Options for selecting the data to display
    variable_options = {
        "Load": ("Load_entsoe", "Load_forecast_entsoe"),
        "Solar": ("Solar_entsoe", "Solar_forecast_entsoe"),
        "Wind Onshore": ("Wind_onshore_entsoe", "Wind_onshore_forecast_entsoe"),
        "Wind Offshore": ("Wind_offshore_entsoe", "Wind_offshore_forecast_entsoe")
    }
    
    # Dropdown to select the variable
    selected_variable = st.selectbox("Select Variable for Line PLot", list(variable_options.keys()))

    # Get the corresponding columns for the selected variable
    actual_col, forecast_col = variable_options[selected_variable]

    # Plot only the selected variable's data
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=last_week.index, y=last_week[actual_col], mode='lines', name='Actual'))
    fig.add_trace(go.Scatter(x=last_week.index, y=last_week[forecast_col], mode='lines', name='Forecast ENTSO-E'))
    fig.update_layout(title=f'Forecasts vs Actual for {selected_variable}', xaxis_title='Date', yaxis_title='Value [MW]')
    
    st.plotly_chart(fig)

    # Scatter plots for error distribution
    st.subheader('Error Distribution')
    st.write('The below scatter plots show the error distribution of all four fields: Solar, Wind Onshore, Wind Offshore and Load.')
    selected_variable = st.selectbox("Select Variable for Error Distribution", list(variable_options.keys()))

    # Get the corresponding columns for the selected variable
    actual_col, forecast_col = variable_options[selected_variable]

    # Filter data for the selected year and check if columns are available
    data_2024 = data[data.index.year > 2023]
    if forecast_col in data_2024.columns:
        obs = data_2024[actual_col]
        pred = data_2024[forecast_col]
        
        # Calculate error and plot
        error = pred - obs 
        fig = px.scatter(x=obs, y=pred, labels={'x': 'Observed [MW]', 'y': 'Forecast ENTSO-E [MW]'})
        fig.update_layout(title=f'Error Distribution for {selected_variable}')
        
        st.plotly_chart(fig)

    
        
    st.subheader('Accuracy Metrics (Sorted by rMAE):')


    date_range = st.date_input(
        "Select Date Range for Metrics Calculation:",
        value=(pd.to_datetime("2024-01-01"), pd.to_datetime(pd.Timestamp('today')))
    )

    if len(date_range) == 2:
        start_date = pd.Timestamp(date_range[0])
        end_date = pd.Timestamp(date_range[1])
    else:
        st.error("Please select a valid date range.")
        st.stop()


    output_text = f"The below metrics are calculated from the selected date range from {start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}. "
    st.write(output_text)
    
    data = data.loc[start_date:end_date]
    accuracy_metrics = pd.DataFrame(columns=['MAE', 'rMAE'], index=['Load', 'Solar', 'Wind Onshore', 'Wind Offshore'])

    for i in range(0, len(forecast_columns), 2):
        actual_col = forecast_columns[i]
        forecast_col = forecast_columns[i + 1]
        if forecast_col in data.columns:
            obs = data[actual_col]
            pred = data[forecast_col]
            error = pred - obs
            
            mae = round(np.mean(np.abs(error)),2)
            if 'Load' in actual_col:
                persistence = obs.shift(168)  # Weekly persistence
            else:
                persistence = obs.shift(24)  # Daily persistence
            
            # Using the whole year's data for rMAE calculations
            rmae = round(mae / np.mean(np.abs(obs - persistence)),2)
            
            row_label = 'Load' if 'Load' in actual_col else 'Solar' if 'Solar' in actual_col else 'Wind Offshore' if 'Wind_offshore' in actual_col else 'Wind Onshore'
            accuracy_metrics.loc[row_label] = [mae, rmae]

    accuracy_metrics.dropna(how='all', inplace=True)# Sort by rMAE (second column)
    accuracy_metrics.sort_values(by=accuracy_metrics.columns[1], ascending=True, inplace=True)
    accuracy_metrics = accuracy_metrics.round(4)

    col1, col2 = st.columns([1, 2])

    with col1:
        st.markdown(
            """
            <style>
            .small-chart {
                margin-top: 30px;  /* Adjust this value as needed */
            }
            </style>
            """,
            unsafe_allow_html=True
        )
        st.dataframe(accuracy_metrics)
        st.markdown(
            """
            <style>
            .small-chart {
                margin-top: -30px;  /* Adjust this value as needed */
            }
            </style>
            """,
            unsafe_allow_html=True
        )

    with col2:
        
        # Prepare data for the radar chart
        rmae_values = accuracy_metrics['rMAE'].tolist()
        categories = accuracy_metrics.index.tolist()
        
        fig = go.Figure()
        fig.add_trace(go.Scatterpolar(
            r=rmae_values,
            theta=categories,
            fill='toself',
            name='rMAE'
        ))
        
        # Configuring radar chart layout to be smaller
        fig.update_layout(
            width=250,  # Adjust width
            height=250,  # Adjust height
            margin=dict(t=20, b=20, l=0, r=0),  # Remove all margins
            polar=dict(
                radialaxis=dict(
                    visible=True,
                    range=[0, max(rmae_values) * 1.2]  # Adjust range dynamically
                )),
            showlegend=False
        )
        
        # Apply CSS class to remove extra space above chart
        st.plotly_chart(fig, use_container_width=True, config={'displayModeBar': False}, className="small-chart")

    st.subheader('ACF plots of Errors')
    st.write('The below plots show the ACF (Auto-Correlation Function) for the errors of all three data fields obtained from ENTSO-E: Solar, Wind Onshore, Wind Offshore and Load.')

    # Dropdown to select the variable
    selected_variable = st.selectbox("Select Variable for ACF of Errors", list(variable_options.keys()))

    # Get the corresponding columns for the selected variable
    actual_col, forecast_col = variable_options[selected_variable]

    # Calculate the error and plot ACF if columns are available
    if forecast_col in data.columns:
        obs = data[actual_col]
        pred = data[forecast_col]
        error = pred - obs

        st.write(f"**ACF of Errors for {selected_variable}**")
        fig, ax = plt.subplots(figsize=(10, 5))
        plot_acf(error.dropna(), ax=ax)
        st.pyplot(fig)

        # Optionally calculate and store ACF values for further analysis if needed
        acf_values = acf(error.dropna(), nlags=240)
        
# Section 3: Insights
elif section == 'Insights':
    st.header("Insights")

    st.write('The scatter plots below are created to explore possible correlations between the data fields: Solar, Wind Onshore, Wind Offshore, Load, and Weather Features.')
    # Add a selection box for the data resolution (weekly, daily, hourly)
    data_2024 = data[data.index.year == 2024]

    resolution = st.selectbox('Select data resolution:', ['Daily', 'Hourly'])

        # Resample data based on the selected resolution
    if resolution == 'Hourly':
        resampled_data = data_2024
    elif resolution == 'Daily':
        resampled_data = data_2024.resample('D').mean()  # Resample to daily mean

    # Select the necessary columns for the scatter plot
    selected_columns = ['Load_entsoe', 'Solar_entsoe', 'Wind_offshore_entsoe', 'Wind_onshore_entsoe'] + weather_columns
    selected_df = resampled_data[selected_columns]
    selected_df.columns = [col.replace('_entsoe', '').replace('_', ' ') for col in selected_df.columns]

    # Drop missing values
    selected_df = selected_df.dropna()

    # Create the scatter plots using seaborn's pairplot
    sns.set_theme(style="ticks")
    pairplot_fig = sns.pairplot(selected_df)

    # Display the pairplot in Streamlit
    st.pyplot(pairplot_fig)

elif selected_country == 'Overall':

    st.subheader("Net Load Error Map")
    st.write("""
        The net load error map highlights the error in the forecasted versus actual net load for each country. 
        Hover over each country to see details on the latest net load error and the timestamp of the last recorded data.
    """)
    
    def plot_net_load_error_map(data_dict):
        # Define forecast columns used in calculation

        def calculate_net_load_error(df):
            filter_df = df[forecast_columns].dropna()
            net_load = filter_df['Load_entsoe'] - filter_df['Wind_onshore_entsoe'] - filter_df['Wind_offshore_entsoe'] - filter_df['Solar_entsoe']
            net_load_forecast = filter_df['Load_forecast_entsoe'] - filter_df['Wind_onshore_forecast_entsoe'] - filter_df['Wind_offshore_forecast_entsoe'] - filter_df['Solar_forecast_entsoe']
            error = (net_load_forecast - net_load).iloc[-1]
            date = filter_df.index[-1].strftime("%Y-%m-%d %H:%M")  # Get the latest date in string format
            return error, date

        # Calculate net load errors and dates for each country
        net_load_errors = {country_name: calculate_net_load_error(data) for country_name, data in data_dict.items()}

        # Create DataFrame for Folium with additional date column
        df_net_load_error = pd.DataFrame({
            'country': list(net_load_errors.keys()),
            'net_load_error': [v[0] for v in net_load_errors.values()],
            'date': [v[1] for v in net_load_errors.values()]
        })

        # Load the GeoJSON file
        geojson_url = "https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/world-countries.json"
        geo_data = requests.get(geojson_url).json()

        # Filter GeoJSON to only include the selected countries
        selected_countries = list(data_dict.keys())  # Get the list of selected countries (Belgium, France, Germany, Netherlands)
        filtered_geojson = {
            "type": "FeatureCollection",
            "features": [feature for feature in geo_data["features"] if feature["properties"]["name"] in selected_countries]
        }

        # Merge the geojson with the error and date data
        for feature in filtered_geojson["features"]:
            country_name = feature["properties"]["name"]
            row = df_net_load_error[df_net_load_error['country'] == country_name]
            if not row.empty:
                feature["properties"]["net_load_error"] = row.iloc[0]["net_load_error"]
                feature["properties"]["date"] = row.iloc[0]["date"]

        # Initialize the Folium map centered on Central Europe
        m = folium.Map(location=[51, 10], zoom_start=5, tiles="cartodb positron")

        # Add choropleth layer to map net load errors by country
        folium.Choropleth(
            geo_data=filtered_geojson,
            name="choropleth",
            data=df_net_load_error,
            columns=["country", "net_load_error"],
            key_on="feature.properties.name",
            fill_color="RdYlBu",  # Use a more vibrant color palette
            fill_opacity=0.7,
            line_opacity=0.5,
            line_color="black",  # Neutral border color
            legend_name="Net Load Error [MW]"
        ).add_to(m)

        # Add a GeoJson layer with custom tooltip for country, error, and date
        folium.GeoJson(
            filtered_geojson,
            style_function=lambda x: {'fillOpacity': 0, 'color': 'black', 'weight': 0},
            tooltip=folium.GeoJsonTooltip(
                fields=["name", "net_load_error", "date"],
                aliases=["Country:", "Net Load Error [MW]:", "Date:"],
                localize=True
            )
        ).add_to(m)

        # Display Folium map in Streamlit
        st_folium(m, width=700, height=600)

    # Data dictionary with full country names
    data_dict = {
        'Belgium': Data_BE,
        'France': Data_FR,
        'Germany': Data_DE,
        'Netherlands': Data_NL
    }
    plot_net_load_error_map(data_dict)

    st.subheader("rMAE of Forecasts published on ENTSO-E TP")
    st.write("""The rMAE of Forecasts chart compares the forecast accuracy of the predictions published by ENTSO-E Transparency Platform for Belgium, Germany, France, and the Netherlands. It shows the rMAE for onshore wind, offshore wind, solar, and load demand, highlighting how well forecasts perform relative to a basic persistence model across these countries and energy sectors.""")

    def calculate_mae(actual, forecast):
        return np.mean(np.abs(actual - forecast))

    # Function to calculate persistence MAE
    def calculate_persistence_mae(data, shift_hours):
        return np.mean(np.abs(data - data.shift(shift_hours)))

    # Function to calculate rMAE for each country
    def calculate_rmae_for_country(df):
        rmae = {}
        rmae['Load'] = calculate_mae(df['Load_entsoe'], df['Load_forecast_entsoe']) / calculate_persistence_mae(df['Load_entsoe'], 168)
        rmae['Wind_onshore'] = calculate_mae(df['Wind_onshore_entsoe'], df['Wind_onshore_forecast_entsoe']) / calculate_persistence_mae(df['Wind_onshore_entsoe'], 24)
        rmae['Wind_offshore'] = calculate_mae(df['Wind_offshore_entsoe'], df['Wind_offshore_forecast_entsoe']) / calculate_persistence_mae(df['Wind_offshore_entsoe'], 24)
        rmae['Solar'] = calculate_mae(df['Solar_entsoe'], df['Solar_forecast_entsoe']) / calculate_persistence_mae(df['Solar_entsoe'], 24)
        return rmae

    # Function to create rMAE DataFrame
    def create_rmae_dataframe(data_dict):
        rmae_values = {'Country': [], 'Load': [], 'Wind_onshore': [], 'Wind_offshore': [], 'Solar': []}
        for country_name, df in data_dict.items():
            df_filtered = df[forecast_columns].dropna()
            rmae = calculate_rmae_for_country(df_filtered)
            rmae_values['Country'].append(country_name)
            for key in rmae:
                rmae_values[key].append(rmae[key])
        return pd.DataFrame(rmae_values)

    # Function to plot radar chart
    def plot_rmae_radar_chart(rmae_df):
        fig = go.Figure()
        angles = ['Load', 'Wind_onshore', 'Wind_offshore', 'Solar']
        for _, row in rmae_df.iterrows():
            fig.add_trace(go.Scatterpolar(r=[row[angle] for angle in angles], theta=angles, fill='toself', name=row['Country']))
        fig.update_layout(polar=dict(radialaxis=dict(visible=True, range=[0, 1.2])), showlegend=True, title="rMAE Radar Chart by Country")
        st.plotly_chart(fig)

    # Main execution to create and display radar plot
    rmae_df = create_rmae_dataframe(data_dict)
    plot_rmae_radar_chart(rmae_df)