app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objs as go
import requests
from io import StringIO

# Function to load predictions from a GitHub repo
@st.cache
def load_data_predictions(github_token):
    # GitHub API URL for the CSV file
    url = 'https://api.github.com/repos/mmmapms/Forecast_DAM_V2/contents/Predictions.csv'
    headers = {'Authorization': f'token {github_token}'}
    
    # Make a GET request to the GitHub API
    response = requests.get(url, headers=headers)
    
    if response.status_code == 200:
        # GitHub API response contains the file content in base64 encoding
        file_content = response.json()['content']
        csv_content = StringIO(file_content)
        
        df = pd.read_csv(csv_content, encoding='utf-8')
        # Your renaming and preprocessing steps here
        df = df.rename(columns={...})  # Your column renaming logic
        df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
        df_filtered = df.dropna(subset=['Real Price'])
        return df, df_filtered
    else:
        st.error("Failed to download data. Please check your GitHub token and repository details.")
        return pd.DataFrame(), pd.DataFrame()

# Streamlit app UI for token input
st.title("Belgium: Electricity Price Forecasting")
github_token = st.secrets["GitHub_Token_Margarida"]

if github_token:
    df, df_filtered = load_data_predictions(github_token)
    # Your existing logic to use df and df_filtered
else:
    st.warning("Please enter your GitHub Personal Access Token to proceed.")


#@st.cache_data
#def load_data_predictions():
#    df = pd.read_csv('Predictions.csv')
#    df = df.rename(columns={
#    'Price': 'Real Price',
#    'DNN1': 'Neural Network 1',
#    'DNN2': 'Neural Network 2',
#    'DNN3': 'Neural Network 3',
#    'DNN4': 'Neural Network 4',
#    'DNN_Ensemble': 'Neural Network Ensemble',
#    'LEAR56': 'Regularized Linear Model 1',
#    'LEAR84': 'Regularized Linear Model 2',
#    'LEAR112': 'Regularized Linear Model 3',
#    'LEAR730': 'Regularized Linear Model 4',
#    'LEAR_Ensemble': 'Regularized Linear Model Ensemble',
#    'Persis': 'Persistence Model',
#    'Hybrid_Ensemble': 'Hybrid Ensemble'
#})
#    df['Date'] = pd.to_datetime(df['Date'], dayfirst=True)
#    df_filtered = df.dropna(subset=['Real Price'])
#   return df, df_filtered

#df, df_filtered = load_data_predictions()

min_date_allowed_pred = df_filtered['Date'].min().date()
max_date_allowed_pred = df_filtered['Date'].max().date()

end_date = df['Date'].max().date()
start_date = end_date - pd.Timedelta(days=7)

models_corr_matrix = ['Neural Network 1', 'Neural Network 2', 'Neural Network 3', 
               'Neural Network 4', 'Regularized Linear Model 1', 
               'Regularized Linear Model 2', 'Regularized Linear Model 3', 
               'Regularized Linear Model 4']

st.title("Belgium: Electricity Price Forecasting")

# Sidebar for inputs
with st.sidebar:
    st.write("### Variables Selection for Graph")
    st.write("Select which variables you'd like to include in the graph. This will affect the displayed charts and available data for download.")
    selected_variables = st.multiselect("Select variables to display:", options=['Real Price', 'Neural Network 1', 'Neural Network 2', 'Neural Network 3', 'Neural Network 4', 'Neural Network Ensemble', 'Regularized Linear Model 1', 'Regularized Linear Model 2','Regularized Linear Model 3', 'Regularized Linear Model 4', 'Regularized Linear Model Ensemble', 'Hybrid Ensemble', 'Persistence Model'], default=['Real Price', 'Neural Network Ensemble', 'Regularized Linear Model Ensemble', 'Persistence Model'])
    st.write("### Model Selection for Scatter Plot")
    model_selection = st.selectbox("Select which model's predictions to display:", options=['Neural Network 1', 'Neural Network 2', 'Neural Network 3', 'Neural Network 4', 'Neural Network Ensemble', 'Regularized Linear Model 1', 'Regularized Linear Model 2','Regularized Linear Model 3', 'Regularized Linear Model 4', 'Regularized Linear Model Ensemble', 'Hybrid Ensemble', 'Persistence Model'], index=10) # Adjust the index as needed to default to your desired option

    st.write("### Date Range for Metrics Calculation")
    st.write("Select the date range to calculate the metrics for the predictions. This will influence the accuracy metrics displayed below. The complete dataset ranges from 10/03/2024 until today.")
    start_date_pred, end_date_pred = st.date_input("Select Date Range for Metrics Calculation:", [min_date_allowed_pred, max_date_allowed_pred])

# Main content
if not selected_variables:
    st.warning("Please select at least one variable to display.")
else:
    # Plotting
    st.write("## Belgian Day-Ahead Electricity Prices")
    temp_df = df[(df['Date'] >= pd.Timestamp(start_date))] #& (df['Date'] <= pd.Timestamp(end_date))]
    fig = go.Figure()

    for variable in selected_variables:
        fig.add_trace(go.Scatter(x=temp_df['Date'], y=temp_df[variable], mode='lines', name=variable))

    fig.update_layout(xaxis_title="Date", yaxis_title="Price [EUR/MWh]")
    st.plotly_chart(fig, use_container_width=True)
    st.write("The graph presented here illustrates the day-ahead electricity price forecasts for Belgium, covering the period from one week ago up to tomorrow. It incorporates predictions from three distinct models: a Neural Network, a Regularized Linear Model, and Persistence, alongside the actual electricity prices up until today.")


if not selected_variables:
    st.warning("Please select at least one variable to display.")
else:
    # Plotting
    st.write("## Scatter Plot: Real Price vs Model Predictions")
    # Filter based on the selected date range for plotting
    plot_df = df[(df['Date'] >= pd.Timestamp(min_date_allowed_pred)) & (df['Date'] <= pd.Timestamp(max_date_allowed_pred))]
    
    model_column = model_selection

    # Create the scatter plot
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=plot_df['Real Price'], y=plot_df[model_column], mode='markers', name=f"Real Price vs {model_selection} Predictions"))

    # Calculate the line of best fit
    m, b = np.polyfit(plot_df['Real Price'], plot_df[model_column], 1)
    # Calculate the y-values based on the line of best fit
    regression_line = m * plot_df['Real Price'] + b

    # Format the equation to display as the legend name
    equation = f"y = {m:.2f}x + {b:.2f}"

    # Add the line of best fit to the figure with the equation as the legend name
    fig.add_trace(go.Scatter(x=plot_df['Real Price'], y=regression_line, mode='lines', name=equation, line=dict(color='black')))

    # Update layout with appropriate titles
    fig.update_layout(xaxis_title="Real Price [EUR/MWh]", yaxis_title=f"{model_selection} Predictions [EUR/MWh]", title=f"Scatter Plot of Real Price vs {model_selection} Predictions from {min_date_allowed_pred} to {max_date_allowed_pred}")
    st.plotly_chart(fig, use_container_width=True)


# Calculating and displaying metrics
if start_date_pred and end_date_pred:
    st.header("Accuracy Metrics")
    #st.write(f"The accuracy metrics are calculated from {start_date_pred} to {end_date_pred}, this intervale can be changed in the sidebar.")
    st.write(f"The accuracy metrics are calculated from **{start_date_pred}** to **{end_date_pred}**. This interval can be changed in the sidebar. Evaluate the forecasting accuracy of our models with key performance indicators. The table summarizes the Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Root Mean Square Error (RMSE) for the selected models over your selected date range. Lower values indicate higher precision and reliability of the forecasts.")
    filtered_df = df_filtered[(df_filtered['Date'] >= pd.Timestamp(start_date_pred)) & (df_filtered['Date'] <= pd.Timestamp(end_date_pred))]
    
    # List of models for convenience
    models = [
        'Neural Network 1', 'Neural Network 2', 'Neural Network 3', 'Neural Network 4', 'Neural Network Ensemble',
        'Regularized Linear Model 1', 'Regularized Linear Model 2', 'Regularized Linear Model 3', 'Regularized Linear Model 4', 'Regularized Linear Model Ensemble',
        'Persistence Model', 'Hybrid Ensemble'
    ]
    
    # Placeholder for results
    results = {'Metric': ['MAE', 'sMAPE', 'RMSE', 'rMAE']}
    
    p_real = filtered_df['Real Price']
    
    # Iterate through each model to calculate and store metrics
    for model in models:
        # Assuming column names in filtered_df match the model names directly for simplicity
        p_pred = filtered_df[model]
        
        mae = np.mean(np.abs(p_real - p_pred))
        smape = 100 * np.mean(np.abs(p_real - p_pred) / ((np.abs(p_real) + np.abs(p_pred)) / 2))
        rmse = np.sqrt(np.mean((p_real - p_pred) ** 2))
        rmae = mae/np.mean(np.abs(p_real - filtered_df['Persistence Model']))

        # Store the results
        results[model] = [f"{mae:.2f}", f"{smape:.2f}%", f"{rmse:.2f}", f"{rmae:.2f}"]
    
    # Convert the results to a DataFrame for display
    metrics_df = pd.DataFrame(results)
    
    transposed_metrics_df = metrics_df.set_index('Metric').T
    col1, col2 = st.columns([3, 2])

    # Display the transposed DataFrame
    with col1:
        # Assuming 'transposed_metrics_df' is your final DataFrame with metrics
        st.dataframe(transposed_metrics_df,  hide_index=False)

with col2:
    st.markdown("""
        <style>
        .big-font {
            font-size: 20px;
            font-weight: 500;
        }
        </style>
        <div class="big-font">
        Equations
        </div>
        """, unsafe_allow_html=True)
    
    # Rendering LaTeX equations
    st.markdown(r"""
    $\text{MAE} = \frac{1}{n}\sum_{i=1}^{n}|y_i - \hat{y}_i|$
    

    $\text{sMAPE} =100\frac{1}{n} \sum_{i=1}^{n} \frac{|y_i - \hat{y}_i|}{\left(|y_i| + |\hat{y}_i|\right)/2}$
    
                
    $\text{RMSE} = \sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_i - \hat{y}_i\right)^2}$
    
                
    $\text{rMAE} = \frac{\text{MAE}}{MAE_{\text{Persistence Model}}}$
    """)


st.write("## Correlation Matrix")


models_df = df_filtered[models_corr_matrix]
corr_matrix = models_df.corr()

fig = go.Figure(data=go.Heatmap(
    z=corr_matrix.values,
    x=corr_matrix.columns,
    y=corr_matrix.index))
fig.update_layout(
    yaxis_autorange='reversed'  # Ensure the y-axis starts from the top
)
st.plotly_chart(fig, use_container_width=True)

st.write("## Access Predictions")
st.write("If you are interested in accessing the predictions made by the models, please contact Margarida Mascarenhas (KU Leuven PhD Student) at margarida.mascarenhas@kuleuven.be")