zarahmer/Load-Forecasting

1import gradio as gr2import pandas as pd3from prophet import Prophet4import plotly.graph_objs as go5import plotly.io as pio6pio.renderers.default = 'colab'7 8# Load and prepare data (replace with your own if needed)9df = pd.read_csv("https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv")10df = df.rename(columns={"Month": "ds", "Passengers": "y"})11df['ds'] = pd.to_datetime(df['ds'])12 13# Train the model once14model = Prophet()15model.fit(df)16 17def forecast_with_plots(months_ahead):18    # Create future dataframe and predict19    future = model.make_future_dataframe(periods=months_ahead, freq='M')20    forecast = model.predict(future)21 22    # Find peak demand in forecast period (last months_ahead rows)23    forecast_period = forecast.tail(months_ahead)24    peak_idx = forecast_period['yhat'].idxmax()25    peak_date = forecast_period.loc[peak_idx, 'ds'].strftime('%Y-%m-%d')26    peak_value = forecast_period.loc[peak_idx, 'yhat']27 28    # Build Plotly figure manually29    fig = go.Figure()30 31    # Original data points32    fig.add_trace(go.Scatter(33        x=df['ds'], y=df['y'],34        mode='markers',35        name='Historical Data',36        marker=dict(color='black')37    ))38 39    # Forecast line40    fig.add_trace(go.Scatter(41        x=forecast['ds'], y=forecast['yhat'],42        mode='lines',43        name='Forecast',44        line=dict(color='blue')45    ))46 47    # Confidence interval area (yhat_lower to yhat_upper)48    fig.add_trace(go.Scatter(49        x=list(forecast['ds']) + list(forecast['ds'][::-1]),50        y=list(forecast['yhat_upper']) + list(forecast['yhat_lower'][::-1]),51        fill='toself',52        fillcolor='rgba(0, 0, 255, 0.2)',53        line=dict(color='rgba(255,255,255,0)'),54        hoverinfo="skip",55        showlegend=True,56        name='Confidence Interval'57    ))58 59    # Highlight peak demand point60    fig.add_trace(go.Scatter(61        x=[peak_date],62        y=[peak_value],63        mode='markers+text',64        marker=dict(color='red', size=12, symbol='star'),65        text=[f"Peak: {peak_date}<br>{peak_value:.1f}"],66        textposition="top center",67        name='Peak Demand'68    ))69 70    fig.update_layout(71        title="Energy Load Forecast",72        xaxis_title="Date",73        yaxis_title="Load",74        hovermode="x unified"75    )76 77    # Return figure and peak info string78    peak_info = f"📅 Peak demand predicted on: {peak_date} with value: {peak_value:.1f}"79    return fig, peak_info80 81# Gradio interface with two outputs: plot and text82iface = gr.Interface(83    fn=forecast_with_plots,84    inputs=gr.Slider(minimum=1, maximum=36, step=1, label="Months Ahead to Forecast"),85    outputs=[gr.Plot(label="Forecast Plot"), gr.Textbox(label="Peak Demand Info")],86    title="Energy Load Forecasting with Prophet",87    description="Select how many months ahead you want to forecast."88)89 90iface.launch()91