Update app.py

app.py

@@ -1,83 +1,98 @@
-def forecast_energy(days, options):
-    from prophet import Prophet
-    import pandas as pd
-    import plotly.graph_objects as go
+# Imports
+import pandas as pd
+import matplotlib.pyplot as plt
+from prophet import Prophet
+import gradio as gr
+
+# Load and preprocess data
+url = 'https://data.open-power-system-data.org/time_series/2020-10-06/time_series_60min_singleindex.csv'
+data = pd.read_csv(url)
+
+df = data[['utc_timestamp', 'DE_load_actual_entsoe_transparency']].copy()
+df.rename(columns={'utc_timestamp': 'ds', 'DE_load_actual_entsoe_transparency': 'y'}, inplace=True)
+df.dropna(inplace=True)
+df['ds'] = pd.to_datetime(df['ds']).dt.tz_localize(None)
 
+# Forecast function with dropdown support
+def forecast_energy(days, graph_type):
     model = Prophet()
     model.fit(df)
 
-    future = model.make_future_dataframe(periods=days * 24, freq='H')
+    future = model.make_future_dataframe(periods=24 * days, freq='H')
     forecast = model.predict(future)
-    forecast_tail = forecast[-days * 24:]
-
-    peak_row = forecast_tail.loc[forecast_tail['yhat'].idxmax()]
-    peak_time = peak_row['ds']
-    peak_value = peak_row['yhat']
+    forecast_future = forecast.tail(24 * days)
 
-    fig = go.Figure()
+    # Paths to store each plot
+    paths = {}
 
-    # Conditionally add traces based on user selection
-    if "Actual Load" in options:
-        fig.add_trace(go.Scatter(
-            x=df['ds'], y=df['y'], mode='lines', name='Actual Load', line=dict(color='black')))
+    # Forecast Plot
+    plt.figure(figsize=(12, 5))
+    plt.plot(forecast_future['ds'], forecast_future['yhat'], label='Predicted Load (MW)', color='blue')
+    plt.fill_between(forecast_future['ds'], forecast_future['yhat_lower'], forecast_future['yhat_upper'], color='skyblue', alpha=0.3, label='Confidence Interval')
+    plt.xticks(rotation=45)
+    plt.xlabel("Date")
+    plt.ylabel("Load (MW)")
+    plt.title(f"Forecasted Load (Next {days} Days)")
+    plt.legend()
+    plt.tight_layout()
+    paths["Forecast Plot"] = f"forecast_plot_{days}.png"
+    plt.savefig(paths["Forecast Plot"])
+    plt.close()
 
-    if "Forecast Line" in options:
-        fig.add_trace(go.Scatter(
-            x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast', line=dict(color='green')))
+    # Actual vs Forecast
+    plt.figure(figsize=(12, 4))
+    plt.plot(df['ds'].tail(24 * 7), df['y'].tail(24 * 7), label='Actual Load (Past Week)', color='black')
+    plt.plot(forecast_future['ds'], forecast_future['yhat'], label='Forecast Load', color='green')
+    plt.xticks(rotation=45)
+    plt.title("Actual vs Forecast")
+    plt.xlabel("Date")
+    plt.ylabel("Load (MW)")
+    plt.legend()
+    plt.tight_layout()
+    paths["Actual vs Forecast"] = f"actual_vs_forecast_{days}.png"
+    plt.savefig(paths["Actual vs Forecast"])
+    plt.close()
 
-    if "Confidence Interval" in options:
-        fig.add_trace(go.Scatter(
-            x=forecast['ds'],
-            y=forecast['yhat_upper'],
-            mode='lines',
-            line=dict(width=0),
-            showlegend=False))
-        fig.add_trace(go.Scatter(
-            x=forecast['ds'],
-            y=forecast['yhat_lower'],
-            mode='lines',
-            fill='tonexty',
-            fillcolor='rgba(0, 255, 100, 0.2)',
-            line=dict(width=0),
-            name='Confidence Interval'))
+    # Hourly Average
+    df['hour'] = df['ds'].dt.hour
+    hourly_avg = df.groupby('hour')['y'].mean()
+    plt.figure(figsize=(10, 4))
+    plt.plot(hourly_avg.index, hourly_avg.values, marker='o', linestyle='-', color='orange')
+    plt.title("Average Load by Hour of Day")
+    plt.xlabel("Hour")
+    plt.ylabel("Average Load (MW)")
+    plt.grid(True)
+    paths["Hourly Pattern"] = f"hourly_pattern.png"
+    plt.savefig(paths["Hourly Pattern"])
+    plt.close()
 
-    if "Peak Demand Marker" in options:
-        fig.add_trace(go.Scatter(
-            x=[peak_time],
-            y=[peak_value],
-            mode='markers+text',
-            name='Peak Demand',
-            text=[f'Peak: {peak_value:.0f} MW'],
-            textposition="top center",
-            marker=dict(size=10, color='red')))
+    # Seasonality
+    seasonality_fig = model.plot_components(forecast)
+    paths["Seasonality"] = f"seasonality_{days}.png"
+    seasonality_fig.savefig(paths["Seasonality"])
+    plt.close()
 
-    fig.update_layout(
-        title=f"{days}-Day Energy Demand Forecast",
-        xaxis_title='Date',
-        yaxis_title='Load (MW)',
-        template="plotly_white"
-    )
-
-    peak_text = f"📈 Peak demand of {peak_value:.0f} MW expected at {peak_time.strftime('%Y-%m-%d %H:%M')}."
-    return fig, peak_text
-
-
-import gradio as gr
+    # Peak demand
+    peak_row = forecast_future.loc[forecast_future['yhat'].idxmax()]
+    peak_time = peak_row['ds']
+    peak_value = round(peak_row['yhat'], 2)
+    peak_info = f"🔺 Peak Demand Time: {peak_time} — {peak_value} MW"
 
-options_list = ["Actual Load", "Forecast Line", "Confidence Interval", "Peak Demand Marker"]
+    return paths[graph_type], peak_info
 
-interface = gr.Interface(
+# Gradio Interface
+iface = gr.Interface(
     fn=forecast_energy,
     inputs=[
         gr.Radio([3, 7, 14], label="Select Forecast Period (Days)"),
-        gr.CheckboxGroup(options_list, label="Choose Graph Elements", value=options_list)
+        gr.Dropdown(["Forecast Plot", "Actual vs Forecast", "Hourly Pattern", "Seasonality"], label="Select Graph Type")
     ],
     outputs=[
-        gr.Plot(label="Energy Forecast Plot"),
-        gr.Textbox(label="Peak Demand Time")
+        gr.Image(type="filepath", label="Selected Plot"),
+        gr.Textbox(label="Peak Demand Info")
     ],
-    title="Smart Energy Demand Forecast",
-    description="Select forecast period and graph elements to customize the plot."
+    title="Smart Energy Load Forecasting",
+    description="Choose forecast days and which graph you want to visualize."
 )
 
-interface.launch()
+iface.launch(share=True)