update app.py

app.py

@@ -2,108 +2,191 @@ import gradio as gr
 import matplotlib.pyplot as plt
 import pandas as pd
 import joblib
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+from math import sqrt
 
-# Load the dataset
+# Step 1: Load the Dataset
+print("Loading Dataset...")
 data_file = "webtraffic.csv"
-webtraffic_data = pd.read_csv(data_file)
 
-# Verify if 'Datetime' exists, or create it
+try:
+    webtraffic_data = pd.read_csv(data_file)
+    print("Dataset loaded successfully!")
+except Exception as e:
+    print(f"Error loading dataset: {e}")
+    exit()
+
+# Step 2: Ensure 'Datetime' column exists or create it
 if "Datetime" not in webtraffic_data.columns:
-    print("Datetime column missing. Attempting to create from 'Hour Index'.")
+    print("Datetime column missing. Creating from 'Hour Index'.")
     start_date = pd.Timestamp("2024-01-01 00:00:00")
-    webtraffic_data["Datetime"] = start_date + pd.to_timedelta(
-        webtraffic_data["Hour Index"], unit="h"
-    )
+    webtraffic_data["Datetime"] = start_date + pd.to_timedelta(webtraffic_data["Hour Index"], unit="h")
 else:
     webtraffic_data["Datetime"] = pd.to_datetime(webtraffic_data["Datetime"])
 
-# Ensure 'Datetime' column is sorted
 webtraffic_data.sort_values("Datetime", inplace=True)
 
-# Load the SARIMA model
-sarima_model = joblib.load("sarima_model.pkl")
-
-# Define future periods for evaluation
-future_periods = 48
-
-# Dummy values for metrics (if needed)
-mae_sarima_future = 100
-rmse_sarima_future = 150
-
-
-# Function to generate plot based on SARIMA model
-def generate_plot():
-    future_dates = pd.date_range(
-        start=webtraffic_data["Datetime"].iloc[-1], periods=future_periods + 1, freq="H"
-    )[1:]
-
-    sarima_predictions = sarima_model.predict(n_periods=future_periods)
-    future_predictions = pd.DataFrame(
-        {"Datetime": future_dates, "SARIMA_Predicted": sarima_predictions}
-    )
-    plt.figure(figsize=(15, 6))
-    plt.plot(
-        webtraffic_data["Datetime"],
-        webtraffic_data["Sessions"],
-        label="Actual Traffic",
-        color="black",
-        linestyle="dotted",
-        linewidth=2,
-    )
-    plt.plot(
-        future_predictions["Datetime"],
-        future_predictions["SARIMA_Predicted"],
-        label="SARIMA Predicted",
-        color="blue",
-        linewidth=2,
-    )
-
-    plt.title("SARIMA Predictions vs Actual Traffic", fontsize=16)
-    plt.xlabel("Datetime", fontsize=12)
-    plt.ylabel("Sessions", fontsize=12)
-    plt.legend(loc="upper left")
-    plt.grid(True)
-    plt.tight_layout()
-
-    plot_path = "sarima_prediction_plot.png"
-    plt.savefig(plot_path)
-    plt.close()
-    return plot_path
-
-
-# Function to display SARIMA metrics
-def display_metrics():
-    metrics = {
-        "Model": ["SARIMA"],
-        "Mean Absolute Error (MAE)": [mae_sarima_future],
-        "Root Mean Squared Error (RMSE)": [rmse_sarima_future],
-    }
-    return pd.DataFrame(metrics)
-
-
-# Gradio interface function
-def dashboard_interface():
-    plot_path = generate_plot()
-    metrics_df = display_metrics()
-    return plot_path, metrics_df.to_string()
-
-
-# Build the Gradio interface
+# Step 3: Load SARIMA Model
+print("Loading SARIMA Model...")
+try:
+    sarima_model = joblib.load("sarima_model.pkl")
+    print("SARIMA model loaded successfully!")
+except Exception as e:
+    print(f"Error loading SARIMA model: {e}")
+    exit()
+
+# Step 4: Define Functions for Gradio Dashboard
+future_periods = 48  # Number of hours to predict
+
+def generate_sarima_plot():
+    """Generate SARIMA predictions and return a detailed plot with metrics."""
+    try:
+        # Generate future dates for predictions
+        future_dates = pd.date_range(
+            start=webtraffic_data["Datetime"].iloc[-1],
+            periods=future_periods + 1,
+            freq="H"
+        )[1:]
+
+        # Generate SARIMA predictions
+        sarima_predictions = sarima_model.predict(n_periods=future_periods)
+        
+        # Extract actual data for the last 'future_periods' hours
+        actual_sessions = webtraffic_data["Sessions"].iloc[-future_periods:].values
+
+        # Calculate metrics
+        mae_sarima = mean_absolute_error(actual_sessions, sarima_predictions[:len(actual_sessions)])
+        rmse_sarima = sqrt(mean_squared_error(actual_sessions, sarima_predictions[:len(actual_sessions)]))
+
+        # Combine predictions into a DataFrame for plotting
+        future_predictions = pd.DataFrame({
+            "Datetime": future_dates,
+            "SARIMA_Predicted": sarima_predictions
+        })
+
+        # Plot Actual Traffic vs SARIMA Predictions
+        plt.figure(figsize=(15, 6))
+        plt.plot(
+            webtraffic_data["Datetime"],
+            webtraffic_data["Sessions"],
+            label="Actual Traffic",
+            color="black",
+            linestyle="dotted",
+            linewidth=2,
+        )
+        plt.plot(
+            future_predictions["Datetime"],
+            future_predictions["SARIMA_Predicted"],
+            label="SARIMA Predicted",
+            color="blue",
+            linewidth=2,
+        )
+
+        plt.title("SARIMA Predictions vs Actual Traffic", fontsize=16)
+        plt.xlabel("Datetime", fontsize=12)
+        plt.ylabel("Sessions", fontsize=12)
+        plt.legend(loc="upper left")
+        plt.grid(True)
+        plt.tight_layout()
+
+        # Save the plot
+        plot_path = "sarima_prediction_plot.png"
+        plt.savefig(plot_path)
+        plt.close()
+
+        # Return plot path and metrics
+        metrics = f"""
+        SARIMA Model Metrics:
+        - Mean Absolute Error (MAE): {mae_sarima:.2f}
+        - Root Mean Squared Error (RMSE): {rmse_sarima:.2f}
+        """
+        return plot_path, metrics
+
+    except Exception as e:
+        print(f"Error generating SARIMA plot: {e}")
+        return None, "Error in generating output. Please check the data and model."
+
+def generate_zoomed_plot():
+    """Generate a zoomed-in SARIMA prediction plot."""
+    try:
+        # Generate future dates for predictions
+        future_dates = pd.date_range(
+            start=webtraffic_data["Datetime"].iloc[-1],
+            periods=future_periods + 1,
+            freq="H"
+        )[1:]
+
+        # Generate SARIMA predictions
+        sarima_predictions = sarima_model.predict(n_periods=future_periods)
+
+        # Combine predictions into a DataFrame for plotting
+        future_predictions = pd.DataFrame({
+            "Datetime": future_dates,
+            "SARIMA_Predicted": sarima_predictions
+        })
+
+        # Zoomed-in view of the plot (recent data only)
+        plt.figure(figsize=(15, 6))
+        plt.plot(
+            webtraffic_data["Datetime"].iloc[-future_periods:],
+            webtraffic_data["Sessions"].iloc[-future_periods:],
+            label="Actual Traffic (Zoomed)",
+            color="black",
+            linestyle="dotted",
+            linewidth=2,
+        )
+        plt.plot(
+            future_predictions["Datetime"],
+            future_predictions["SARIMA_Predicted"],
+            label="SARIMA Predicted (Zoomed)",
+            color="green",
+            linewidth=2,
+        )
+
+        plt.title("Zoomed-In SARIMA Predictions vs Actual Traffic", fontsize=16)
+        plt.xlabel("Datetime", fontsize=12)
+        plt.ylabel("Sessions", fontsize=12)
+        plt.legend(loc="upper left")
+        plt.grid(True)
+        plt.tight_layout()
+
+        # Save the zoomed plot
+        zoomed_plot_path = "sarima_zoomed_plot.png"
+        plt.savefig(zoomed_plot_path)
+        plt.close()
+
+        return zoomed_plot_path
+
+    except Exception as e:
+        print(f"Error generating zoomed plot: {e}")
+        return None
+
+# Step 5: Gradio Dashboard with Two Tiles and Metrics
 with gr.Blocks() as dashboard:
-    gr.Markdown("## Interactive SARIMA Web Traffic Prediction Dashboard")
-    gr.Markdown(
-        "This dashboard shows SARIMA model predictions vs actual traffic along with performance metrics."
-    )
+    gr.Markdown("## Enhanced SARIMA Web Traffic Prediction Dashboard")
+    gr.Markdown("This dashboard includes SARIMA predictions, performance metrics, and a zoomed-in view of recent data.")
+
+    # Outputs: Main Plot and Metrics
+    plot_output = gr.Image(label="SARIMA Prediction Plot")
+    metrics_output = gr.Textbox(label="Model Metrics", lines=6)
 
-    plot_output = gr.Image(label="Prediction Plot")
-    metrics_output = gr.Textbox(label="Metrics", lines=15)
+    # Outputs: Zoomed Plot
+    zoomed_plot_output = gr.Image(label="Zoomed-In Prediction Plot")
 
+    # Button to Generate Results
     gr.Button("Generate Predictions").click(
-        fn=dashboard_interface,
+        fn=generate_sarima_plot,
         inputs=[],
         outputs=[plot_output, metrics_output],
     )
 
-# Launch the Gradio dashboard
+    gr.Button("Generate Zoomed-In Plot").click(
+        fn=generate_zoomed_plot,
+        inputs=[],
+        outputs=[zoomed_plot_output],
+    )
+
+# Launch the Gradio Dashboard
 if __name__ == "__main__":
+    print("\nLaunching Enhanced Gradio Dashboard...")
     dashboard.launch()