app.py

import gradio as gr
import matplotlib.pyplot as plt
import pandas as pd
import joblib

# Load the dataset
data_file = "webtraffic.csv"
webtraffic_data = pd.read_csv(data_file)

# Verify if 'Datetime' exists, or create it
if "Datetime" not in webtraffic_data.columns:
    print("Datetime column missing. Attempting to create 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"
    )
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
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."
    )

    plot_output = gr.Image(label="Prediction Plot")
    metrics_output = gr.Textbox(label="Metrics", lines=15)

    gr.Button("Generate Predictions").click(
        fn=dashboard_interface,
        inputs=[],
        outputs=[plot_output, metrics_output],
    )

# Launch the Gradio dashboard
if __name__ == "__main__":
    dashboard.launch()