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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import numpy as np | |
| import tensorflow as tf | |
| import joblib | |
| # Load the dataset | |
| webtraffic_data = pd.read_csv("webtraffic.csv") | |
| # Ensure the 'Datetime' column is in datetime format | |
| webtraffic_data['Datetime'] = pd.to_datetime(webtraffic_data['Datetime']) | |
| # Load the pre-trained models | |
| sarima_model = joblib.load("sarima_model.pkl") # Load SARIMA model | |
| lstm_model = tf.keras.models.load_model("lstm_model.keras") # Load LSTM model | |
| # Load the scaler for LSTM if used during training (optional) | |
| scaler = joblib.load("scaler.pkl") | |
| # Function to generate predictions and plots | |
| def generate_custom_prediction(model, future_hours): | |
| future_hours = int(future_hours) | |
| future_datetimes = pd.date_range( | |
| start=webtraffic_data['Datetime'].iloc[-1], | |
| periods=future_hours + 1, | |
| freq='H' | |
| )[1:] | |
| if model == "SARIMA": | |
| # SARIMA Predictions | |
| sarima_predictions = sarima_model.forecast(steps=future_hours) | |
| plt.figure(figsize=(15, 6)) | |
| plt.plot(webtraffic_data['Datetime'], webtraffic_data['Sessions'], label="Actual Data", color="blue") | |
| plt.plot(future_datetimes, sarima_predictions, label="SARIMA Predictions", color="green") | |
| elif model == "LSTM": | |
| # Prepare data for LSTM (reshape and scale as necessary) | |
| lstm_input = webtraffic_data['Sessions'].values[-future_hours:].reshape(-1, 1) | |
| lstm_input_scaled = scaler.transform(lstm_input) # Scale input using the saved scaler | |
| lstm_input_scaled = lstm_input_scaled.reshape(1, future_hours, 1) # Reshape for LSTM model | |
| # LSTM Predictions | |
| lstm_predictions = lstm_model.predict(lstm_input_scaled) | |
| lstm_predictions = scaler.inverse_transform(lstm_predictions).flatten() # Inverse scale | |
| plt.figure(figsize=(15, 6)) | |
| plt.plot(webtraffic_data['Datetime'], webtraffic_data['Sessions'], label="Actual Data", color="blue") | |
| plt.plot(future_datetimes, lstm_predictions, label="LSTM Predictions", color="green") | |
| # Customize the plot | |
| plt.title(f"{model} Web Traffic Predictions", 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 as an image | |
| plot_path = f"{model.lower()}_prediction_plot.png" | |
| plt.savefig(plot_path) | |
| plt.close() | |
| return plot_path | |
| # Gradio interface function | |
| def prediction_dashboard(model, future_hours): | |
| plot_path = generate_custom_prediction(model, future_hours) | |
| return plot_path | |
| # Build the Gradio interface | |
| with gr.Blocks() as dashboard: | |
| gr.Markdown("## Interactive Web Traffic Prediction Dashboard") | |
| gr.Markdown("Input the number of hours to predict and select a model for future web traffic forecasting.") | |
| # Dropdown for model selection | |
| model_selection = gr.Dropdown(["SARIMA", "LSTM"], label="Select Model", value="SARIMA") | |
| # Input for future hours | |
| future_hours_input = gr.Number(label="Future Hours to Predict", value=24) | |
| # Output: Plot | |
| plot_output = gr.Image(label="Prediction Plot") | |
| # Button to generate predictions | |
| gr.Button("Generate Prediction").click( | |
| fn=prediction_dashboard, | |
| inputs=[model_selection, future_hours_input], | |
| outputs=[plot_output] | |
| ) | |
| # Launch the Gradio dashboard | |
| dashboard.launch() | |