Update app.py

app.py

@@ -1,124 +1,109 @@
 import gradio as gr
 import matplotlib.pyplot as plt
 import pandas as pd
-import numpy as np
-import tensorflow as tf
 import joblib
-from sklearn.metrics import mean_absolute_error, mean_squared_error
-from sklearn.preprocessing import MinMaxScaler
 
 # Load the dataset
-webtraffic_data = pd.read_csv("webtraffic.csv")
-
-# Convert 'Hour Index' to datetime
-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.drop(columns=['Hour Index'], inplace=True)
-
-# Split the data into train/test
-train_size = int(len(webtraffic_data) * 0.8)
-train_data = webtraffic_data.iloc[:train_size]
-test_data = webtraffic_data.iloc[train_size:]
-
-# Load pre-trained models
-sarima_model = joblib.load("sarima_model.pkl")  # SARIMA model
-lstm_model = tf.keras.models.load_model("lstm_model.keras")  # LSTM model
-
-# Initialize scalers and scale the data for LSTM
-scaler_X = MinMaxScaler(feature_range=(0, 1))
-scaler_y = MinMaxScaler(feature_range=(0, 1))
-
-# Scale training data
-X_train_scaled = scaler_X.fit_transform(train_data['Sessions'].values.reshape(-1, 1))
-y_train_scaled = scaler_y.fit_transform(train_data['Sessions'].values.reshape(-1, 1))
-
-# Scale test data
-X_test_scaled = scaler_X.transform(test_data['Sessions'].values.reshape(-1, 1))
-y_test_scaled = scaler_y.transform(test_data['Sessions'].values.reshape(-1, 1))
-
-# Reshape test data for LSTM (samples, time_steps, features)
-X_test_lstm = X_test_scaled.reshape((X_test_scaled.shape[0], 1, 1))
-
-# Generate predictions for SARIMA
-future_periods = len(test_data)
-sarima_predictions = sarima_model.predict(n_periods=future_periods)
-
-# Generate predictions for LSTM
-lstm_predictions_scaled = lstm_model.predict(X_test_lstm[:future_periods])
-lstm_predictions = scaler_y.inverse_transform(lstm_predictions_scaled)
-
-# Combine predictions into a DataFrame for visualization
-future_predictions = pd.DataFrame({
-    "Datetime": test_data['Datetime'],
-    "SARIMA_Predicted": sarima_predictions,
-    "LSTM_Predicted": lstm_predictions.flatten()
-})
-
-# Calculate metrics
-mae_sarima_future = mean_absolute_error(test_data['Sessions'], sarima_predictions)
-rmse_sarima_future = mean_squared_error(test_data['Sessions'], sarima_predictions, squared=False)
-
-mae_lstm_future = mean_absolute_error(test_data['Sessions'], lstm_predictions)
-rmse_lstm_future = mean_squared_error(test_data['Sessions'], lstm_predictions, squared=False)
-
-# Function to plot actual vs. predicted traffic
-def plot_predictions():
+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))
-
-    # Plot actual traffic
-    plt.plot(webtraffic_data['Datetime'].iloc[-future_periods:],
-             test_data['Sessions'].values[-future_periods:],
-             label='Actual Traffic', color='black', linestyle='dotted', linewidth=2)
-
-    # Plot SARIMA predictions
-    plt.plot(future_predictions['Datetime'],
-             future_predictions['SARIMA_Predicted'],
-             label='SARIMA Predicted', color='blue', linewidth=2)
-
-    # Plot LSTM predictions
-    plt.plot(future_predictions['Datetime'],
-             future_predictions['LSTM_Predicted'],
-             label='LSTM Predicted', color='green', linewidth=2)
-
-    plt.title("Future Traffic Predictions: SARIMA vs LSTM", fontsize=16)
+    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 to a file
-    plot_path = "/content/predictions_plot.png"
+    plot_path = "sarima_prediction_plot.png"
     plt.savefig(plot_path)
     plt.close()
     return plot_path
 
-# Function to display prediction metrics
+
+# Function to display SARIMA metrics
 def display_metrics():
     metrics = {
-        "Model": ["SARIMA", "LSTM"],
-        "Mean Absolute Error (MAE)": [mae_sarima_future, mae_lstm_future],
-        "Root Mean Squared Error (RMSE)": [rmse_sarima_future, rmse_lstm_future]
+        "Model": ["SARIMA"],
+        "Mean Absolute Error (MAE)": [mae_sarima_future],
+        "Root Mean Squared Error (RMSE)": [rmse_sarima_future],
     }
     return pd.DataFrame(metrics)
 
-# Gradio function to display the dashboard
-def gradio_dashboard():
-    plot_path = plot_predictions()
+
+# Gradio interface function
+def dashboard_interface():
+    plot_path = generate_plot()
     metrics_df = display_metrics()
     return plot_path, metrics_df.to_string()
 
-# Gradio interface
+
+# Build the Gradio interface
 with gr.Blocks() as dashboard:
-    gr.Markdown("## Web Traffic Prediction Dashboard")
-    gr.Markdown("This dashboard compares predictions from SARIMA and LSTM models.")
+    gr.Markdown("## Interactive SARIMA Web Traffic Prediction Dashboard")
+    gr.Markdown(
+        "This dashboard shows SARIMA model predictions vs actual traffic along with performance metrics."
+    )
 
-    # Show the plot
     plot_output = gr.Image(label="Prediction Plot")
-    metrics_output = gr.Textbox(label="Prediction Metrics", lines=15)
+    metrics_output = gr.Textbox(label="Metrics", lines=15)
 
-    # Define the Gradio button and actions
-    gr.Button("Update Dashboard").click(gradio_dashboard, outputs=[plot_output, metrics_output])
+    gr.Button("Generate Predictions").click(
+        fn=dashboard_interface,
+        inputs=[],
+        outputs=[plot_output, metrics_output],
+    )
 
-# Launch the dashboard
-dashboard.launch()
+# Launch the Gradio dashboard
+if __name__ == "__main__":
+    dashboard.launch()