Update app.py

app.py

@@ -1,36 +1,47 @@
 import pandas as pd
 import numpy as np
 from datetime import datetime
-from prophet import Prophet
 import matplotlib.pyplot as plt
 import gradio as gr
+from xgboost import XGBRegressor
+from sklearn.model_selection import train_test_split
 
-def simulate_factory_data(days=30, freq='H'):
+def simulate_factory_data(days=7, freq='H'):
     date_rng = pd.date_range(end=datetime.now(), periods=24 * days, freq=freq)
     df = pd.DataFrame(date_rng, columns=['ds'])
     df['temperature'] = np.random.normal(loc=70, scale=4, size=(len(date_rng)))
-    df['vibration'] = np.random.normal(loc=20, scale=3, size=(len(date_rng)))
-    df['power_usage'] = np.random.normal(loc=120, scale=8, size=(len(date_rng)))
-    anomaly_indices = np.random.choice(len(df), size=10, replace=False)
-    df.loc[anomaly_indices, 'temperature'] += np.random.uniform(10, 20, size=10)
-    df.loc[anomaly_indices, 'vibration'] += np.random.uniform(5, 10, size=10)
-    df.loc[anomaly_indices, 'power_usage'] += np.random.uniform(30, 50, size=10)
+    anomaly_indices = np.random.choice(len(df), size=8, replace=False)
+    df.loc[anomaly_indices, 'temperature'] += np.random.uniform(10, 20, size=8)
+    return df
+
+def create_lag_features(df, lags=6):
+    for i in range(1, lags + 1):
+        df[f'lag_{i}'] = df['temperature'].shift(i)
+    df = df.dropna().reset_index(drop=True)
     return df
 
 def forecast_temperature(hours):
-    periods = int(hours)  # Prophet expects integer periods
     df = simulate_factory_data()
-    model = Prophet()
-    df_temp = df[['ds', 'temperature']].rename(columns={"temperature": "y"})
-    model.fit(df_temp)
-    future = model.make_future_dataframe(periods=periods, freq='H')
-    forecast = model.predict(future)
+    df = create_lag_features(df)
+    X = df[[f'lag_{i}' for i in range(1, 7)]]
+    y = df['temperature']
+    model = XGBRegressor(n_estimators=100, learning_rate=0.1)
+    model.fit(X, y)
+
+    last_row = df.iloc[-1][[f'lag_{i}' for i in range(1, 7)]].values
+    preds = []
+    for _ in range(int(hours)):
+        pred = model.predict([last_row])[0]
+        preds.append(pred)
+        last_row = np.roll(last_row, -1)
+        last_row[-1] = pred
+
+    future_dates = pd.date_range(start=df['ds'].iloc[-1], periods=int(hours)+1, freq='H')[1:]
     fig, ax = plt.subplots(figsize=(10, 5))
     ax.plot(df['ds'], df['temperature'], label='Actual')
-    ax.plot(forecast['ds'], forecast['yhat'], label='Forecast')
-    ax.fill_between(forecast['ds'], forecast['yhat_lower'], forecast['yhat_upper'], alpha=0.2, label='Confidence')
+    ax.plot(future_dates, preds, label='Forecast', color='orange')
     ax.axhspan(85, 100, color='red', alpha=0.1, label='Danger Zone')
-    ax.set_title("Machine Temperature Forecast")
+    ax.set_title("Machine Temperature Forecast (XGBoost)")
     ax.set_xlabel("Time")
     ax.set_ylabel("Temperature (°C)")
     ax.legend()
@@ -39,10 +50,10 @@ def forecast_temperature(hours):
 
 demo = gr.Interface(
     fn=forecast_temperature,
-    inputs=gr.Slider(12.0, 72.0, value=48.0, step=0.5, label="Forecast Hours"),
+    inputs=gr.Slider(6, 48, value=24, step=1, label="Forecast Hours"),
     outputs=gr.Plot(label="Forecasted Temperature Chart"),
-    title="Smart Factory AI Pipeline",
-    description="Forecast machine temperature using Prophet. Red zone shows potential overheating risk."
+    title="Smart Factory AI Pipeline (XGBoost)",
+    description="Forecast machine temperature using XGBoost. Red zone shows potential overheating."
 )
 
 if __name__ == "__main__":