Create app.py

app.py

@@ -0,0 +1,49 @@
+# app.py
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+from prophet import Prophet
+import matplotlib.pyplot as plt
+import gradio as gr
+
+# Simulate factory sensor data
+def simulate_factory_data(days=30, 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=5, 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=10, size=(len(date_rng)))
+    return df
+
+# Forecast temperature using Prophet
+def forecast_temperature(days):
+    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=days, freq='H')
+    forecast = model.predict(future)
+
+    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.set_title("Machine Temperature Forecast")
+    ax.set_xlabel("Time")
+    ax.set_ylabel("Temperature (°C)")
+    ax.legend()
+    plt.tight_layout()
+    return fig
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=forecast_temperature,
+    inputs=gr.Slider(12, 72, value=48, label="Forecast Hours"),
+    outputs=gr.Plot(label="Forecasted Temperature Chart"),
+    title="Smart Factory AI Pipeline",
+    description="Simulated machine temperature forecasting using Prophet. Drag the slider to choose how far to forecast."
+)
+
+if __name__ == "__main__":
+    demo.launch()