Create app.py

app.py

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+# app.py
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+from prophet import Prophet
+import streamlit as st
+import matplotlib.pyplot as plt
+
+# 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(df):
+    model = Prophet()
+    df_temp = df[['ds', 'temperature']].rename(columns={"temperature": "y"})
+    model.fit(df_temp)
+    future = model.make_future_dataframe(periods=48, freq='H')
+    forecast = model.predict(future)
+    return forecast
+
+# Streamlit UI
+st.title("🧠 Smart Factory AI Dashboard")
+st.markdown("Forecasting machine temperature to predict maintenance needs.")
+
+df = simulate_factory_data()
+forecast = forecast_temperature(df)
+
+# Plot forecast
+st.subheader("📈 Machine Temperature Forecast (Next 48 hours)")
+fig, ax = plt.subplots()
+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 Interval')
+ax.set_xlabel("Time")
+ax.set_ylabel("Temperature (°C)")
+ax.legend()
+st.pyplot(fig)