Update app.py

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestClassifier
+from prophet import Prophet
+
+# Load dataset
+@st.cache_data
+def load_data(filename):
+    return pd.read_csv(filename)
+
+# Main application
+st.sidebar.title("AI-Powered Industrial Management")
+page = st.sidebar.radio("Choose a page", ["Predictive Maintenance for Wind Turbines", "Intelligent Energy Management Systems"])
+
+# Predictive Maintenance for Wind Turbines
+if page == "Predictive Maintenance for Wind Turbines":
+    st.header("Predictive Maintenance for Wind Turbines")
+
+    # Load and display dataset
+    data = load_data('wind_turbine_data.csv')
+
+    st.write("### Dataset Preview:")
+    st.write(data.head())
+
+    # Dataset Meta Information
+    st.write("### Dataset Meta Information:")
+    st.write("""
+    - **Timestamp:** The time at which the data was recorded.
+    - **Turbine_ID:** Unique identifier for each wind turbine.
+    - **Wind_Speed (m/s):** The speed of the wind at the time of measurement.
+    - **Ambient_Temperature (°C):** The outside temperature where the turbine is located.
+    - **Power_Output (MW):** The power generated by the turbine at the time of measurement.
+    - **Blade_Vibration (mm/s):** The vibration level of the turbine blades, a critical indicator of potential mechanical issues.
+    - **Gearbox_Temperature (°C):** Temperature inside the turbine's gearbox.
+    - **Oil_Level (%):** The level of lubrication oil in the turbine.
+    - **Maintenance_History (0/1):** Whether the turbine underwent maintenance in the last 30 days (1) or not (0).
+    - **Component_Failure (0/1):** Whether a component failure occurred (1) or not. This is the target variable.
+    """)
+
+    # Data Exploration
+    st.write("### Data Exploration")
+
+    # Select Graph Type
+    graph_type = st.selectbox("Select a graph to visualize:", 
+                              ["Wind Speed Distribution", 
+                               "Ambient Temperature vs. Power Output", 
+                               "Blade Vibration vs. Gearbox Temperature", 
+                               "Oil Level Distribution"])
+
+    # Plotting based on selection
+    if graph_type == "Wind Speed Distribution":
+        st.write("#### Wind Speed Distribution")
+        plt.figure(figsize=(10, 6))
+        plt.hist(data['Wind_Speed'], bins=20, color='skyblue', edgecolor='black')
+        plt.xlabel('Wind Speed (m/s)')
+        plt.ylabel('Frequency')
+        plt.title('Distribution of Wind Speed')
+        st.pyplot(plt)
+        st.write("This graph shows the distribution of wind speeds recorded in the dataset. "
+                 "It helps in understanding the common wind speeds at which the turbines operate, "
+                 "which can be crucial for assessing turbine performance and predicting failures.")
+
+    elif graph_type == "Ambient Temperature vs. Power Output":
+        st.write("#### Ambient Temperature vs. Power Output")
+        plt.figure(figsize=(10, 6))
+        plt.scatter(data['Ambient_Temperature'], data['Power_Output'], color='green', alpha=0.6)
+        plt.xlabel('Ambient Temperature (°C)')
+        plt.ylabel('Power Output (MW)')
+        plt.title('Ambient Temperature vs. Power Output')
+        st.pyplot(plt)
+        st.write("This scatter plot illustrates the relationship between ambient temperature and power output. "
+                 "It shows how temperature variations can affect the power generated by the wind turbines, "
+                 "which is important for understanding operational efficiency under different weather conditions.")
+
+    elif graph_type == "Blade Vibration vs. Gearbox Temperature":
+        st.write("#### Blade Vibration vs. Gearbox Temperature")
+        plt.figure(figsize=(10, 6))
+        plt.scatter(data['Blade_Vibration'], data['Gearbox_Temperature'], color='orange', alpha=0.6)
+        plt.xlabel('Blade Vibration (mm/s)')
+        plt.ylabel('Gearbox Temperature (°C)')
+        plt.title('Blade Vibration vs. Gearbox Temperature')
+        st.pyplot(plt)
+        st.write("This scatter plot shows the correlation between blade vibration and gearbox temperature. "
+                 "High levels of blade vibration can indicate mechanical issues, and this plot helps in identifying "
+                 "potential stress on the gearbox due to vibrations.")
+
+    elif graph_type == "Oil Level Distribution":
+        st.write("#### Oil Level Distribution")
+        plt.figure(figsize=(10, 6))
+        plt.hist(data['Oil_Level'], bins=20, color='purple', edgecolor='black')
+        plt.xlabel('Oil Level (%)')
+        plt.ylabel('Frequency')
+        plt.title('Distribution of Oil Level')
+        st.pyplot(plt)
+        st.write("This histogram displays the distribution of oil levels in the turbines. "
+                 "Maintaining optimal oil levels is crucial for the smooth operation of turbine components. "
+                 "This graph helps in understanding how often turbines operate with low or high oil levels, "
+                 "which can impact their reliability and longevity.")
+
+    # Predictive Maintenance
+    st.write("### Predict Equipment Failure")
+    user_input = {
+        'Wind_Speed': st.number_input('Wind Speed (m/s)'),
+        'Ambient_Temperature': st.number_input('Ambient Temperature (°C)'),
+        'Power_Output': st.number_input('Power Output (MW)'),
+        'Blade_Vibration': st.number_input('Blade Vibration (mm/s)'),
+        'Gearbox_Temperature': st.number_input('Gearbox Temperature (°C)'),
+        'Oil_Level': st.number_input('Oil Level (%)'),
+        'Maintenance_History': st.selectbox('Maintenance History (0/1)', [0, 1])
+    }
+
+    input_df = pd.DataFrame([user_input])
+
+    # Load pre-trained model (assumed pre-trained, you should load it here)
+    model = RandomForestClassifier()  # This is where you would load your trained model
+    model.fit(data.drop(columns=['Component_Failure', 'Timestamp', 'Turbine_ID']), data['Component_Failure'])  # For demonstration only
+
+    prediction = model.predict(input_df)
+    st.write(f"Predicted Component Failure: {'Yes' if prediction[0] == 1 else 'No'}")
+
+# Intelligent Energy Management Systems
+elif page == "Intelligent Energy Management Systems":
+    st.header("Intelligent Energy Management Systems")
+
+    # Load the datasets
+    energy_df = load_data('energy_consumption.csv')
+    production_df = load_data('production_schedule.csv')
+    pricing_df = load_data('energy_pricing.csv')
+
+    # Function to display dataset metadata
+    def display_metadata(df, title):
+        st.subheader(f"{title} Metadata")
+        st.write(f"Number of records: {df.shape[0]}")
+        st.write(f"Number of columns: {df.shape[1]}")
+        st.write("Columns:")
+        for col in df.columns:
+            st.write(f"- **{col}**: {df[col].dtype}")
+
+    st.write("""
+    This app demonstrates AI-powered systems that can dynamically manage and allocate energy resources in industrial settings, optimizing energy consumption in real time.
+    """)
+
+    # Display dataset metadata information
+    st.write("### Dataset Metadata Information")
+
+    st.write("""
+    #### Energy Consumption Data:
+    This dataset records the energy consumption of different machines over time, along with associated parameters like temperature, humidity, and operational status.
+    """)
+    display_metadata(energy_df, "Energy Consumption")
+
+    st.write("""
+    #### Production Schedule Data:
+    This dataset contains the production schedules for different machines, including the start and end times of production, the type of product being produced, and the target output.
+    """)
+    display_metadata(production_df, "Production Schedule")
+
+    st.write("""
+    #### Energy Pricing Data:
+    This dataset provides the energy pricing information over time, including indicators for peak hours, which can influence energy consumption optimization strategies.
+    """)
+    display_metadata(pricing_df, "Energy Pricing")
+
+    # Visualization: Energy consumption over time for a selected machine
+    st.header("Energy Consumption Analysis by Machine")
+
+    selected_machine = st.selectbox("Select a Machine ID", energy_df['machine_id'].unique())
+
+    machine_energy_df = energy_df[energy_df['machine_id'] == selected_machine]
+
+    fig, ax = plt.subplots()
+    ax.plot(pd.to_datetime(machine_energy_df['timestamp']), machine_energy_df['energy_consumed_kWh'], label='Energy Consumption (kWh)', color='blue')
+    ax.set_xlabel('Timestamp')
+    ax.set_ylabel('Energy Consumed (kWh)')
+    ax.legend()
+
+    st.pyplot(fig)
+
+    st.write("""
+    This graph shows the energy consumption for the selected machine over time.
+    """)
+
+    # AI-Powered Forecasting
+    st.header("Energy Consumption Forecasting")
+
+    # Prepare the data for Prophet
+    forecast_data = machine_energy_df[['timestamp', 'energy_consumed_kWh']].rename(columns={'timestamp': 'ds', 'energy_consumed_kWh': 'y'})
+
+    # Initialize Prophet model
+    model = Prophet()
+    model.fit(forecast_data)
+
+    # Create future dataframe
+    future = model.make_future_dataframe(periods=48, freq='H')  # Forecasting the next 48 hours
+
+    # Forecast
+    forecast = model.predict(future)
+
+    # Plot the forecast
+    st.subheader("Forecasting Results")
+    fig1 = model.plot(forecast)
+    st.pyplot(fig1)
+
+    fig2 = model.plot_components(forecast)
+    st.pyplot(fig2)
+
+    st.write("""
+    The above charts display the forecasted energy consumption for the selected machine over the next 48 hours, along with the trend and seasonality components.
+    """)