Create visuals.py

modules/visuals.py

@@ -0,0 +1,46 @@
+import streamlit as st
+import matplotlib.pyplot as plt
+
+def display_dashboard(df):
+    """
+    Display system-wide summary metrics on the dashboard.
+    
+    :param df: DataFrame containing the pole data.
+    """
+    st.subheader("📊 System Summary")
+
+    # Columns to display different metrics
+    col1, col2, col3 = st.columns(3)
+
+    # Total Poles
+    col1.metric("Total Poles", df.shape[0])
+
+    # Red Alerts
+    col2.metric("🚨 Red Alerts", df[df['Alert Level'] == "Red"].shape[0])
+
+    # Power Insufficiency Issues
+    col3.metric("⚡ Power Issues", df[df['Power Sufficient'] == "No"].shape[0])
+
+def display_charts(df):
+    """
+    Display charts for energy generation and tilt vs vibration.
+    
+    :param df: DataFrame containing the pole data.
+    """
+    st.subheader("⚙️ Energy Generation Trends")
+
+    # Plot bar chart for Solar and Wind Generation
+    fig, ax = plt.subplots(figsize=(10, 6))
+    df.set_index("Pole ID")[["Solar Gen (kWh)", "Wind Gen (kWh)"]].plot(kind="bar", ax=ax)
+    ax.set_ylabel("Energy Generation (kWh)")
+    ax.set_xlabel("Pole ID")
+    st.pyplot(fig)
+
+    st.subheader("📉 Tilt vs Vibration")
+
+    # Plot scatter chart for Tilt vs Vibration
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.scatter(df["Tilt (°)"], df["Vibration (g)"], color='blue')
+    ax.set_xlabel("Tilt (°)")
+    ax.set_ylabel("Vibration (g)")
+    st.pyplot(fig)