Create modules/visuals.py

modules/visuals.py

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+import streamlit as st
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+def display_dashboard(df):
+    st.subheader("📊 System Summary")
+    col1, col2, col3 = st.columns(3)
+    col1.metric("Total Poles", df.shape[0])
+    col2.metric("🚨 Red Alerts", df[df['AlertLevel']=="Red"].shape[0])
+    col3.metric("⚡ Power Issues", df[df['PowerSufficient']=="No"].shape[0])
+
+def display_charts(df):
+    st.subheader("⚙️ Energy Generation Trends")
+    st.bar_chart(df.set_index("PoleID")[["SolarGen(kWh)", "WindGen(kWh)"]])
+    st.subheader("📉 Tilt vs Vibration")
+    st.scatter_chart(df.rename(columns={"Tilt(°)": "Tilt", "Vibration(g)": "Vibration"}).set_index("PoleID")[["Tilt", "Vibration"]])
+    
+def display_heatmap(df):
+    st.subheader("🔥 Correlation Heatmap")
+    # Select numerical columns for correlation
+    numerical_cols = ["SolarGen(kWh)", "WindGen(kWh)", "Tilt(°)", "Vibration(g)"]
+    corr_matrix = df[numerical_cols].corr()
+    
+    # Create heatmap using seaborn
+    fig, ax = plt.subplots(figsize=(8, 6))
+    sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", vmin=-1, vmax=1, center=0, ax=ax)
+    plt.title("Correlation Heatmap of Pole Metrics")
+    
+    # Display in Streamlit
+    st.pyplot(fig)