Create visuals.py

modules/visuals.py

@@ -0,0 +1,45 @@
+import streamlit as st
+import plotly.express as px
+import pandas as pd
+
+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):
+    # Map AlertLevel to numeric values for heatmap intensity
+    alert_map = {"Green": 0, "Yellow": 1, "Red": 2}
+    df["AlertValue"] = df["AlertLevel"].map(alert_map)
+    
+    # Create a pivot table for heatmap (single row for all poles)
+    pivot_df = df[["PoleID", "AlertValue"]].set_index("PoleID").T
+    
+    # Create heatmap using Plotly
+    fig = px.imshow(
+        pivot_df,
+        color_continuous_scale=["green", "yellow", "red"],
+        zmin=0,
+        zmax=2,
+        labels=dict(color="Alert Level"),
+        title="Pole Alert Heatmap",
+        height=300
+    )
+    fig.update_layout(
+        xaxis_title="Pole ID",
+        yaxis_title="",
+        yaxis_showticklabels=False,
+        coloraxis_colorbar=dict(
+            tickvals=[0, 1, 2],
+            ticktext=["Green", "Yellow", "Red"]
+        )
+    )
+    st.plotly_chart(fig, use_container_width=True)