Update modules/visuals.py

modules/visuals.py

@@ -2,60 +2,52 @@ import streamlit as st
 import plotly.express as px
 import pandas as pd
 
-def display_dashboard(df):
-    st.subheader("📊 System Summary")
-    col1, col2, col3, col4 = st.columns(4)
+def display_dashboard(df, location):
+    st.subheader(f"📊 System Summary - {location}")
+    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])
-    col4.metric("📍 Locations", len(df['Location'].unique()))
 
 def display_charts(df):
     st.subheader("⚙️ Energy Generation Trends")
-    st.bar_chart(df.groupby("Location")[["SolarGen(kWh)", "WindGen(kWh)"]].sum())
+    st.bar_chart(df.groupby("Zone")[["SolarGen(kWh)", "WindGen(kWh)"]].sum())
     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 = df.copy()  # Avoid modifying the original DataFrame
-    df["AlertValue"] = df["AlertLevel"].map(alert_map)
+def display_map_heatmap(df, location):
+    if df.empty:
+        st.warning("No data available for this location.")
+        return
     
-    # Pivot table for heatmap values (AlertValue)
-    pivot_df = df.pivot_table(index="Location", columns="PoleID", values="AlertValue", fill_value=0)
+    # Map AlertLevel to colors
+    df = df.copy()
+    df["AlertColor"] = df["AlertLevel"].map({"Green": "green", "Yellow": "yellow", "Red": "red"})
     
-    # Create hover text DataFrame
-    hover_data = df[["Location", "PoleID", "AlertLevel", "Anomalies"]].copy()
-    hover_text = pivot_df.copy().astype(str)
-    for loc in pivot_df.index:
-        for pole in pivot_df.columns:
-            # Find matching row in hover_data
-            match = hover_data[(hover_data["Location"] == loc) & (hover_data["PoleID"] == pole)]
-            if not match.empty:
-                alert = match["AlertLevel"].iloc[0]
-                anomalies = match["Anomalies"].iloc[0]
-                hover_text.loc[loc, pole] = f"Pole: {pole}<br>Alert: {alert}<br>Anomalies: {anomalies}"
-            else:
-                hover_text.loc[loc, pole] = f"Pole: {pole}<br>Alert: None<br>Anomalies: None"
-    
-    # Create heatmap using Plotly
-    fig = px.imshow(
-        pivot_df,
-        color_continuous_scale=["green", "yellow", "red"],
-        zmin=0,
-        zmax=2,
-        title="Pole Alert Heatmap by Location",
-        text_auto=False,
-        height=500
+    # Create scatter map
+    fig = px.scatter_mapbox(
+        df,
+        lat="Latitude",
+        lon="Longitude",
+        color="AlertLevel",
+        color_discrete_map={"Green": "green", "Yellow": "yellow", "Red": "red"},
+        size_max=15,
+        zoom=10,
+        hover_data={
+            "PoleID": True,
+            "RFID": True,
+            "AlertLevel": True,
+            "Anomalies": True,
+            "Zone": True,
+            "Latitude": False,
+            "Longitude": False
+        },
+        title=f"Pole Alert Map - {location}",
+        height=600
     )
-    fig.update_traces(hovertemplate="%{customdata}<br>%{x}<br>%{y}", customdata=hover_text)
     fig.update_layout(
-        xaxis_title="Pole ID",
-        yaxis_title="Location",
-        coloraxis_colorbar=dict(
-            tickvals=[0, 1, 2],
-            ticktext=["Green", "Yellow", "Red"]
-        )
+        mapbox_style="open-street-map",
+        margin={"r":0,"t":50,"l":0,"b":0},
+        showlegend=True
     )
     st.plotly_chart(fig, use_container_width=True)