Update app.py

app.py

@@ -1,53 +1,50 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
-import plotly.graph_objects as go
+import pydeck as pdk
 from salesforce_integration import fetch_poles
+from modules.visuals import display_dashboard, display_charts
 
 # Title
 st.title("📡 VIEP Smart Poles Dashboard")
 
-# Fetch data
+# Fetch the raw data from Salesforce
 df = fetch_poles()
 
-# Sidebar Filters
+# --- Sidebar Filters ---
 st.sidebar.header("📁 Filter Data")
 
-# Dynamic values from Salesforce data
-alert_levels = df["Alert_Level__c"].dropna().unique().tolist()
-sites = df["Site__c"].dropna().unique().tolist()
-camera_statuses = df["Camera_Status__c"].dropna().unique().tolist()
+# Alert Level Filter
+selected_alert_levels = st.sidebar.multiselect(
+    "Alert Level", ["Red", "Yellow", "Green"], default=["Red", "Yellow", "Green"]
+)
 
-selected_alert_levels = st.sidebar.multiselect("Alert Level", alert_levels, default=alert_levels)
-selected_camera_status = st.sidebar.selectbox("Camera Status", ["All"] + camera_statuses)
+# Camera Status Filter
+selected_camera_status = st.sidebar.selectbox(
+    "Camera Status", ["All", "Online", "Offline"]
+)
 
-# Initial filtering by alert level and camera status
-filtered_df = df[
-    (df["Alert_Level__c"].isin(selected_alert_levels))
-]
+# Site Filter with "All" option
+site_options = ["All"] + df["Site__c"].dropna().unique().tolist()
+selected_site = st.sidebar.selectbox("Site", site_options, index=0)
+
+# --- Filtering Logic ---
+filtered_df = df[df["Alert_Level__c"].isin(selected_alert_levels)]
 
 if selected_camera_status != "All":
     filtered_df = filtered_df[filtered_df["Camera_Status__c"] == selected_camera_status]
 
-# Site filter logic (place here)
-site_options = ["All"] + df["Site__c"].dropna().unique().tolist()
-selected_site = st.sidebar.selectbox("Site", site_options, index=0)
 if selected_site != "All":
     filtered_df = filtered_df[filtered_df["Site__c"] == selected_site]
 
-# Site filter logic (place here)
-if selected_site != "All":
-    filtered_df = filtered_df[filtered_df["Site__c"] == selected_site]
-
-
-# --- System Summary ---
-st.subheader("📊 System Summary")
+# --- Count Faults Based on Alert Level ---
+# Assuming default 0 fault count or based on Alert Level:
+filtered_df["Fault_Count__c"] = filtered_df["Alert_Level__c"].apply(
+    lambda x: 3 if x == "Red" else (2 if x == "Yellow" else 1)
+)
 
-col1, col2, col3 = st.columns(3)
-
-col1.metric("Total Poles", len(filtered_df))
-col2.metric("Red Alerts", len(filtered_df[filtered_df["Alert_Level__c"] == "Red"]))
-col3.metric("Offline Cameras", len(filtered_df[filtered_df["Camera_Status__c"] == "Offline"]))
+# --- Display System Summary ---
+display_dashboard(filtered_df)
 
 # --- Pole Table ---
 st.subheader("📋 Pole Table")
@@ -55,54 +52,86 @@ st.dataframe(filtered_df, use_container_width=True)
 
 # --- Energy Generation Chart ---
 st.subheader("⚙ Energy Generation (Solar vs Wind)")
-if not filtered_df.empty:
-    energy_chart = px.bar(
-        filtered_df,
-        x="Name",
-        y=["Solar_Generation__c", "Wind_Generation__c"],
-        barmode="group",
-        title="Solar vs Wind Energy Generation"
-    )
-    st.plotly_chart(energy_chart, use_container_width=True)
-else:
-    st.info("No data available for the selected filters.")
-
-# --- Alert Level Breakdown ---
-st.subheader("🚨 Alert Level Breakdown")
-if not filtered_df.empty:
-    alert_counts = filtered_df["Alert_Level__c"].value_counts().reset_index()
-    alert_counts.columns = ["Alert Level", "Count"]
-    alert_pie = px.pie(alert_counts, values="Count", names="Alert Level", title="Alert Distribution")
-    st.plotly_chart(alert_pie, use_container_width=True)
-else:
-    st.info("No alerts to display.")
-
-# 5. Tilt vs Vibration Chart
-st.subheader("📉 Tilt vs Vibration")
-
-# Extract Tilt and Vibration from RFID_Tag__c
-filtered_df["Tilt"] = filtered_df["RFID_Tag__c"].str.extract(r'Tilt:(\d+\.?\d*)').astype(float)
-filtered_df["Vibration"] = filtered_df["RFID_Tag__c"].str.extract(r'Vib:(\d+\.?\d*)').astype(float)
-
-# Drop rows with no tilt or vibration data
-tilt_vib_df = filtered_df.dropna(subset=["Tilt", "Vibration"])
-
-if not tilt_vib_df.empty:
-    fig_tilt_vib = go.Figure()
-    fig_tilt_vib.add_trace(go.Scatter(
-        x=tilt_vib_df["Name"],
-        y=tilt_vib_df["Tilt"],
-        mode='lines+markers',
-        name='Tilt'
-    ))
-    fig_tilt_vib.add_trace(go.Scatter(
-        x=tilt_vib_df["Name"],
-        y=tilt_vib_df["Vibration"],
-        mode='lines+markers',
-        name='Vibration'
-    ))
-    fig_tilt_vib.update_layout(title="Tilt vs Vibration by Pole", xaxis_title="Pole Name", yaxis_title="Value")
-    st.plotly_chart(fig_tilt_vib, use_container_width=True)
-else:
-    st.info("No Tilt or Vibration data available.")
-
+st.plotly_chart(px.bar(
+    filtered_df,
+    x="Name",
+    y=["Solar_Generation__c", "Wind_Generation__c"],
+    barmode="group"
+))
+
+# --- Tilt vs Vibration Chart ---
+st.subheader("🧭 Tilt vs Vibration Comparison")
+
+fig_tilt_vibration = px.line(
+    filtered_df,
+    x="Name",
+    y=["Tilt__c", "Vibration__c"],
+    markers=True,
+    title="Tilt vs Vibration per Pole"
+)
+st.plotly_chart(fig_tilt_vibration)
+
+# --- Alert Level Breakdown Chart ---
+display_charts(filtered_df)
+
+# --- Heatmap Section ---
+st.subheader("🌡 Pole Fault Heatmap")
+
+# Map Color based on Faults
+def get_fault_level(fault_count):
+    if fault_count <= 1:
+        return "Green"
+    elif 2 <= fault_count <= 3:
+        return "Yellow"
+    else:
+        return "Red"
+
+filtered_df["Fault_Level"] = filtered_df["Fault_Count__c"].apply(get_fault_level)
+
+color_map = {
+    "Green": [0, 255, 0],
+    "Yellow": [255, 255, 0],
+    "Red": [255, 0, 0]
+}
+filtered_df["color"] = filtered_df["Fault_Level"].map(color_map)
+
+# Pydeck Map
+layer = pdk.Layer(
+    "ScatterplotLayer",
+    data=filtered_df,
+    get_position='[Longitude__c, Latitude__c]',
+    get_color="color",
+    get_radius=80,
+    pickable=True,
+    auto_highlight=True
+)
+
+view_state = pdk.ViewState(
+    latitude=filtered_df["Latitude__c"].mean(),
+    longitude=filtered_df["Longitude__c"].mean(),
+    zoom=10,
+    pitch=40
+)
+
+tooltip = {
+    "html": """
+    <b>Pole Name:</b> {Name}<br/>
+    <b>Site:</b> {Site__c}<br/>
+    <b>Fault Count:</b> {Fault_Count__c}<br/>
+    <b>Alert Level:</b> {Alert_Level__c}<br/>
+    <b>RFID Tag:</b> {RFID_Tag__c}<br/>
+    <b>Tilt:</b> {Tilt__c}<br/>
+    <b>Vibration:</b> {Vibration__c}
+    """,
+    "style": {
+        "backgroundColor": "steelblue",
+        "color": "white"
+    }
+}
+
+st.pydeck_chart(pdk.Deck(
+    map_style="mapbox://styles/mapbox/dark-v10",
+    initial_view_state=view_state,
+    layers=[layer],
+    tooltip=tooltip
+))