Update app.py

app.py

@@ -1,82 +1,267 @@
+
+You said:
+import random
+import pandas as pd
 import streamlit as st
+import pydeck as pdk
+
+# ---- Area-Specific Configuration ----
+AREA_DETAILS = {
+    "Hyderabad": {
+        "coords": [17.4036, 78.5247],
+        "area_name": "Ramanthapur Dairy Farm",
+        "purpose": "Dairy Farm"
+    },
+    "Ballari": {
+        "coords": [15.1468, 76.9237],
+        "area_name": "Cowl Bazar Power Station",
+        "purpose": "Power Station"
+    },
+    "Gadwal": {
+        "coords": [16.2315, 77.7965],
+        "area_name": "Bheem Nagar Solar Station",
+        "purpose": "Solar Station"
+    },
+    "Kurnool": {
+        "coords": [15.8281, 78.0373],
+        "area_name": "Venkata Ramana Agriculture Field",
+        "purpose": "Agriculture Monitoring"
+    }
+}
+
+POLES_PER_SITE = 12
+
+# ---- Generate Poles with Anomalies ----
+def generate_open_area_poles(site_name, center_lat, center_lon, area, purpose):
+    poles = []
+    spacing = 0.0006
+    anomalies_options = ['None', 'Sensor Fault', 'Overheat', 'Power Surge']
+    anomaly_weights = [0.6, 0.2, 0.1, 0.1]
+
+    for i in range(POLES_PER_SITE):
+        lat = center_lat + random.uniform(-0.0002, 0.0002)
+        lon = center_lon + (i - POLES_PER_SITE // 2) * spacing
+        alert_level = random.choices(['Green', 'Yellow', 'Red'], weights=[6, 4, 2])[0]
+        anomaly = random.choices(anomalies_options, weights=anomaly_weights)[0]
+
+        poles.append({
+            "Pole ID": f"{site_name[:3].upper()}-{i+1:03}",
+            "Site": site_name,
+            "Latitude": lat,
+            "Longitude": lon,
+            "Alert Level": alert_level,
+            "Health Score": round(random.uniform(70, 100), 2),
+            "Power Status": random.choice(['Sufficient', 'Insufficient']),
+            "Camera Status": random.choice(['Online', 'Offline']),
+            "Location Area": area,
+            "Purpose": purpose,
+            "Anomalies": anomaly
+        })
+    return poles
+
+# ---- Prepare Full DataFrame ----
+all_poles = []
+for site, details in AREA_DETAILS.items():
+    poles = generate_open_area_poles(site, *details['coords'], details['area_name'], details['purpose'])
+    all_poles.extend(poles)
+
+df = pd.DataFrame(all_poles)
+
+# ---- Streamlit UI ----
+st.set_page_config(page_title="Smart Pole Visual Dashboard", layout="wide")
+st.title("🌐 Smart Renewable Pole Monitoring Dashboard")
+
+site = st.selectbox("📍 Select a site location:", list(AREA_DETAILS.keys()))
+selected = AREA_DETAILS[site]
+
+# ---- Filtered View ----
+filtered_df = df[df["Site"] == site]
+
+# ---- Display Site Description ----
+st.markdown(f"### 📌 Location: **{selected['area_name']}**")
+st.markdown(f"🔧 **Poles Purpose**: {selected['purpose']}")
+
+# ---- KPI Metrics ----
+col1, col2, col3 = st.columns(3)
+col1.metric("Total Poles", POLES_PER_SITE)
+col2.metric("🔴 Red Alerts", filtered_df[filtered_df["Alert Level"] == "Red"].shape[0])
+col3.metric("📷 Offline Cameras", filtered_df[filtered_df["Camera Status"] == "Offline"].shape[0])
+
+# ---- Alert Level to Color ----
+def alert_color(alert):
+    return {
+        "Green": [0, 255, 0, 160],
+        "Yellow": [255, 255, 0, 160],
+        "Red": [255, 0, 0, 160]
+    }[alert]
+
+filtered_df = filtered_df.copy()
+filtered_df["Color"] = filtered_df["Alert Level"].apply(alert_color)
+
+# ---- Map Visualization ----
+st.subheader("🗺️ Pole Location & Health Status")
+st.pydeck_chart(pdk.Deck(
+    initial_view_state=pdk.ViewState(
+        latitude=selected['coords'][0],
+        longitude=selected['coords'][1],
+        zoom=16.5,
+        pitch=45
+    ),
+    layers=[
+        pdk.Layer(
+            "ScatterplotLayer",
+            data=filtered_df,
+            get_position='[Longitude, Latitude]',
+            get_color='Color',
+            get_radius=30,
+            pickable=True
+        )
+    ],
+    tooltip={
+        "html": "<b>Pole ID:</b> {Pole ID}<br/>"
+                "<b>Location:</b> {Location Area}<br/>"
+                "<b>Purpose:</b> {Purpose}<br/>"
+                "<b>Health Score:</b> {Health Score}<br/>"
+                "<b>Alert Level:</b> {Alert Level}<br/>"
+                "<b>Camera:</b> {Camera Status}<br/>"
+                "<b>Power:</b> {Power Status}<br/>"
+                "<b>Anomaly:</b> {Anomalies}",
+        "style": {"color": "white", "backgroundColor": "black"}
+    }
+))
+
+# ---- Data Table ----
+st.subheader("📋 Detailed Pole Information")
+st.dataframe(filtered_df, use_container_width=True)                                                                                              import random
 import pandas as pd
-import folium
-from streamlit_folium import st_folium
-from folium.plugins import HeatMap
-import random
+import streamlit as st
+import pydeck as pdk
 
-# ---- Simulated Smart Pole Data ----
-AREAS = {
-    "Hyderabad": [17.4036, 78.5247],
-    "Ballari": [15.1468, 76.9237],
-    "Kurnool": [15.8281, 78.0373],
-    "Gadwal": [16.2315, 77.7965]
+# ---- Area-Specific Configuration ----
+AREA_DETAILS = {
+    "Hyderabad": {
+        "coords": [17.4036, 78.5247],
+        "area_name": "Ramanthapur Dairy Farm",
+        "purpose": "Dairy Farm"
+    },
+    "Ballari": {
+        "coords": [15.1468, 76.9237],
+        "area_name": "Cowl Bazar Power Station",
+        "purpose": "Power Station"
+    },
+    "Gadwal": {
+        "coords": [16.2315, 77.7965],
+        "area_name": "Bheem Nagar Solar Station",
+        "purpose": "Solar Station"
+    },
+    "Kurnool": {
+        "coords": [15.8281, 78.0373],
+        "area_name": "Venkata Ramana Agriculture Field",
+        "purpose": "Agriculture Monitoring"
+    }
 }
 
-def generate_poles(site, lat, lon, count=12):
-    data = []
-    for i in range(count):
-        lat_offset = random.uniform(-0.002, 0.002)
-        lon_offset = random.uniform(-0.002, 0.002)
-        energy = round(random.uniform(20, 100), 2)
-        wind = round(random.uniform(0, 20), 2)
-        sensor = round(random.uniform(0, 1), 2)
-        alert = random.choice(["Normal", "Alert"])
-        data.append({
-            "Site": site,
-            "Pole ID": f"{site[:3].upper()}-{i+1:03}",
-            "Latitude": lat + lat_offset,
-            "Longitude": lon + lon_offset,
-            "Energy": energy,
-            "Wind": wind,
-            "Sensor": sensor,
-            "Alert": alert
+POLES_PER_SITE = 12
+
+# ---- Generate Poles with Anomalies ----
+def generate_open_area_poles(site_name, center_lat, center_lon, area, purpose):
+    poles = []
+    spacing = 0.0006
+    anomalies_options = ['None', 'Sensor Fault', 'Overheat', 'Power Surge']
+    anomaly_weights = [0.6, 0.2, 0.1, 0.1]
+
+    for i in range(POLES_PER_SITE):
+        lat = center_lat + random.uniform(-0.0002, 0.0002)
+        lon = center_lon + (i - POLES_PER_SITE // 2) * spacing
+        alert_level = random.choices(['Green', 'Yellow', 'Red'], weights=[6, 4, 2])[0]
+        anomaly = random.choices(anomalies_options, weights=anomaly_weights)[0]
+
+        poles.append({
+            "Pole ID": f"{site_name[:3].upper()}-{i+1:03}",
+            "Site": site_name,
+            "Latitude": lat,
+            "Longitude": lon,
+            "Alert Level": alert_level,
+            "Health Score": round(random.uniform(70, 100), 2),
+            "Power Status": random.choice(['Sufficient', 'Insufficient']),
+            "Camera Status": random.choice(['Online', 'Offline']),
+            "Location Area": area,
+            "Purpose": purpose,
+            "Anomalies": anomaly
         })
-    return data
+    return poles
 
-# Generate full dataset
-poles_data = []
-for area, coords in AREAS.items():
-    poles_data.extend(generate_poles(area, coords[0], coords[1]))
+# ---- Prepare Full DataFrame ----
+all_poles = []
+for site, details in AREA_DETAILS.items():
+    poles = generate_open_area_poles(site, *details['coords'], details['area_name'], details['purpose'])
+    all_poles.extend(poles)
 
-df = pd.DataFrame(poles_data)
+df = pd.DataFrame(all_poles)
 
 # ---- Streamlit UI ----
-st.set_page_config(layout="wide")
-st.title("🌐 Hugging Face Compatible Solar Pole Heatmap")
-
-# ---- Create Folium Map with Esri Satellite ----
-center = [16.5, 77.5]
-m = folium.Map(location=center, zoom_start=7,
-               tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
-               attr='Esri Satellite')
-
-# ---- Add HeatMap layer for energy ----
-heat_data = [[row["Latitude"], row["Longitude"], row["Energy"]] for _, row in df.iterrows()]
-HeatMap(heat_data, radius=15).add_to(m)
-
-# ---- Add Pole Markers with Alerts ----
-for _, row in df.iterrows():
-    popup_html = f"""
-    <b>Pole ID:</b> {row['Pole ID']}<br/>
-    <b>Site:</b> {row['Site']}<br/>
-    <b>Energy:</b> {row['Energy']}<br/>
-    <b>Wind:</b> {row['Wind']}<br/>
-    <b>Sensor:</b> {row['Sensor']}<br/>
-    <b>Status:</b> {row['Alert']}
-    """
-
-    icon = folium.Icon(color="red" if row["Alert"] == "Alert" else "green", icon="info-sign")
-    folium.Marker(
-        location=[row["Latitude"], row["Longitude"]],
-        popup=popup_html,
-        icon=icon
-    ).add_to(m)
-
-# ---- Display Map ----
-st.subheader("🗺️ Smart Pole Satellite Heatmap")
-st_data = st_folium(m, width=1200, height=600)
-
-# ---- Optional: Table view
-with st.expander("📋 Show Pole Data Table"):
-    st.dataframe(df)
+st.set_page_config(page_title="Smart Pole Visual Dashboard", layout="wide")
+st.title("🌐 Smart Renewable Pole Monitoring Dashboard")
+
+site = st.selectbox("📍 Select a site location:", list(AREA_DETAILS.keys()))
+selected = AREA_DETAILS[site]
+
+# ---- Filtered View ----
+filtered_df = df[df["Site"] == site]
+
+# ---- Display Site Description ----
+st.markdown(f"### 📌 Location: **{selected['area_name']}**")
+st.markdown(f"🔧 **Poles Purpose**: {selected['purpose']}")
+
+# ---- KPI Metrics ----
+col1, col2, col3 = st.columns(3)
+col1.metric("Total Poles", POLES_PER_SITE)
+col2.metric("🔴 Red Alerts", filtered_df[filtered_df["Alert Level"] == "Red"].shape[0])
+col3.metric("📷 Offline Cameras", filtered_df[filtered_df["Camera Status"] == "Offline"].shape[0])
+
+# ---- Alert Level to Color ----
+def alert_color(alert):
+    return {
+        "Green": [0, 255, 0, 160],
+        "Yellow": [255, 255, 0, 160],
+        "Red": [255, 0, 0, 160]
+    }[alert]
+
+filtered_df = filtered_df.copy()
+filtered_df["Color"] = filtered_df["Alert Level"].apply(alert_color)
+
+# ---- Map Visualization ----
+st.subheader("🗺️ Pole Location & Health Status")
+st.pydeck_chart(pdk.Deck(
+    initial_view_state=pdk.ViewState(
+        latitude=selected['coords'][0],
+        longitude=selected['coords'][1],
+        zoom=16.5,
+        pitch=45
+    ),
+    layers=[
+        pdk.Layer(
+            "ScatterplotLayer",
+            data=filtered_df,
+            get_position='[Longitude, Latitude]',
+            get_color='Color',
+            get_radius=30,
+            pickable=True
+        )
+    ],
+    tooltip={
+        "html": "<b>Pole ID:</b> {Pole ID}<br/>"
+                "<b>Location:</b> {Location Area}<br/>"
+                "<b>Purpose:</b> {Purpose}<br/>"
+                "<b>Health Score:</b> {Health Score}<br/>"
+                "<b>Alert Level:</b> {Alert Level}<br/>"
+                "<b>Camera:</b> {Camera Status}<br/>"
+                "<b>Power:</b> {Power Status}<br/>"
+                "<b>Anomaly:</b> {Anomalies}",
+        "style": {"color": "white", "backgroundColor": "black"}
+    }
+))
+
+# ---- Data Table ----
+st.subheader("📋 Detailed Pole Information")
+st.dataframe(filtered_df, use_container_width=True)