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| 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 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) |