import random import pandas as pd import streamlit as st import pydeck as pdk from datetime import datetime, timedelta # ---- Constants ---- POLES_PER_SITE = 12 SITES = { "Hyderabad": [17.385044, 78.486671], "Gadwal": [16.2351, 77.8052], "Kurnool": [15.8281, 78.0373], "Ballari": [12.9716, 77.5946] } # ---- Helper Functions ---- def generate_location(base_lat, base_lon): return [ base_lat + random.uniform(-0.02, 0.02), base_lon + random.uniform(-0.02, 0.02) ] def simulate_pole(pole_id, site_name): lat, lon = generate_location(*SITES[site_name]) solar_kwh = round(random.uniform(3.0, 7.5), 2) wind_kwh = round(random.uniform(0.5, 2.0), 2) power_required = round(random.uniform(4.0, 8.0), 2) total_power = solar_kwh + wind_kwh power_status = 'Sufficient' if total_power >= power_required else 'Insufficient' tilt_angle = round(random.uniform(0, 45), 2) vibration = round(random.uniform(0, 5), 2) camera_status = random.choice(['Online', 'Offline']) alert_level = 'Green' anomaly_details = [] if tilt_angle > 30 or vibration > 3: alert_level = 'Yellow' anomaly_details.append("Tilt or Vibration threshold exceeded.") if tilt_angle > 40 or vibration > 4.5: alert_level = 'Red' anomaly_details.append("Critical tilt or vibration detected.") health_score = max(0, 100 - (tilt_angle + vibration * 10)) timestamp = datetime.now() - timedelta(hours=random.randint(0, 6)) return { 'Pole ID': f'{site_name[:3].upper()}-{pole_id:03}', 'Site': Site__c, 'Latitude': Location_Latitude__c, 'Longitude': Location_Longitude__c, 'Solar (kWh)': solar_kwh, 'Wind (kWh)': wind_kwh, 'Power Required (kWh)': Power_Required__c, 'Total Power (kWh)': total_power, 'Power Status': power_status, 'Tilt Angle (°)': tilt_angle, 'Vibration (g)': vibration, 'Camera Status': Camera_Status__c, 'Health Score': Health_Score__c, 'Alert Level': Alert_Level__c, 'Anomalies': "; ".join(anomaly_details), 'Last Checked': timestamp.strftime('%Y-%m-%d %H:%M:%S') } # ---- Streamlit UI ---- st.set_page_config(page_title="Smart Pole Monitoring", layout="wide") st.title("🌍 Smart Renewable Pole Monitoring - Multi-Site") selected_site = st.text_input("Enter site to view (Hyderabad, Gadwal, Kurnool, Ballari):", "Hyderabad") if selected_site in SITES: SITES = ['Kurnool', 'Hyderabad', 'Ballari', 'Gadwal'] POLES_PER_SITE = 12 def simulate_pole(pole_id, site): return { 'Id': f'P{pole_id}_{site[:3].upper()}', 'Site': site, 'Energy': round(100 + pole_id * 5.5, 2), 'Status': 'OK' } with st.spinner(f"Simulating poles at {selected_site}..."): poles_data = [simulate_pole(i + 1, site) for site in SITES for i in range(POLES_PER_SITE)] df = pd.DataFrame(poles_data) site_df = df[df['Site'] == selected_site] # 1. Debug: Print columns in site_df st.write("DEBUG: Columns in site_df ➜", site_df.columns.tolist()) # 2. Safely access 'AlertLevel' and count red alerts if 'AlertLevel' in site_df.columns: red_alerts = site_df[site_df['AlertLevel'] == 'Red'].shape[0] else: red_alerts = 0 # 3. Use Streamlit to display red alert count col1, col2, col3 = st.columns(3) col1.metric("Total Poles", len(site_df)) # Show the red alerts in the second column col2.metric("Red Alerts", red_alerts) # If you have other metrics like energy: col3.metric("Energy", int(site_df['Energy'].sum())) # Table View # Check if the selected site exists (ensure indentation here is consistent) if selected_site: # Display the subheader with the selected site name st.subheader(f"📋 Pole Data Table for {selected_site}") # Add the rest of your logic here if needed for the table # For example, showing a data table for the selected site st.dataframe(site_df) # Other code related to displaying the metrics can go here too col1, col2, col3 = st.columns(3) col1.metric("Total Poles", len(site_df)) # Safely access 'AlertLevel' and count red alerts if 'AlertLevel' in site_df.columns: red_alerts = site_df[site_df['AlertLevel'] == 'Red'].shape[0] else: red_alerts = 0 col2.metric("Red Alerts", red_alerts) # If you have other metrics like energy: col3.metric("Energy", int(site_df['Energy'].sum())) else: st.warning("Please select a valid site.") with st.expander("Filter Options"): alert_filter = st.multiselect("Alert Level", options=site_df['Alert Level'].unique(), default=site_df['Alert Level'].unique()) camera_filter = st.multiselect("Camera Status", options=site_df['Camera Status'].unique(), default=site_df['Camera Status'].unique()) filtered_df = site_df[(site_df['Alert Level'].isin(alert_filter)) & (site_df['Camera Status'].isin(camera_filter))] st.dataframe(filtered_df, use_container_width=True) # Charts st.subheader("📊 Energy Generation Comparison") st.bar_chart(site_df[['Solar (kWh)', 'Wind (kWh)']].mean()) st.subheader("📈 Tilt vs. Vibration") st.scatter_chart(site_df[['Tilt Angle (°)', 'Vibration (g)']]) # Map with Red Alerts st.subheader("📍 Red Alert Pole Locations") red_df = site_df[site_df['Alert Level'] == 'Red'] if not red_df.empty: st.pydeck_chart(pdk.Deck( initial_view_state=pdk.ViewState( latitude=SITES[selected_site][0], longitude=SITES[selected_site][1], zoom=12, pitch=50 ), layers=[ pdk.Layer( 'ScatterplotLayer', data=red_df, get_position='[Longitude, Latitude]', get_color='[255, 0, 0, 160]', get_radius=100, ) ] )) st.markdown("

Red Alert Poles are Blinking

", unsafe_allow_html=True) else: st.info("No red alerts at this time.") if selected_site == "Kurnool": st.write("Simulating for Kurnool") else: st.write("Simulating for another site") st.warning("Invalid site. Please enter one of: Hyderabad, Gadwal, Kurnool, Ballari") import streamlit as st from salesforce_integration import fetch_salesforce_data from utils import process_data # Optional, if you need data processing # Fetch data from Salesforce poles_data = fetch_salesforce_data() # Process the data (optional, based on your model needs) processed_data = process_data(poles_data) # Display the processed data in Streamlit st.title("Poles Data Visualization") st.write("Processed Poles Data", processed_data)