app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from salesforce_integration import fetch_poles

# Title
st.title("📡 VIEP Smart Poles Dashboard")

# Fetch data
df = fetch_poles()

# 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()

selected_alert_levels = st.sidebar.multiselect("Alert Level", alert_levels, default=alert_levels)
selected_camera_status = st.sidebar.selectbox("Camera Status", ["All"] + camera_statuses)

# Initial filtering by alert level and camera status
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")

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"]))

# --- Pole Table ---
st.subheader("📋 Pole Table")
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.")