src/streamlit_app.py

import streamlit as st
import json
import folium
from streamlit_folium import folium_static, st_folium
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import os

from constants import zone_colors, ALL_ZONES

# Set page title
st.set_page_config(layout="wide")
st.title("🌧️ Storm Water Drain Dashboard - Hyderabad")


fSWDDetails = {}
all_zones = []

# Load SWD details from JSON file
file_path = "SWD_Details_Working.json"
json_path = os.path.join(os.path.dirname(__file__), file_path)


if os.path.exists(json_path):
    # st.success(f"Found file: {json_path}")
    with open(json_path, "r") as file:
        fSWDDetails = json.load(file)
        # st.write("Loaded JSON:")
        # st.json(fSWDDetails)
else:
    st.error(f"File not found: {file_path}")


def get_all_zones():
    """Extract unique zones from SWD details."""
    global all_zones
    if len(all_zones) == 0:
        # Step 1: Extract all zones
        for item in fSWDDetails:
            if item['Zone'] not in all_zones:
                all_zones.append(item['Zone'])
    
    all_zones.append("All Zones")
    all_zones.sort()
    return all_zones
        
get_all_zones()

selected_zone = st.sidebar.radio("Filter by Zone", options=all_zones)

# avg_lat, avg_lon = 17.3850, 78.4867  # Default to Hyderabad center
avg_lat, avg_lon = 17.4250, 78.5567  # Default to Hyderabad center

zoom_level = 11.5  # Default zoom level

# Step 3: Filter data
filtered_data = fSWDDetails

if selected_zone != ALL_ZONES:
    filtered_data = [item for item in fSWDDetails if item['Zone'] == selected_zone]
    zoom_level = 13  # Zoom in closer for specific zone

    avg_lat = sum(item['Latitude'] for item in filtered_data) / len(filtered_data) + 0.005
    avg_lon = sum(item['Longitude'] for item in filtered_data) / len(filtered_data) + 0.035

selected_pit_id = st.sidebar.selectbox("Select Pit ID to View Details", ["None"] + [item['Id'] for item in filtered_data])

# Set layout with columns
col1, col2 = st.columns([2, 1])  # Map on left (wider), details on right

selected_pit = next((item for item in filtered_data if item['Id'] == selected_pit_id), None)
if selected_pit:
    st.sidebar.write(f"Selected Pit: {selected_pit['Id']}")
    st.sidebar.write(f"Zone: {selected_pit['Zone']}")
    info = selected_pit['Details']['info']

with col1:
    st.header("Storm Water Pit Map")
    if selected_pit:
        zoom_level = 15
        avg_lat = selected_pit['Latitude']
        avg_lon = selected_pit['Longitude']

    hydMap = folium.Map(location=[avg_lat, avg_lon], zoom_start=zoom_level)

    # Marker + Circle Highlight
    # Add SWD entries to the map
    for _, item in enumerate(filtered_data):

        color = zone_colors.get(item["Zone"], "gray")  # default to gray if zone not found
        popup_info = f"""
        <b>{item['Id']}</b><br>
        <b>{item['Address']['Area']}</b><br>
        """
        folium.Marker(
            [item['Latitude'], item['Longitude']],
            popup=popup_info,
            tooltip=popup_info,
            icon=folium.Icon(color=color, icon='info-sign')
        ).add_to(hydMap)

    if selected_pit:
        zoom_level = 15
        folium.CircleMarker(
            location=[selected_pit["Latitude"], selected_pit["Longitude"]],
            radius=25,
            color='orange',
            fill=True,
            fill_color='yellow',
            fill_opacity=0.7,
            weight=3
        ).add_to(hydMap)

    st_data = st_folium(hydMap, width=1500, height=600)

with col2:
    if selected_pit is not None:
        st.header(f"Pit - {selected_pit['Id']}")
        address = selected_pit['Address']
        address_str = address['Street'] + "\n" + address['Area'] + "\n" + address['Pin']
        
        subcol1, subcol2 = st.columns(2)

        with subcol1:
            info = selected_pit["Details"]["info"]
            st.text_input("Debris Level", info['DebrisPitLevel'], disabled=True)
            st.text_input("Last Cleaned", info['LastCleaned'], disabled=True)
            st.text_input("Next Cleaning", info['NextScheduledCleaning'], disabled=True)
            st.text_input("Maintenance", info['MaintenanceStatus'], disabled=True)
            st.text_area("Address", address_str, height=110, disabled=True)

        with subcol2:
            st.text_input("Compression Enabled", info['CompressionEnabled'], disabled=True)
            
            latcol1, longcol2 = st.columns(2)
            with latcol1:
                st.text_input("Latitude", selected_pit['Latitude'], disabled=True)
            with longcol2:
                st.text_input("Longitude", selected_pit['Longitude'], disabled=True)

            st.text_input("Size", info['Size'], disabled=True)
            st.text_input("Phone", selected_pit['Phone'], disabled=True)
            st.text_area("Notes", info['Notes'], height=110, disabled=True)

            
        st.slider("Compression Distance", min_value=3, max_value=12, value=5, step=1)
        
        btnCol1, btnCol2 = st.columns(2)

        with btnCol1:
            st.button("Change Level", disabled=False, help="Coming Soon!", type="primary")
        with btnCol2:
            st.button("Raise Request", disabled=False, help="Coming Soon!", type="primary")

# Sample data

# if st.button("Show Storm Water Pit Level Trend"):
if selected_pit and st.checkbox("Show Debris Level Chart"):

    st.subheader("Storm Water Pit Level Trend")

    debris_levels = selected_pit.get("Details", {}).get("DebrisLevels", [])
    if not debris_levels:
        st.warning("No debris level data available for this pit.")
        st.stop()
    
    # data = [
    #     {"Timestamp": "2025-10-01T10:00:00Z", "Level": 0.5, "Status": "Normal"},
    #     {"Timestamp": "2025-10-02T10:00:00Z", "Level": 0.6, "Status": "Normal"},
    #     {"Timestamp": "2025-10-03T10:00:00Z", "Level": 0.7, "Status": "High"},
    #     {"Timestamp": "2025-10-04T10:00:00Z", "Level": 0.8, "Status": "Critical"},
    #     {"Timestamp": "2025-10-05T10:00:00Z", "Level": 0.9, "Status": "Critical"},
    #     {"Timestamp": "2025-10-06T10:00:00Z", "Level": 0.95, "Status": "High"},
    #     {"Timestamp": "2025-10-07T10:00:00Z", "Level": 1.0, "Status": "Critical"},
    #     {"Timestamp": "2025-10-08T10:00:00Z", "Level": 1.0, "Status": "Critical"},
    #     {"Timestamp": "2025-10-09T10:00:00Z", "Level": 0.85, "Status": "High"},
    #     {"Timestamp": "2025-10-10T10:00:00Z", "Level": 0.2, "Status": "Normal"}
    # ]

    # Convert to DataFrame
    df = pd.DataFrame(debris_levels)
    df["Timestamp"] = pd.to_datetime(df["Timestamp"])
    df["DateLabel"] = df["Timestamp"].dt.strftime("%b %d")  # e.g., 'Oct 01'


    # Optional: Define color map for status
    status_color_map = {
        "Normal": "green",
        "High": "orange",
        "Critical": "red"
    }
    df["Color"] = df["Status"].map(status_color_map)

    # Calculate average level
    avg_level = df["Level"].mean()

    fig = go.Figure()

    # Plotly Bar Chart
    fig = go.Figure()
    fig.add_trace(go.Bar(
        x=df["DateLabel"],
        y=df["Level"],
        marker_color=df["Color"],
        text=df["Status"],
        textposition="outside"
    ))

    # Add average line
    fig.add_hline(
        y=avg_level,
        line_dash="dash",
        line_color="blue",
        annotation_text=f"Average: {avg_level:.2f}",
        annotation_position="top left"
    )

    fig.update_layout(
        title="Storm Water Pit Level Trend",
        xaxis_title="Date",
        yaxis_title="Level (m)",
        yaxis_range=[0, 1.2],
        xaxis_tickangle=-30,
        height=500
    )

    # Display in Streamlit
    st.plotly_chart(fig, use_container_width=True)