"""Address Geocoding page for the Streamlit application."""


import pandas as pd
import streamlit as st


def render_address_geocoding_page():
    """Render the Address Geocoding tutorial page."""
    st.header("Address Geocoding & Analysis")

    st.markdown("""
    Convert street addresses into geographic coordinates and analyze accessibility from 
    specific locations. Perfect for site selection and resident-focused analysis.
    
    **What you'll learn:**
    - 📍 Geocode individual addresses
    - 📋 Batch process multiple addresses
    - 🏠 Analyze accessibility from residential locations
    - 🎯 Find nearest POIs to any address
    """)

    # Create tabs for single vs batch
    tab1, tab2 = st.tabs(["Single Address", "Batch Addresses"])

    with tab1:
        render_single_address_section()

    with tab2:
        render_batch_address_section()


def render_single_address_section():
    """Render single address geocoding section."""
    st.subheader("Single Address Analysis")

    with st.form("single_address"):
        address = st.text_input(
            "Street Address",
            value="123 Main Street, Durham, NC 27701",
            help="Enter a full street address"
        )

        col1, col2 = st.columns(2)

        with col1:
            find_nearest = st.checkbox("Find nearest POIs", value=True)
            poi_type = st.selectbox(
                "POI Type",
                options=["library", "park", "hospital", "school"],
                disabled=not find_nearest
            )

        with col2:
            analyze_demographics = st.checkbox("Analyze demographics", value=True)
            travel_time = st.slider(
                "Travel time (min)",
                min_value=5,
                max_value=30,
                value=15,
                disabled=not analyze_demographics
            )

        submitted = st.form_submit_button("Geocode & Analyze")

    if submitted:
        with st.spinner("Geocoding address..."):
            # Placeholder for geocoding
            st.success("✅ Address geocoded successfully!")

            # Display results
            col1, col2 = st.columns(2)

            with col1:
                st.metric("Latitude", "35.9940")
                st.metric("Longitude", "-78.8986")

            with col2:
                st.metric("Geocoding Confidence", "High")
                st.metric("Location Type", "Rooftop")

            if find_nearest:
                st.subheader("🎯 Nearest POIs")
                st.markdown("""
                | POI Name | Distance | Travel Time |
                |----------|----------|-------------|
                | Durham County Library | 0.5 mi | 10 min walk |
                | Central Park | 0.8 mi | 15 min walk |
                | Duke Hospital | 2.1 mi | 8 min drive |
                """)

            if analyze_demographics:
                st.subheader("📊 Area Demographics")
                col1, col2, col3 = st.columns(3)

                with col1:
                    st.metric("Population", "8,542")

                with col2:
                    st.metric("Median Income", "$52,400")

                with col3:
                    st.metric("Households", "3,218")


def render_batch_address_section():
    """Render batch address geocoding section."""
    st.subheader("Batch Address Processing")

    uploaded_file = st.file_uploader(
        "Upload CSV with addresses",
        type="csv",
        help="CSV should have an 'address' column"
    )

    if uploaded_file is not None:
        try:
            df = pd.read_csv(uploaded_file)

            if 'address' in df.columns:
                st.success(f"✅ Loaded {len(df)} addresses")

                # Preview
                st.subheader("Address Preview")
                st.dataframe(df.head())

                # Batch processing options
                col1, col2 = st.columns(2)

                with col1:
                    process_demographics = st.checkbox(
                        "Include demographics for each address",
                        value=False
                    )

                with col2:
                    find_pois = st.checkbox(
                        "Find nearest POIs for each address",
                        value=True
                    )

                if st.button("Process Batch", type="primary"):
                    progress_bar = st.progress(0)
                    status_text = st.empty()

                    for idx, row in df.iterrows():
                        status_text.text(f"Processing address {idx+1} of {len(df)}...")
                        progress_bar.progress((idx + 1) / len(df))

                    st.success("✅ Batch processing complete!")
                    st.info("Download results coming soon!")
            else:
                st.error("CSV must contain an 'address' column")

        except Exception as e:
            st.error(f"Error reading file: {e!s}")

    # Template download
    with st.expander("📋 Download Template"):
        template_df = pd.DataFrame({
            'address': [
                '123 Main St, Durham, NC 27701',
                '456 Oak Ave, Chapel Hill, NC 27514',
                '789 Pine Rd, Raleigh, NC 27603'
            ],
            'name': ['Location 1', 'Location 2', 'Location 3']
        })

        csv = template_df.to_csv(index=False)
        st.download_button(
            label="Download Address Template",
            data=csv,
            file_name="address_template.csv",
            mime="text/csv"
        )
