"""
Feature 2: Testing Cycle Browser
Month-year filter with pre-computed cycles from the cycle_periods DB table.
"""
import streamlit as st
import pandas as pd
from core.db_connector import get_db_connector
from ui.components import page_header, metric_row
from ui.plotly_charts import create_cycle_timeline

page_header(
    "Testing Cycles",
    "Cycles are identified from motor activity — >0% starts a cycle and returning to 0% ends a cycle. "
    "To qualify, a testing cycle must show either >5% motor speed OR >10 sec duration observed."
)

db = get_db_connector()


@st.cache_data(ttl=3600)
def get_available_months():
    return db.get_available_months()


@st.cache_data(ttl=3600)
def get_cycle_months():
    return db.get_cycle_months()


@st.cache_data(ttl=600)
def load_cycles_for_month(month_str: str):
    """Load pre-computed cycles from cycle_periods table for a given month."""
    return db.get_cycle_periods(month_str)


@st.cache_data(ttl=600)
def load_all_cycles():
    """Load all pre-computed cycles (for cross-page use)."""
    return db.get_cycle_periods()


# Sidebar: month selector
with st.sidebar:
    st.subheader("Select Period")
    sensor_months = get_available_months()
    cycle_months = get_cycle_months()
    months = sorted(set(sensor_months) | set(cycle_months))

    if not months:
        st.error("No data months found in database.")
        st.stop()

    selected_month = st.selectbox(
        "Month",
        months,
        index=len(months) - 1,  # Default to most recent
        key="cycle_month",
    )

# Load cycles for selected month
cycles = load_cycles_for_month(selected_month)

# Also cache all cycles for the comparison page
all_cycles = load_all_cycles()
st.session_state.all_cycles = all_cycles

if not cycles:
    st.info(f"No testing cycles found in {selected_month}.")
else:
    # Summary metrics
    total_runtime = sum(c['duration_minutes'] for c in cycles)
    peak_pressures = [c.get('peak_pressure', 0) for c in cycles if c.get('peak_pressure')]
    max_pressure = max(peak_pressures) if peak_pressures else 0
    total_dispensed = sum(c.get('total_kg_dispensed', 0) for c in cycles)

    metrics = [
        {'label': 'Cycles', 'value': len(cycles), 'unit': ''},
        {'label': 'Total Runtime', 'value': f"{total_runtime:.0f}", 'unit': 'min'},
        {'label': 'Peak Pressure (PT130)', 'value': f"{max_pressure:.0f}", 'unit': 'bar'},
        {'label': 'Total Dispensed', 'value': f"{total_dispensed:.1f}", 'unit': 'kg'},
    ]
    metric_row(metrics)

    st.divider()

    # Timeline chart
    fig = create_cycle_timeline(cycles, selected_month)
    st.plotly_chart(fig, use_container_width=True)

    st.divider()

    # Cycle table
    st.subheader("Cycle Details")

    table_data = []
    for c in cycles:
        display_start = c.get('start_time_et') or c['start_time']
        display_end = c.get('end_time_et') or c['end_time']
        table_data.append({
            'Cycle': c['cycle_id'],
            'Start (ET)': display_start.strftime('%b %d %H:%M'),
            'End (ET)': display_end.strftime('%b %d %H:%M'),
            'Duration (min)': f"{c['duration_minutes']:.1f}",
            'Peak Speed (RPM)': f"{c.get('peak_speed', 0):.0f}",
            'Peak Pressure (bar)': f"{c.get('peak_pressure', 0):.0f}" if c.get('peak_pressure') else 'N/A',
            'Avg Flow (kg/min)': f"{c.get('avg_flow', 0):.2f}" if c.get('avg_flow') else 'N/A',
            'Dispensed (kg)': f"{c.get('total_kg_dispensed', 0):.2f}" if c.get('total_kg_dispensed') else 'N/A',
            'Pump Strokes': f"{c.get('total_pump_strokes', 0):.0f}" if c.get('total_pump_strokes') else 'N/A',
        })

    df_table = pd.DataFrame(table_data)
    st.dataframe(df_table, use_container_width=True, hide_index=True)

    # Store cycles in session state for other pages
    st.session_state.detected_cycles = cycles

    st.info("Select a cycle above, then navigate to **Cycle Detail** page for in-depth analysis.")