"""Fetch feed status from the Flask backend and reshape into a DataFrame."""
import os

import pandas as pd
import requests
import streamlit as st

from feed_status.ui.status import chip_label, status_glyph


BASE_URL = os.environ.get('MONTY_API_BASE_URL', 'http://localhost:5000')

FEED_ID_COL = '_feed_id'
FEED_COL = 'Feed'
FRESH_COL = 'Fresh'
STABLE_COL = 'Stable'

_FIXED_COLS = (FEED_COL, FRESH_COL, STABLE_COL)

_SESSION = requests.Session()


@st.cache_data(ttl=120, show_spinner='Loading feed status…')
def fetch_section(apipath, date=None):
    """Fetch one section and return (DataFrame, date_columns, details).

    The DataFrame is wide format with a hidden FEED_ID_COL so selected rows
    can be mapped back to feed_id. `details` maps feed_id → dict with the
    raw 'statuses' and 'status_details' from the API payload.
    """
    url = f'{BASE_URL}{apipath}'
    if date:
        url = f'{url}/{date}'
    response = _SESSION.get(url, timeout=120)
    response.raise_for_status()
    return _reshape(response.json())


@st.cache_data(ttl=600, show_spinner=False)
def fetch_service_links():
    """Fetch the service-link config (Sentry, scheduler, AWS SWF, etc.)."""
    url = f'{BASE_URL}/ingestion_feed/service_links'
    response = _SESSION.get(url, timeout=30)
    response.raise_for_status()
    body = response.json()
    return body.get('supported_links', {}), body.get('service_links', {})


def get_links(feed_id, supported, links):
    """Return {service: url} for the feed, dropping empty URLs."""
    group = supported.get(feed_id)
    if not group:
        return {}
    return {name: url for name, url in (links.get(group) or {}).items() if url}


def _reshape(body):
    """Transform the API JSON payload into a wide DataFrame plus details."""
    dates = list(body.get('dates', []))
    rows = []
    details = {}
    for item in body.get('data', []):
        feed_id = item.get('feed_id', '')
        statuses = item.get('status') or item.get('status_history') or {}
        status_details = item.get('status_details') or {}
        metrics = {m['name']: m['status'] for m in item.get('metrics', [])}
        row = {
            FEED_ID_COL: feed_id,
            FEED_COL: item.get('feed_name', ''),
            FRESH_COL: chip_label(metrics.get('current_status', '')),
            STABLE_COL: chip_label(metrics.get('stability', '')),
        }
        for d in dates:
            row[d] = status_glyph(statuses.get(d))
        rows.append(row)
        details[feed_id] = {
            'statuses': statuses,
            'status_details': status_details,
        }
    df = pd.DataFrame(rows, columns=[FEED_ID_COL, *_FIXED_COLS, *dates])
    return df, dates, details