from web.database import query_db

class StatsService:
    @staticmethod
    def get_recent_matches(limit=5):
        sql = """
            SELECT m.match_id, m.start_time, m.map_name, m.score_team1, m.score_team2, m.winner_team,
                   p.username as mvp_name
            FROM fact_matches m
            LEFT JOIN dim_players p ON m.mvp_uid = p.uid
            ORDER BY m.start_time DESC
            LIMIT ?
        """
        return query_db('l2', sql, [limit])

    @staticmethod
    def get_matches(page=1, per_page=20, map_name=None, date_from=None, date_to=None):
        offset = (page - 1) * per_page
        args = []
        where_clauses = ["1=1"]
        
        if map_name:
            where_clauses.append("map_name = ?")
            args.append(map_name)
        
        if date_from:
            where_clauses.append("start_time >= ?")
            args.append(date_from)
            
        if date_to:
            where_clauses.append("start_time <= ?")
            args.append(date_to)
            
        where_str = " AND ".join(where_clauses)
        
        sql = f"""
            SELECT m.match_id, m.start_time, m.map_name, m.score_team1, m.score_team2, m.winner_team, m.duration
            FROM fact_matches m
            WHERE {where_str}
            ORDER BY m.start_time DESC
            LIMIT ? OFFSET ?
        """
        args.extend([per_page, offset])
        
        matches = query_db('l2', sql, args)
        
        # Enrich matches with Avg ELO, Party info, and Our Team Result
        if matches:
            match_ids = [m['match_id'] for m in matches]
            placeholders = ','.join('?' for _ in match_ids)
            
            # Fetch ELO
            elo_sql = f"""
                SELECT match_id, AVG(group_origin_elo) as avg_elo
                FROM fact_match_teams
                WHERE match_id IN ({placeholders}) AND group_origin_elo > 0
                GROUP BY match_id
            """
            elo_rows = query_db('l2', elo_sql, match_ids)
            elo_map = {row['match_id']: row['avg_elo'] for row in elo_rows}
            
            # Fetch Max Party Size
            party_sql = f"""
                SELECT match_id, MAX(cnt) as max_party
                FROM (
                    SELECT match_id, match_team_id, COUNT(*) as cnt
                    FROM fact_match_players
                    WHERE match_id IN ({placeholders}) AND match_team_id > 0
                    GROUP BY match_id, match_team_id
                )
                GROUP BY match_id
            """
            party_rows = query_db('l2', party_sql, match_ids)
            party_map = {row['match_id']: row['max_party'] for row in party_rows}
            
            # --- New: Determine "Our Team" Result ---
            # Logic: Check if any player from `active_roster` played in these matches.
            # Use WebService to get the active roster
            from web.services.web_service import WebService
            import json
            
            lineups = WebService.get_lineups()
            active_roster_ids = []
            if lineups:
                try:
                    # Load IDs and ensure they are all strings for DB comparison consistency
                    raw_ids = json.loads(lineups[0]['player_ids_json'])
                    active_roster_ids = [str(uid) for uid in raw_ids]
                except:
                    pass
            
            # If no roster, we can't determine "Our Result"
            if not active_roster_ids:
                result_map = {}
            else:
                # 1. Get UIDs for Roster Members involved in these matches
                # We query fact_match_players to ensure we get the UIDs actually used in these matches
                roster_placeholders = ','.join('?' for _ in active_roster_ids)
                uid_sql = f"""
                    SELECT DISTINCT steam_id_64, uid 
                    FROM fact_match_players 
                    WHERE match_id IN ({placeholders})
                    AND CAST(steam_id_64 AS TEXT) IN ({roster_placeholders})
                """
                combined_args_uid = match_ids + active_roster_ids
                uid_rows = query_db('l2', uid_sql, combined_args_uid)
                
                # Set of "Our UIDs" (as strings)
                our_uids = set()
                for r in uid_rows:
                    if r['uid']:
                        our_uids.add(str(r['uid']))
                
                # 2. Get Group UIDs and Winner info from fact_match_teams
                # We need to know which group contains our UIDs
                teams_sql = f"""
                    SELECT fmt.match_id, fmt.group_id, fmt.group_uids, m.winner_team
                    FROM fact_match_teams fmt
                    JOIN fact_matches m ON fmt.match_id = m.match_id
                    WHERE fmt.match_id IN ({placeholders})
                """
                teams_rows = query_db('l2', teams_sql, match_ids)
                
                # 3. Determine Result per Match
                result_map = {}
                
                # Group data by match
                match_groups = {} # match_id -> {group_id: [uids...], winner: int}
                
                for r in teams_rows:
                    mid = r['match_id']
                    gid = r['group_id']
                    uids_str = r['group_uids'] or ""
                    # Split and clean UIDs
                    uids = set(str(u).strip() for u in uids_str.split(',') if u.strip())
                    
                    if mid not in match_groups:
                        match_groups[mid] = {'groups': {}, 'winner': r['winner_team']}
                    
                    match_groups[mid]['groups'][gid] = uids
                
                # Analyze
                for mid, data in match_groups.items():
                    winner_gid = data['winner']
                    groups = data['groups']
                    
                    our_in_winner = False
                    our_in_loser = False
                    
                    # Check each group
                    for gid, uids in groups.items():
                        # Intersection of Our UIDs and Group UIDs
                        common = our_uids.intersection(uids)
                        if common:
                            if gid == winner_gid:
                                our_in_winner = True
                            else:
                                our_in_loser = True
                    
                    if our_in_winner and not our_in_loser:
                        result_map[mid] = 'win'
                    elif our_in_loser and not our_in_winner:
                        result_map[mid] = 'loss'
                    elif our_in_winner and our_in_loser:
                        result_map[mid] = 'mixed'
                    else:
                        # Fallback: If UID matching failed (maybe missing UIDs), try old team_id method?
                        # Or just leave it as None (safe)
                        pass

            # Convert to dict to modify
            matches = [dict(m) for m in matches]
            for m in matches:
                m['avg_elo'] = elo_map.get(m['match_id'], 0)
                m['max_party'] = party_map.get(m['match_id'], 1)
                m['our_result'] = result_map.get(m['match_id'])
            
            # Convert to dict to modify
            matches = [dict(m) for m in matches]
            for m in matches:
                m['avg_elo'] = elo_map.get(m['match_id'], 0)
                m['max_party'] = party_map.get(m['match_id'], 1)
                m['our_result'] = result_map.get(m['match_id'])
        
        # Count total for pagination
        count_sql = f"SELECT COUNT(*) as cnt FROM fact_matches WHERE {where_str}"
        total = query_db('l2', count_sql, args[:-2], one=True)['cnt']
        
        return matches, total

    @staticmethod
    def get_match_detail(match_id):
        sql = "SELECT * FROM fact_matches WHERE match_id = ?"
        return query_db('l2', sql, [match_id], one=True)

    @staticmethod
    def get_match_players(match_id):
        sql = """
            SELECT mp.*, p.username, p.avatar_url 
            FROM fact_match_players mp
            LEFT JOIN dim_players p ON mp.steam_id_64 = p.steam_id_64
            WHERE mp.match_id = ?
            ORDER BY mp.team_id, mp.rating DESC
        """
        return query_db('l2', sql, [match_id])

    @staticmethod
    def get_match_rounds(match_id):
        sql = "SELECT * FROM fact_rounds WHERE match_id = ? ORDER BY round_num"
        return query_db('l2', sql, [match_id])

    @staticmethod
    def get_players(page=1, per_page=20, search=None, sort_by='rating_desc'):
        offset = (page - 1) * per_page
        args = []
        where_clauses = ["1=1"]
        
        if search:
            # Force case-insensitive search
            where_clauses.append("(LOWER(username) LIKE LOWER(?) OR steam_id_64 LIKE ?)")
            args.append(f"%{search}%")
            args.append(f"%{search}%")
            
        where_str = " AND ".join(where_clauses)
        
        # Sort mapping
        order_clause = "rating DESC" # Default logic (this query needs refinement as L2 dim_players doesn't store avg rating)
        # Wait, dim_players only has static info. We need aggregated stats.
        # Ideally, we should fetch from L3 for player list stats.
        # But StatsService is for L2. 
        # For the Player List, we usually want L3 data (Career stats).
        # I will leave the detailed stats logic for FeatureService or do a join here if necessary.
        # For now, just listing players from dim_players.
        
        sql = f"""
            SELECT * FROM dim_players
            WHERE {where_str}
            LIMIT ? OFFSET ?
        """
        args.extend([per_page, offset])
        
        players = query_db('l2', sql, args)
        total = query_db('l2', f"SELECT COUNT(*) as cnt FROM dim_players WHERE {where_str}", args[:-2], one=True)['cnt']
        
        return players, total

    @staticmethod
    def get_player_info(steam_id):
        sql = "SELECT * FROM dim_players WHERE steam_id_64 = ?"
        return query_db('l2', sql, [steam_id], one=True)
        
    @staticmethod
    def get_daily_match_counts(days=365):
        # Return list of {date: 'YYYY-MM-DD', count: N}
        sql = """
            SELECT date(start_time, 'unixepoch') as day, COUNT(*) as count
            FROM fact_matches
            WHERE start_time > strftime('%s', 'now', ?)
            GROUP BY day
            ORDER BY day
        """
        # sqlite modifier for 'now' needs format like '-365 days'
        modifier = f'-{days} days'
        rows = query_db('l2', sql, [modifier])
        return rows

    @staticmethod
    def get_players_by_ids(steam_ids):
        if not steam_ids:
            return []
        placeholders = ','.join('?' for _ in steam_ids)
        sql = f"SELECT * FROM dim_players WHERE steam_id_64 IN ({placeholders})"
        return query_db('l2', sql, steam_ids)

    @staticmethod
    def get_player_basic_stats(steam_id):
        # Calculate stats from fact_match_players
        # Prefer calculating from sums (kills/deaths) for K/D accuracy
        # AVG(adr) is used as damage_total might be missing in some sources
        sql = """
            SELECT 
                AVG(rating) as rating, 
                SUM(kills) as total_kills,
                SUM(deaths) as total_deaths,
                AVG(kd_ratio) as avg_kd, 
                AVG(kast) as kast,
                AVG(adr) as adr,
                COUNT(*) as matches_played
            FROM fact_match_players
            WHERE steam_id_64 = ?
        """
        row = query_db('l2', sql, [steam_id], one=True)
        
        if row and row['matches_played'] > 0:
            res = dict(row)
            
            # Calculate K/D: Sum Kills / Sum Deaths
            kills = res.get('total_kills') or 0
            deaths = res.get('total_deaths') or 0
            
            if deaths > 0:
                res['kd'] = kills / deaths
            else:
                res['kd'] = kills # If 0 deaths, K/D is kills (or infinity, but kills is safer for display)
                
            # Fallback to avg_kd if calculation failed (e.g. both 0) but avg_kd exists
            if res['kd'] == 0 and res['avg_kd'] and res['avg_kd'] > 0:
                res['kd'] = res['avg_kd']
                
            # ADR validation
            if res['adr'] is None:
                res['adr'] = 0.0
                
            return res
        return None

    @staticmethod
    def get_shared_matches(steam_ids):
        # Find matches where ALL steam_ids were present
        if not steam_ids or len(steam_ids) < 1:
            return []
            
        placeholders = ','.join('?' for _ in steam_ids)
        count = len(steam_ids)
        
        # We need to know which team the players were on to determine win/loss
        # Assuming they were on the SAME team for "shared experience"
        # If count=1, it's just match history
        
        # Query: Get matches where all steam_ids are present
        # Also join to get team_id to check if they were on the same team (optional but better)
        # For simplicity in v1: Just check presence in the match.
        # AND check if the player won.
        
        # We need to return: match_id, map_name, score, result (Win/Loss)
        # "Result" is relative to the lineup.
        # If they were on the winning team, it's a Win.
        
        sql = f"""
            SELECT m.match_id, m.start_time, m.map_name, m.score_team1, m.score_team2, m.winner_team,
                   MAX(mp.team_id) as player_team_id -- Just take one team_id (assuming same)
            FROM fact_matches m
            JOIN fact_match_players mp ON m.match_id = mp.match_id
            WHERE mp.steam_id_64 IN ({placeholders})
            GROUP BY m.match_id
            HAVING COUNT(DISTINCT mp.steam_id_64) = ?
            ORDER BY m.start_time DESC
        """
        
        args = list(steam_ids)
        args.append(count)
        
        rows = query_db('l2', sql, args)
        
        results = []
        for r in rows:
            # Determine if Win
            # winner_team in DB is 'Team 1' or 'Team 2' usually, or the team name.
            # fact_matches.winner_team stores the NAME of the winner? Or 'team1'/'team2'?
            # Let's check how L2_Builder stores it. Usually it stores the name.
            # But fact_match_players.team_id stores the name too.
            
            # Logic: If m.winner_team == mp.team_id, then Win.
            is_win = (r['winner_team'] == r['player_team_id'])
            
            # If winner_team is NULL or empty, it's a draw?
            if not r['winner_team']:
                result_str = 'Draw'
            elif is_win:
                result_str = 'Win'
            else:
                result_str = 'Loss'
                
            res = dict(r)
            res['is_win'] = is_win # Boolean for styling
            res['result_str'] = result_str # Text for display
            results.append(res)
            
        return results

    @staticmethod
    def get_player_trend(steam_id, limit=20):
        # We need party_size: count of players with same match_team_id in the same match
        # Using a correlated subquery for party_size
        sql = """
            SELECT * FROM (
                SELECT 
                    m.start_time, 
                    mp.rating, 
                    mp.kd_ratio, 
                    mp.adr, 
                    m.match_id, 
                    m.map_name, 
                    mp.is_win,
                    mp.match_team_id,
                    (SELECT COUNT(*) 
                     FROM fact_match_players p2 
                     WHERE p2.match_id = mp.match_id 
                     AND p2.match_team_id = mp.match_team_id
                     AND p2.match_team_id > 0 -- Ensure we don't count 0 (solo default) as a massive party
                    ) as party_size
                FROM fact_match_players mp
                JOIN fact_matches m ON mp.match_id = m.match_id
                WHERE mp.steam_id_64 = ?
                ORDER BY m.start_time DESC
                LIMIT ?
            ) ORDER BY start_time ASC
        """
        return query_db('l2', sql, [steam_id, limit])

    @staticmethod
    def get_live_matches():
        # Query matches started in last 2 hours with no winner
        # Assuming we have a way to ingest live matches. 
        # For now, this query is 'formal' but will likely return empty on static dataset.
        sql = """
            SELECT m.match_id, m.map_name, m.score_team1, m.score_team2, m.start_time
            FROM fact_matches m
            WHERE m.winner_team IS NULL 
            AND m.start_time > strftime('%s', 'now', '-2 hours')
        """
        return query_db('l2', sql)

    @staticmethod
    def get_head_to_head_stats(match_id):
        """
        Returns a matrix of kills between players.
        List of {attacker_steam_id, victim_steam_id, kills}
        """
        sql = """
            SELECT attacker_steam_id, victim_steam_id, COUNT(*) as kills
            FROM fact_round_events
            WHERE match_id = ? AND event_type = 'kill'
            GROUP BY attacker_steam_id, victim_steam_id
        """
        return query_db('l2', sql, [match_id])

    @staticmethod
    def get_match_round_details(match_id):
        """
        Returns a detailed dictionary of rounds, events, and economy.
        {
            round_num: {
                info: {winner_side, win_reason_desc, end_time_stamp...},
                events: [ {event_type, event_time, attacker..., weapon...}, ... ],
                economy: { steam_id: {main_weapon, equipment_value...}, ... }
            }
        }
        """
        # 1. Base Round Info
        rounds_sql = "SELECT * FROM fact_rounds WHERE match_id = ? ORDER BY round_num"
        rounds_rows = query_db('l2', rounds_sql, [match_id])
        
        if not rounds_rows:
            return {}

        # 2. Events
        events_sql = """
            SELECT * FROM fact_round_events 
            WHERE match_id = ? 
            ORDER BY round_num, event_time
        """
        events_rows = query_db('l2', events_sql, [match_id])
        
        # 3. Economy (if avail)
        eco_sql = """
            SELECT * FROM fact_round_player_economy 
            WHERE match_id = ?
        """
        eco_rows = query_db('l2', eco_sql, [match_id])
        
        # Structure Data
        result = {}
        
        # Initialize rounds
        for r in rounds_rows:
            r_num = r['round_num']
            result[r_num] = {
                'info': dict(r),
                'events': [],
                'economy': {}
            }
            
        # Group events
        for e in events_rows:
            r_num = e['round_num']
            if r_num in result:
                result[r_num]['events'].append(dict(e))
                
        # Group economy
        for eco in eco_rows:
            r_num = eco['round_num']
            sid = eco['steam_id_64']
            if r_num in result:
                result[r_num]['economy'][sid] = dict(eco)
                
        return result