#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ NBA AI Summary Generator Generuje podsumowania występów ulubionych graczy NBA przy użyciu Claude AI """ import mysql.connector from datetime import datetime, timedelta import anthropic from config import DB_CONFIG, ANTHROPIC_API_KEY class NBAPlayerTracker: def __init__(self): self.db = mysql.connector.connect(**DB_CONFIG['nba']) self.cursor = self.db.cursor(dictionary=True) self.client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY) def get_player_name(self, player_id): """Pobiera imię i nazwisko gracza""" self.cursor.execute(""" SELECT first_name, last_name, team_id FROM players WHERE player_id = %s """, (player_id,)) return self.cursor.fetchone() def get_team_name(self, team_id): """Pobiera nazwę drużyny""" self.cursor.execute(""" SELECT full_name, abbreviation FROM team_info WHERE team_id = %s """, (team_id,)) return self.cursor.fetchone() def get_recent_game_stats(self, player_id, days=1): """ Pobiera statystyki gracza z ostatnich N dni """ date_from = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d') query = """ SELECT pgs.*, g.game_date, g.home_team_id, g.away_team_id, g.home_team_score, g.away_team_score, g.status FROM player_game_stats pgs JOIN games g ON pgs.game_id = g.game_id WHERE pgs.player_id = %s AND g.game_date >= %s AND g.status = 'Final' ORDER BY g.game_date DESC """ self.cursor.execute(query, (player_id, date_from)) return self.cursor.fetchall() def format_game_data(self, player_id, stats): """ Formatuje dane meczu do czytelnej formy """ if not stats: return None stat = stats[0] # Najnowszy mecz player_info = self.get_player_name(player_id) player_team = self.get_team_name(stat['team_id']) # Określ przeciwnika opponent_id = stat['away_team_id'] if stat['team_id'] == stat['home_team_id'] else stat['home_team_id'] opponent = self.get_team_name(opponent_id) # Określ wynik dla drużyny gracza is_home = stat['team_id'] == stat['home_team_id'] team_score = stat['home_team_score'] if is_home else stat['away_team_score'] opp_score = stat['away_team_score'] if is_home else stat['home_team_score'] result = "W" if team_score > opp_score else "L" # Oblicz FG%, 3P%, FT% fg_pct = round(stat['field_goals_made'] / stat['field_goals_attempted'] * 100, 1) if stat['field_goals_attempted'] > 0 else 0 three_pct = round(stat['three_pointers_made'] / stat['three_pointers_attempted'] * 100, 1) if stat['three_pointers_attempted'] > 0 else 0 ft_pct = round(stat['free_throws_made'] / stat['free_throws_attempted'] * 100, 1) if stat['free_throws_attempted'] > 0 else 0 return { 'player_name': f"{player_info['first_name']} {player_info['last_name']}", 'team': player_team['full_name'], 'team_abbr': player_team['abbreviation'], 'opponent': opponent['full_name'], 'opponent_abbr': opponent['abbreviation'], 'result': result, 'team_score': team_score, 'opp_score': opp_score, 'date': stat['game_date'], 'minutes': stat['minutes_played'], 'points': stat['points'], 'rebounds': stat['rebounds'], 'assists': stat['assists'], 'steals': stat['steals'], 'blocks': stat['blocks'], 'turnovers': stat['turnovers'], 'fg_made': stat['field_goals_made'], 'fg_attempted': stat['field_goals_attempted'], 'fg_pct': fg_pct, 'three_made': stat['three_pointers_made'], 'three_attempted': stat['three_pointers_attempted'], 'three_pct': three_pct, 'ft_made': stat['free_throws_made'], 'ft_attempted': stat['free_throws_attempted'], 'ft_pct': ft_pct, 'plus_minus': stat['plus_minus'] } def generate_ai_summary(self, game_data): """ Generuje naturalne podsumowanie występu gracza przez Claude AI """ prompt = f"""Jesteś komentratorem sportowym NBA. Napisz krótkie, naturalne podsumowanie występu gracza w meczu NBA. DANE MECZU: Gracz: {game_data['player_name']} ({game_data['team_abbr']}) Mecz: {game_data['team_abbr']} vs {game_data['opponent_abbr']} Wynik: {game_data['team_score']}-{game_data['opp_score']} ({game_data['result']}) Data: {game_data['date']} STATYSTYKI: - Minuty: {game_data['minutes']} - Punkty: {game_data['points']} - Zbiórki: {game_data['rebounds']} - Asysty: {game_data['assists']} - Przechwyty: {game_data['steals']} - Bloki: {game_data['blocks']} - Straty: {game_data['turnovers']} - Rzuty z gry: {game_data['fg_made']}/{game_data['fg_attempted']} ({game_data['fg_pct']}%) - Trójki: {game_data['three_made']}/{game_data['three_attempted']} ({game_data['three_pct']}%) - Rzuty wolne: {game_data['ft_made']}/{game_data['ft_attempted']} ({game_data['ft_pct']}%) - +/-: {game_data['plus_minus']:+d} WYMAGANIA: 1. Napisz 2-3 zdania po polsku 2. Użyj naturalnego języka komentarza sportowego 3. Podkreśl najważniejsze aspekty występu (dobre/słabe) 4. Wspomniij o wyniku meczu 5. Nie używaj bullet points, tylko ciągły tekst Przykład dobrego stylu: "Kevin Durant poprowadził Suns do zwycięstwa nad Lakers 118-114, zdobywając 32 punkty przy skuteczności 60% z gry. Do tego dołożył 8 zbiórek i 5 asyst, dominując szczególnie w czwartej kwarcie gdzie zdobył 12 punktów." """ message = self.client.messages.create( model="claude-sonnet-4-20250514", max_tokens=300, messages=[{ "role": "user", "content": prompt }] ) return message.content[0].text def get_all_summaries(self, days=1): """ Generuje podsumowania dla wszystkich ulubionych graczy """ summaries = [] for player_id in FAVORITE_PLAYERS: stats = self.get_recent_game_stats(player_id, days) if not stats: continue game_data = self.format_game_data(player_id, stats) if not game_data: continue # Generuj AI summary ai_summary = self.generate_ai_summary(game_data) summaries.append({ 'player_id': player_id, 'player_name': game_data['player_name'], 'team': game_data['team_abbr'], 'opponent': game_data['opponent_abbr'], 'result': game_data['result'], 'score': f"{game_data['team_score']}-{game_data['opp_score']}", 'date': game_data['date'], 'stats': { 'minutes': game_data['minutes'], 'points': game_data['points'], 'rebounds': game_data['rebounds'], 'assists': game_data['assists'], 'fg': f"{game_data['fg_made']}/{game_data['fg_attempted']}", 'fg_pct': game_data['fg_pct'], 'three': f"{game_data['three_made']}/{game_data['three_attempted']}", 'plus_minus': game_data['plus_minus'] }, 'ai_summary': ai_summary, 'full_data': game_data }) return summaries def close(self): self.cursor.close() self.db.close() if __name__ == "__main__": # Test tracker = NBAPlayerTracker() summaries = tracker.get_all_summaries(days=2) print(f"\n🏀 NBA Player Summaries ({len(summaries)} graczy)\n") print("=" * 80) for summary in summaries: print(f"\n{summary['player_name']} ({summary['team']})") print(f"vs {summary['opponent']} • {summary['score']} ({summary['result']}) • {summary['date']}") print(f"📊 {summary['stats']['points']} PTS | {summary['stats']['rebounds']} REB | {summary['stats']['assists']} AST") print(f"\n{summary['ai_summary']}") print("-" * 80) tracker.close()