144 lines
5.5 KiB
Python
144 lines
5.5 KiB
Python
import json
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from openai import AsyncOpenAI
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from app.core.config import settings
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from app.schemas import ChallengeGenerated
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class GPTService:
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"""Service for generating challenges using OpenAI GPT"""
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def __init__(self):
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self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
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async def generate_challenges(
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self,
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games: list[dict]
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) -> dict[int, list[ChallengeGenerated]]:
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"""
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Generate challenges for multiple games in one API call.
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Args:
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games: List of dicts with keys: id, title, genre
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Returns:
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Dict mapping game_id to list of generated challenges
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"""
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if not games:
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return {}
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games_text = "\n".join([
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f"- {g['title']}" + (f" (жанр: {g['genre']})" if g.get('genre') else "")
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for g in games
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])
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prompt = f"""Ты — эксперт по видеоиграм. Сгенерируй по 6 КОНКРЕТНЫХ челленджей для каждой из следующих игр:
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{games_text}
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ВАЖНО: Челленджи должны быть СПЕЦИФИЧНЫМИ для каждой игры!
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- Используй РЕАЛЬНЫЕ названия локаций, боссов, персонажей, миссий, уровней из игры
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- Основывайся на том, какие челленджи РЕАЛЬНО делают игроки в этой игре
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- НЕ генерируй абстрактные челленджи типа "пройди уровень" или "убей 10 врагов"
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Требования по сложности ДЛЯ КАЖДОЙ ИГРЫ:
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- 2 лёгких (15-30 мин): простые задачи
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- 2 средних (1-2 часа): требуют навыка
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- 2 сложных (3+ часа): серьёзный челлендж
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Формат ответа — JSON с объектом где ключи это ТОЧНЫЕ названия игр, как они указаны в запросе:
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{{
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"Название игры 1": {{
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"challenges": [
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{{"title": "...", "description": "...", "type": "completion|no_death|speedrun|collection|achievement|challenge_run", "difficulty": "easy|medium|hard", "points": 50, "estimated_time": 30, "proof_type": "screenshot|video|steam", "proof_hint": "..."}}
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]
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}},
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"Название игры 2": {{
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"challenges": [...]
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}}
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}}
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points: easy=20-40, medium=45-75, hard=90-150
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Ответь ТОЛЬКО JSON."""
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response = await self.client.chat.completions.create(
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model="gpt-5-mini",
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messages=[{"role": "user", "content": prompt}],
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response_format={"type": "json_object"},
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)
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content = response.choices[0].message.content
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data = json.loads(content)
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# Map game titles to IDs (case-insensitive, strip whitespace)
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title_to_id = {g['title'].lower().strip(): g['id'] for g in games}
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# Also keep original titles for logging
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id_to_title = {g['id']: g['title'] for g in games}
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print(f"[GPT] Requested games: {[g['title'] for g in games]}")
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print(f"[GPT] Response keys: {list(data.keys())}")
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result = {}
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for game_title, game_data in data.items():
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# Try exact match first, then case-insensitive
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game_id = title_to_id.get(game_title.lower().strip())
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if not game_id:
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# Try partial match if exact match fails
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for stored_title, gid in title_to_id.items():
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if stored_title in game_title.lower() or game_title.lower() in stored_title:
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game_id = gid
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break
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if not game_id:
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print(f"[GPT] Could not match game: '{game_title}'")
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continue
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challenges = []
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for ch in game_data.get("challenges", []):
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challenges.append(self._parse_challenge(ch))
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result[game_id] = challenges
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print(f"[GPT] Generated {len(challenges)} challenges for '{id_to_title.get(game_id)}'")
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return result
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def _parse_challenge(self, ch: dict) -> ChallengeGenerated:
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"""Parse and validate a single challenge from GPT response"""
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ch_type = ch.get("type", "completion")
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if ch_type not in ["completion", "no_death", "speedrun", "collection", "achievement", "challenge_run"]:
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ch_type = "completion"
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difficulty = ch.get("difficulty", "medium")
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if difficulty not in ["easy", "medium", "hard"]:
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difficulty = "medium"
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proof_type = ch.get("proof_type", "screenshot")
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if proof_type not in ["screenshot", "video", "steam"]:
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proof_type = "screenshot"
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points = ch.get("points", 30)
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if not isinstance(points, int) or points < 1:
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points = 30
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if difficulty == "easy":
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points = max(20, min(40, points))
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elif difficulty == "medium":
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points = max(45, min(75, points))
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elif difficulty == "hard":
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points = max(90, min(150, points))
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return ChallengeGenerated(
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title=ch.get("title", "Unnamed Challenge")[:100],
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description=ch.get("description", "Complete the challenge"),
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type=ch_type,
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difficulty=difficulty,
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points=points,
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estimated_time=ch.get("estimated_time"),
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proof_type=proof_type,
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proof_hint=ch.get("proof_hint"),
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)
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gpt_service = GPTService()
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