import os
from pathlib import Path

APP_ID = "com.cht.StreamAgent"
APP_NAME = "CHT"

# Default session data location — in project dir for easy clearing
PROJECT_DIR = Path(__file__).resolve().parent.parent
DATA_DIR = PROJECT_DIR / "data"
SESSIONS_DIR = Path(os.environ.get("CHT_SESSIONS_DIR", DATA_DIR / "sessions"))

# Stream defaults
STREAM_HOST = "0.0.0.0"
STREAM_PORT = 4444
RELAY_PORT = 4445  # UDP loopback relay for live display

# Frame extraction — scene-only, no interval fallback
SCENE_THRESHOLD = 0.10  # 0-1, lower = more sensitive; 0.1 catches slide/window changes
SCENE_FLUSH_FRAMES = 2  # extra frames after scene change to flush encoder/muxer buffer (0 to disable)

# Segment recording
SEGMENT_DURATION = 60  # seconds per .ts segment

# Audio extraction
AUDIO_EXTRACT_INTERVAL = 3    # seconds between extraction cycles
AUDIO_SAFETY_MARGIN = 2       # seconds safety margin (matches scene detector)
WAVEFORM_BUCKET_MS = 50       # milliseconds per waveform peak bucket

# Transcription
WHISPER_MODEL = "medium"       # "small" for speed, "medium" for accuracy
WHISPER_DEVICE = "cuda"        # "cuda" or "cpu"
TRANSCRIBE_MIN_CHUNK_S = 5     # minimum seconds of audio before transcribing
TRANSCRIBE_LINES_PER_GROUP = 3 # whisper segments grouped per transcript ID (1-5)

# Agent settings
AGENT_PERMISSION_MODE = "bypassPermissions"  # default|acceptEdits|plan|bypassPermissions|dontAsk
AGENT_MAX_TURNS = 5

# Offline summarization (post-session diarization + export)
# whisperx lives in its own venv to avoid dep clashes with cht's faster_whisper.
# Defaults mirror transcribe_oneoff.sh: large-v3 + int8 fits in ~3-4 GB VRAM and
# is less hallucination-prone than medium on long meetings.
WHISPERX_BIN = os.environ.get("CHT_WHISPERX_BIN", "/home/mariano/wdir/venv/def/bin/whisperx")
WHISPERX_MODEL = os.environ.get("CHT_WHISPERX_MODEL", "large-v3")
WHISPERX_DEVICE = os.environ.get("CHT_WHISPERX_DEVICE", "cuda")
WHISPERX_COMPUTE_TYPE = os.environ.get("CHT_WHISPERX_COMPUTE_TYPE", "int8")
WHISPERX_BATCH_SIZE = int(os.environ.get("CHT_WHISPERX_BATCH_SIZE", "4"))
HF_TOKEN = os.environ.get("HF_TOKEN")  # required for pyannote diarization
DEFAULT_PARTICIPANTS = 2
WHISPERX_LD_LIBRARY_PATH = os.environ.get("CHT_WHISPERX_LD_LIBRARY_PATH")  # cuDNN override