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1d9dc1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | import os
from dataclasses import dataclass
from pathlib import Path
from dotenv import load_dotenv
DEFAULT_LLM_MODEL_FILE = "Llama-3.2-1B-Instruct-Q4_K_M.gguf"
def read_int(name, default_value):
raw_value = os.getenv(name, str(default_value)).strip()
try:
return int(raw_value)
except ValueError:
return default_value
def resolve_path(root_dir, raw_value):
path = Path(raw_value)
if path.is_absolute():
return path
return root_dir / path
def resolve_llm_model_path(models_dir, model_file):
if model_file:
requested_path = models_dir / model_file
if requested_path.exists():
return requested_path
default_path = models_dir / DEFAULT_LLM_MODEL_FILE
if default_path.exists():
return default_path
gguf_files = sorted(models_dir.glob("*.gguf"))
if gguf_files:
return gguf_files[0]
return models_dir / (model_file or DEFAULT_LLM_MODEL_FILE)
def make_local_model_dir(hf_cache_dir, repo_id):
safe_name = repo_id.replace("/", "--")
return hf_cache_dir / "downloads" / safe_name
def resolve_hf_source(hf_cache_dir, repo_id):
local_dir = make_local_model_dir(hf_cache_dir, repo_id)
if local_dir.exists():
return local_dir
return repo_id
@dataclass(frozen=True)
class Settings:
project_root: Path
models_dir: Path
hf_cache_dir: Path
data_dir: Path
llm_model_file: str
llm_model_path: Path
context_size: int
live_asr_model_id: str
live_asr_source: str | Path
batch_asr_model_id: str
batch_asr_source: str | Path
image_model_id: str
image_model_source: str | Path
tts_en_model_id: str
tts_en_source: str | Path
tts_hi_model_id: str
tts_hi_source: str | Path
asr_compute_type: str
embedding_model_id: str
tesseract_cmd: str
def load_settings():
project_root = Path(__file__).resolve().parent.parent
load_dotenv(project_root / ".env")
models_dir = resolve_path(project_root, os.getenv("EDUAI_MODELS_DIR", "models"))
hf_cache_dir = resolve_path(project_root, os.getenv("EDUAI_HF_CACHE_DIR", "models/hf_cache"))
data_dir = resolve_path(project_root, os.getenv("EDUAI_DATA_DIR", "data"))
models_dir.mkdir(parents=True, exist_ok=True)
hf_cache_dir.mkdir(parents=True, exist_ok=True)
data_dir.mkdir(parents=True, exist_ok=True)
os.environ.setdefault("HF_HOME", str(hf_cache_dir))
os.environ.setdefault("HF_HUB_CACHE", str(hf_cache_dir / "hub"))
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
llm_model_file = os.getenv("EDUAI_LLM_MODEL_FILE", DEFAULT_LLM_MODEL_FILE).strip()
live_asr_model_id = os.getenv("EDUAI_LIVE_ASR_MODEL_ID", "Systran/faster-whisper-small").strip()
batch_asr_model_id = os.getenv("EDUAI_BATCH_ASR_MODEL_ID", "Systran/faster-whisper-large-v3").strip()
image_model_id = os.getenv("EDUAI_IMAGE_MODEL_ID", "HuggingFaceTB/SmolVLM-256M-Instruct").strip()
tts_en_model_id = os.getenv("EDUAI_TTS_EN_MODEL_ID", "facebook/mms-tts-eng").strip()
tts_hi_model_id = os.getenv("EDUAI_TTS_HI_MODEL_ID", "facebook/mms-tts-hin").strip()
embedding_model_id = os.getenv("EDUAI_EMBEDDING_MODEL_ID", "sentence-transformers/all-MiniLM-L6-v2").strip()
return Settings(
project_root=project_root,
models_dir=models_dir,
hf_cache_dir=hf_cache_dir,
data_dir=data_dir,
llm_model_file=llm_model_file,
llm_model_path=resolve_llm_model_path(models_dir, llm_model_file),
context_size=read_int("EDUAI_CONTEXT_SIZE", 8192),
live_asr_model_id=live_asr_model_id,
live_asr_source=resolve_hf_source(hf_cache_dir, live_asr_model_id),
batch_asr_model_id=batch_asr_model_id,
batch_asr_source=resolve_hf_source(hf_cache_dir, batch_asr_model_id),
image_model_id=image_model_id,
image_model_source=resolve_hf_source(hf_cache_dir, image_model_id),
tts_en_model_id=tts_en_model_id,
tts_en_source=resolve_hf_source(hf_cache_dir, tts_en_model_id),
tts_hi_model_id=tts_hi_model_id,
tts_hi_source=resolve_hf_source(hf_cache_dir, tts_hi_model_id),
asr_compute_type=os.getenv("EDUAI_ASR_COMPUTE_TYPE", "int8").strip() or "int8",
embedding_model_id=embedding_model_id,
tesseract_cmd=os.getenv("TESSERACT_CMD", "").strip(),
)
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