Update config.py

config.py

@@ -4,66 +4,56 @@ import os
 from dataclasses import dataclass
 from pathlib import Path
 
-
 BASE_DIR = Path(__file__).resolve().parent
 DATA_DIR = BASE_DIR / "data"
 LOCAL_LOG_DIR = BASE_DIR / "logs"
 
 
+def env_bool(name: str, default: bool = False) -> bool:
+    return os.getenv(name, "1" if default else "0").strip().lower() in {"1", "true", "yes", "on"}
+
+
 @dataclass(frozen=True)
 class Settings:
+    # App
     app_name: str = os.getenv("APP_NAME", "Trading Game Study AI")
     app_version: str = os.getenv("APP_VERSION", "2.0.0")
     port: int = int(os.getenv("PORT", "7860"))
 
-    embedding_model: str = os.getenv(
-        "EMBEDDING_MODEL",
-        "sentence-transformers/all-MiniLM-L6-v2",
-    )
-    cross_encoder_model: str = os.getenv(
-        "CROSS_ENCODER_MODEL",
-        "cross-encoder/ms-marco-MiniLM-L-6-v2",
-    )
-    generator_model: str = os.getenv(
-        "GENERATOR_MODEL",
-        "google/flan-t5-small",
-    )
-    generator_task: str = os.getenv(
-        "GENERATOR_TASK",
-        "text2text-generation",
-    )
+    # Models
+    embedding_model: str = os.getenv("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+    cross_encoder_model: str = os.getenv("CROSS_ENCODER_MODEL", "cross-encoder/ms-marco-MiniLM-L-6-v2")
+    generator_model: str = os.getenv("GENERATOR_MODEL", "google/flan-t5-small")
+    generator_task: str = os.getenv("GENERATOR_TASK", "text2text-generation")
     generator_max_new_tokens: int = int(os.getenv("GENERATOR_MAX_NEW_TOKENS", "220"))
     generator_temperature: float = float(os.getenv("GENERATOR_TEMPERATURE", "0.6"))
     generator_top_p: float = float(os.getenv("GENERATOR_TOP_P", "0.9"))
-    generator_do_sample: bool = os.getenv("GENERATOR_DO_SAMPLE", "1") == "1"
+    generator_do_sample: bool = env_bool("GENERATOR_DO_SAMPLE", True)
 
-    local_chunks_path: str = os.getenv(
-        "LOCAL_CHUNKS_PATH",
-        str(DATA_DIR / "gmat_hf_chunks.jsonl"),
-    )
-    question_seed_path: str = os.getenv(
-        "QUESTION_SEED_PATH",
-        str(DATA_DIR / "gmat_question_seed.jsonl"),
-    )
-    topic_index_path: str = os.getenv(
-        "TOPIC_INDEX_PATH",
-        str(DATA_DIR / "gmat_topic_index.json"),
-    )
+    # Local data
+    local_chunks_path: str = os.getenv("LOCAL_CHUNKS_PATH", str(DATA_DIR / "gmat_hf_chunks.jsonl"))
+    question_seed_path: str = os.getenv("QUESTION_SEED_PATH", str(DATA_DIR / "gmat_question_seed.jsonl"))
+    topic_index_path: str = os.getenv("TOPIC_INDEX_PATH", str(DATA_DIR / "gmat_topic_index.json"))
+
+    # Retrieval
     dataset_repo_id: str = os.getenv("DATASET_REPO_ID", "j-js/gmat-quant-corpus")
     dataset_split: str = os.getenv("DATASET_SPLIT", "train")
     retrieval_k: int = int(os.getenv("RETRIEVAL_K", "8"))
     rerank_k: int = int(os.getenv("RERANK_K", "4"))
     max_chunks_to_show: int = int(os.getenv("MAX_CHUNKS_TO_SHOW", "3"))
     max_reply_chars: int = int(os.getenv("MAX_REPLY_CHARS", "1600"))
-    enable_remote_dataset_fallback: bool = os.getenv("ENABLE_REMOTE_DATASET_FALLBACK", "1") == "1"
+    enable_remote_dataset_fallback: bool = env_bool("ENABLE_REMOTE_DATASET_FALLBACK", True)
 
+    # Logging
     local_log_dir: str = os.getenv("LOCAL_LOG_DIR", str(LOCAL_LOG_DIR))
+    push_logs_to_hub: bool = env_bool("PUSH_LOGS_TO_HUB", False)
+    log_dataset_repo_id: str = os.getenv("LOG_DATASET_REPO_ID", "")
+    log_dataset_private: bool = env_bool("LOG_DATASET_PRIVATE", True)
+
+    # Secrets
+    hf_token: str = os.getenv("HF_TOKEN", "")
     ingest_api_key: str = os.getenv("INGEST_API_KEY", "")
     research_api_key: str = os.getenv("RESEARCH_API_KEY", "")
-    hf_token: str = os.getenv("HF_TOKEN", "")
-    log_dataset_repo_id: str = os.getenv("LOG_DATASET_REPO_ID", "")
-    log_dataset_private: bool = os.getenv("LOG_DATASET_PRIVATE", "1") == "1"
-    push_logs_to_hub: bool = os.getenv("PUSH_LOGS_TO_HUB", "0") == "1"
 
 
-settings = Settings()
+settings = Settings()