Add ground_truth API and refactor

great_ai/src/great_ai/great_ai/context/configure.py

@@ -1,112 +0,0 @@
-import os
-import random
-from logging import DEBUG, Logger
-from pathlib import Path
-from typing import Optional, Type
-
-import great_ai.great_ai.context.context as context
-from great_ai.large_file import LargeFile, LargeFileLocal
-from great_ai.utilities.logger import get_logger
-
-from ..constants import (
-    DEFAULT_LARGE_FILE_CONFIG_PATHS,
-    DEFAULT_TRACING_DB_FILENAME,
-    ENV_VAR_KEY,
-    PRODUCTION_KEY,
-)
-from ..tracing.parallel_tinydb_driver import ParallelTinyDbDriver, TracingDatabase
-
-
-def configure(
-    log_level: int = DEBUG,
-    seed: int = 42,
-    tracing_database: TracingDatabase = ParallelTinyDbDriver(
-        Path(DEFAULT_TRACING_DB_FILENAME)
-    ),
-    large_file_implementation: Type[LargeFile] = LargeFileLocal,
-    should_log_exception_stack: Optional[bool] = None,
-    prediction_cache_size: int = 512,
-) -> None:
-    logger = get_logger("great_ai", level=log_level)
-
-    if context._context is not None:
-        logger.warn(
-            "Configuration has been already initialised, overwriting.\n"
-            + "Make sure to call `configure()` before importing your application code."
-        )
-
-    is_production = _is_in_production_mode(logger=logger)
-    _initialize_large_file(large_file_implementation, logger=logger)
-    _set_seed(seed)
-
-    if not tracing_database.is_threadsafe:
-        logger.warning(
-            f"The selected persistence driver ({type(tracing_database).__name__}) is not threadsafe"
-        )
-
-    context._context = context.Context(
-        tracing_database=tracing_database,
-        large_file_implementation=large_file_implementation,
-        is_production=is_production,
-        logger=logger,
-        should_log_exception_stack=not is_production
-        if should_log_exception_stack is None
-        else should_log_exception_stack,
-        prediction_cache_size=prediction_cache_size,
-    )
-
-    logger.info("Options: configured ✅")
-
-
-def _is_in_production_mode(logger: Optional[Logger]) -> bool:
-    environment = os.environ.get(ENV_VAR_KEY)
-
-    if environment is None:
-        if logger:
-            logger.warning(
-                f"Environment variable {ENV_VAR_KEY} is not set, defaulting to development mode ‼️"
-            )
-        is_production = False
-    else:
-        is_production = environment.lower() == PRODUCTION_KEY
-        if logger:
-            if not is_production:
-                logger.info(
-                    f"Value of {ENV_VAR_KEY} is `{environment}` which is not equal to `{PRODUCTION_KEY}`"
-                    + "defaulting to development mode ‼️"
-                )
-            else:
-                logger.info("Running in production mode ✅")
-
-    return is_production
-
-
-def _initialize_large_file(large_file: Type[LargeFile], logger: Logger) -> None:
-    path = DEFAULT_LARGE_FILE_CONFIG_PATHS[large_file]
-    if path is None:
-        return
-
-    if large_file.initialized:
-        logger.warning(
-            f"{large_file.__name__} has been already configured: skipping initialisation"
-        )
-        return
-
-    if path.exists():
-        large_file.configure_credentials_from_file(path)
-        logger.info(f"{large_file.__name__} initialised with config ({path.resolve()})")
-    else:
-        logger.warning(
-            f"Default {large_file.__name__} config ({path.resolve()}) not found, skipping {large_file.__name__} initialisation"
-        )
-
-
-def _set_seed(seed: int) -> None:
-    random.seed(seed)
-
-    try:
-        import numpy
-
-        numpy.random.seed(seed + 1)
-    except ImportError:
-        pass