# Enables postponed evaluation of type annotations for forward-compatible typing behavior
from __future__ import annotations

# Provides access to environment variables used to configure the application at runtime
import os

# defines the display title used by the ML app
APP_TITLE = "Agentic Machine Learning"

# reads the Hugging Face authentication token from the environment and removes surrounding whitespace
HF_TOKEN = os.getenv("HF_TOKEN", "").strip()

# selects the HF model ID while providing a default model when none is configured
HF_MODEL_ID = os.getenv(
    "HF_MODEL_ID",
    "Qwen/Qwen3-Coder-30B-A3B-Instruct",
).strip()

# selects the HF inference provider and falls back to automatic provider selection
HF_PROVIDER = os.getenv("HF_PROVIDER", "auto").strip() or "auto"

# sets the max allowed upload size in megabytes from an environment variable or default value
MAX_UPLOAD_MB = int(os.getenv("MAX_UPLOAD_MB", "50"))

# limits the number of rows used when profiling uploaded datasets
MAX_PROFILE_ROWS = int(os.getenv("MAX_PROFILE_ROWS", "100000"))

# limits the number of rows used during model training to control runtime and resource usage
MAX_TRAIN_ROWS = int(os.getenv("MAX_TRAIN_ROWS", "25000"))

# max number of tool-driven reasoning steps the agent may execute
MAX_AGENT_STEPS = int(os.getenv("MAX_AGENT_STEPS", "4"))

# default max number of tokens allowed in generated model responses
DEFAULT_MAX_TOKENS = int(os.getenv("DEFAULT_MAX_TOKENS", "1800"))

# deterministic random seed
RANDOM_STATE = int(os.getenv("RANDOM_STATE", "42"))

# Maximum ZeroGPU allocation for the decorated agent callback.
# Hugging Face ZeroGPU defaults to 60 seconds; this project allows more time
# because the agent can execute several bounded tool/inference steps.
ZERO_GPU_DURATION = int(os.getenv("ZERO_GPU_DURATION", "120"))

# lists the dataset file extensions accepted by the app
SUPPORTED_DATA_EXTENSIONS = {
    ".csv",
    ".parquet",
    ".json",
    ".jsonl",
    ".xlsx",
    ".xls",
}

# HF repo containing the bundled example dataset
EXAMPLE_DATASET_REPO = "scikit-learn/adult-census-income"

# filename of the example dataset within the repo
EXAMPLE_DATASET_FILE = "adult.csv"

# target column used for the example ML modeling task
EXAMPLE_DATASET_TARGET = "income"