State Management API

from state.workflow import init_workflow_state

init_workflow_state()

from state.workflow import generate_session_id

session_id = generate_session_id()
# Returns: "swift-golden-falcon"

from state.workflow import get_session_id

session_id = get_session_id()

from state.workflow import clear_workflow_state

clear_workflow_state()

from state.workflow import save_dataset_config

config = {
    "dataset_path": "/path/to/dataset",
    "train_split": 70,
    "val_split": 15,
    "test_split": 15,
    "target_size": "224x224",
    "color_mode": "RGB"
}
save_dataset_config(config)

from state.workflow import get_dataset_config

config = get_dataset_config()

from state.workflow import has_dataset_config

if has_dataset_config():
    print("Dataset is configured")

from state.workflow import add_model_to_library

config = {
    "model_type": "Custom CNN",
    "cnn_config": {
        "layers": [
            {"type": "conv", "filters": 32, "kernel_size": 3},
            {"type": "maxpool", "pool_size": 2}
        ]
    }
}

model_id = add_model_to_library("My CNN Model", config)
# Returns: "model_a3f8b2c1"

from state.workflow import get_model_from_library

model = get_model_from_library("model_a3f8b2c1")
if model:
    print(f"Model: {model['name']}")
    print(f"Type: {model['model_type']}")

from state.workflow import update_model_in_library

success = update_model_in_library(
    "model_a3f8b2c1",
    "Updated CNN Model",
    updated_config
)

from state.workflow import delete_model_from_library

success = delete_model_from_library("model_a3f8b2c1")

from state.workflow import get_model_library

models = get_model_library()
for model in models:
    print(f"{model['name']} ({model['model_type']})")

from state.workflow import add_training_to_library

config = {
    "epochs": 50,
    "batch_size": 32,
    "learning_rate": 0.001,
    "optimizer": "adam"
}

training_id = add_training_to_library("Standard Training", config)
# Returns: "train_d4e9f1a2"

from state.workflow import get_training_library

trainings = get_training_library()
for training in trainings:
    print(f"{training['name']}: {training['config']['epochs']} epochs")

from state.workflow import create_experiment

exp_id = create_experiment(
    "CNN Baseline",
    "model_a3f8b2c1",
    "train_d4e9f1a2"
)
# Returns: "exp_b7c4d2e5"

from state.workflow import get_experiment

experiment = get_experiment("exp_b7c4d2e5")
if experiment:
    print(f"Status: {experiment['status']}")
    print(f"Current epoch: {experiment['current_epoch']}")

from state.workflow import update_experiment

success = update_experiment("exp_b7c4d2e5", {
    "status": "training",
    "current_epoch": 10,
    "metrics": {
        "train_loss": 0.234,
        "val_acc": 0.892
    }
})

from state.workflow import delete_experiment

success = delete_experiment("exp_b7c4d2e5")

from state.workflow import get_experiments

experiments = get_experiments()
for exp in experiments:
    print(f"{exp['name']}: {exp['status']}")

from state.persistence import save_session
from state.workflow import get_session_id

session_id = get_session_id()
if save_session(session_id):
    print("Session saved successfully")

from state.persistence import load_session

if load_session("swift-golden-falcon"):
    print("Session loaded successfully")

from state.persistence import list_saved_sessions

sessions = list_saved_sessions()
for session in sessions:
    print(f"Session: {session}")

from state.persistence import delete_session

if delete_session("old-session-id"):
    print("Session deleted successfully")

from state.cache import get_dataset_info

info = get_dataset_info()
if info:
    print(f"Total training samples: {info['total_train']}")
    print(f"Total validation samples: {info['total_val']}")
    print(f"Classes: {', '.join(info['classes'])}")

from state.cache import set_dataset_info

info = {
    "total_train": 1000,
    "total_val": 200,
    "classes": ["malware_a", "malware_b"],
    "train_samples": {"malware_a": 600, "malware_b": 400},
    "val_samples": {"malware_a": 120, "malware_b": 80}
}
set_dataset_info(info)

from state.cache import clear_cache_state

clear_cache_state()

class WorkflowState(TypedDict, total=False):
    session_id: str
    dataset_config: dict[str, Any]
    model_library: list[dict[str, Any]]
    training_library: list[dict[str, Any]]
    experiments: list[dict[str, Any]]
    model_config: dict[str, Any]  # Legacy
    training_config: dict[str, Any]  # Legacy
    training_active: bool  # Legacy
    monitor_config: dict[str, Any]  # Legacy
    results: dict[str, Any] | None  # Legacy