add some type annotations

pyproject.toml

@@ -53,6 +53,7 @@ test = [
     "pytest-cov >=3.0.0",
     "pre-commit",
     "mypy >=0.991",
+    "types-requests",
 ]
 doc = [
     "mkdocs >=1.4.2",
@@ -129,7 +130,7 @@ warn_unused_ignores = true
 warn_redundant_casts = true
 disallow_untyped_calls = true
 disallow_untyped_defs = true
-disallow_any_generics = true
+disallow_any_generics = false
 
 
 [tool.pytest.ini_options]

src/trapi_predict_kit/config.py

@@ -1,10 +1,11 @@
 import os
+from typing import Optional
 
 from pydantic import BaseSettings
 
 
 class Settings(BaseSettings):
-    VIRTUAL_HOST: str = None
+    VIRTUAL_HOST: Optional[str] = None
     TIMEOUT: int = 30
 
     DEV_MODE: bool = False

src/trapi_predict_kit/decorators.py

@@ -1,5 +1,5 @@
 import functools
-from typing import Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional
 
 from reasoner_pydantic import MetaEdge, MetaNode
 
@@ -14,16 +14,16 @@ def trapi_predict(
     description: Optional[str] = "",
     default_input: Optional[str] = "drugbank:DB00394",
     default_model: Optional[str] = "openpredict_baseline",
-):
+) -> Callable:
     """A decorator to indicate a function is a function to generate prediction that can be integrated to TRAPI.
     The function needs to take an input_id and returns a list of predicted entities related to the input entity
     """
     if not name:
         name = path
 
-    def decorator(func):
+    def decorator(func: Callable) -> Any:
         @functools.wraps(func)
-        def wrapper(input_id: str, options: Optional[PredictOptions] = None):
+        def wrapper(input_id: str, options: Optional[PredictOptions] = None) -> Callable:
             options = PredictOptions.parse_obj(options) if options else PredictOptions()
             return func(input_id, options)
 

src/trapi_predict_kit/utils.py

@@ -17,7 +17,7 @@
 log.addHandler(console_handler)
 
 
-def get_openpredict_dir(subfolder=""):
+def get_openpredict_dir(subfolder: str = "") -> str:
     """Return the full path to the provided files in the OpenPredict data folder
     Where models and features for runs are stored
     """
@@ -26,7 +26,7 @@ def get_openpredict_dir(subfolder=""):
     return settings.OPENPREDICT_DATA_DIR + subfolder
 
 
-def init_openpredict_dir():
+def init_openpredict_dir() -> None:
     """Create OpenPredict folder and initiate files if necessary."""
     if not os.path.exists(get_openpredict_dir("input/drugbank-drug-goa.csv")):
         raise ValueError(