feature(Inference): Created the inference process, added model saving. (#153)

* feat: Basic scaffolding up for inference process after training.

* feat: Saving and loading models works. Inference works nearly.

* feat: Added inference pipeline.

* feat: Saving model now accoring to date and time; loading models now selects from latest file. Fixed the creation of dictionary of models.

* feat: Added lightweight asset config, but full pipeline.

* feat: Added new naming for dictionary.

* fix: Fixed dictionary naming convention.

* fix: Fixed naming again, now the model structure is good

* fix: Changed the output path and the return values from run_pipeline.

* feat: Added function to make sure folder exists for output models.

Co-authored-by: Daniel Szemerey <szemereydaniel@gmail.com>

models/saving.py

@@ -0,0 +1,37 @@
+import pickle
+import datetime
+from typing import Optional, Union
+import os
+import warnings
+
+
+
+def save_models(all_models_for_all_assets: dict, data_config:dict, training_config:dict) -> None:
+    all_models_for_all_assets['training_config'] = training_config
+    all_models_for_all_assets['data_config'] = data_config
+    
+    date_string = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M")
+    
+    if not os.path.exists('output/models'):
+        warnings.warn("No folder exists, creating one.")
+        os.makedirs('output/models')
+        
+    pickle.dump( all_models_for_all_assets, open( "output/models/{}.p".format(date_string), "wb" ) )
+
+
+def load_models(file_name:Union[str, None]) -> tuple[dict, dict, dict]:
+
+    if file_name is None: 
+        warnings.warn("No file name provided, will load latest models and configurations.")
+        files_in_directory:list = os.listdir('output/models')
+        
+        assert len(files_in_directory) > 0, "No models found in output/models."
+        file_name = sorted(files_in_directory)[-1]
+    
+    all_models_for_all_assets = pickle.load( open( "output/models/{}".format(file_name), "rb" ) )
+    
+    data_config = all_models_for_all_assets['data_config']
+    training_config = all_models_for_all_assets['training_config']
+    
+    return all_models_for_all_assets, data_config, training_config
+