heroku deployment failed: reason - empty database

[mukeshmk]

Heroku Deployment Failed: reason - empty database.

when the application is deployed for the first time or the started after the database is cleared, the feature to load the data and train a model is breaking as the data frame loaded is an empty one and getting Xy for the model training fails with the error.

Traceback (most recent call last):
  File "app.py", line 24, in <module>
    from src.api import metrics_api
  File "/home/blake/code/fm-learn/src/api.py", line 22, in <module>
    fml.load_data()
  File "/home/blake/code/fm-learn/src/fmlearn.py", line 60, in load_data
    self._X, self._y = utils.get_Xy(self._df)
  File "/home/blake/code/fm-learn/src/utils/utils.py", line 60, in get_Xy
    y = df[[DATASET_HASH]]
  File "/home/blake/code/fm-learn/venv/lib/python3.7/site-packages/pandas/core/frame.py", line 2806, in __getitem__
    indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
  File "/home/blake/code/fm-learn/venv/lib/python3.7/site-packages/pandas/core/indexing.py", line 1553, in _get_listlike_indexer
    keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing
  File "/home/blake/code/fm-learn/venv/lib/python3.7/site-packages/pandas/core/indexing.py", line 1640, in _validate_read_indexer
    raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Dataset Hash'], dtype='object')] are in the [columns]"

Possible Fix:
Stop training the model if the data frame is empty? and train later once data is available.

Other Issues to be considered:

• prediction of an algorithm when no model/dataset exists (cold start problem) #18 prediction of an algorithm when no model/dataset exists (cold start problem)
• when to load the data and train model for the first time #19 when to load the data and train model when said scenario occurs?