# Developer
To simplify the continuous development of the gmailsorter service, the whole development is based on the `gmailsorter`
python module. While this module is used in the webservice, the docker container and the stand-alone python application
the later two also allow loading the current state of the model directly. 

## Python Interface 
Just install the `gmailsorter` python package and then import the `Gmail` class and the function `load_client_secrets_file`
from the `gmailsorter` module:
```
from gmailsorter import Gmail, load_client_secrets_file
```

### Initialize gmailsorter
Create a `gmail` object from the `Gmail()` class:
```
gmail = Gmail(
    client_config=load_client_secrets_file(
        client_secrets_file="/absolute/path/to/credentials.json"
    ),
    connection_str="sqlite:////absolute/path/to/email.db",
)
```
Based on the configuration from the previous section, the function `load_client_secrets_file` is used to load the
`credentials.json` file and provide its content as python dictionary to the `client_config` parameter of the `Gmail()`
class. In addition to the `client_config` parameter the `Gmail()` class also requires a connection to an SQL database
which is provided as `connection_str`. In addition the `email_download_format` can be specified as either `metadata` or 
`full`, where the primary difference is whether the content of the email is stored or not. 

### Sync local database with email account
To reduce the communication overhead, the emails are stored locally in an SQLite database.
```
gmail.update_database(quick=False)
```
By setting the optional flag `quick` to `True` only new emails are downloaded while changes to existing emails are 
ignored.

### Generate pandas dataframe for emails
Load all emails from the local SQLite database and combine them in a pandas DataFrame for further postprocessing: 
```
df = gmail.get_all_emails_in_database()
```

### Download specific label from email server
Download emails with the label `"MyLabel"` from the email server:
```
df = gmail.download_emails_for_label(label="MyLabel")
```
In this case the emails are not stored in the local SQLite database. 

### Filter emails using machine learning
Assign new email labels to the emails with the label `"MyLabel"`:
```
gmail.filter_messages_from_server
    label="MyLabel",
    recommendation_ratio=0.9,
)
```
This functionality is based on the `download_emails_for_label()` function above. It checks the server for new emails for
a selected label `"MyLabel"`. Then reloads the machine learning model from the local SQLite database and trys to predict
the correct labels for these emails. The `recommendation_ratio` defines the level of certainty required to actually move
the email, with `0.9` equalling a certainty of 90%. 

## Future directions
The current machine learning model is limited in the precision and memory usage. So there is a great interest to replace
it with a computationally more efficient model. All suggestions and feedback are welcome. Beyond the optimization of the
machine learning model and general improvements to the stability of the code base, the extension to other email services
would be great but is currently on hold based on limited resources.