Update README.md

README.md

@@ -107,15 +107,15 @@ pdf_path_dict = {
 ```
 Prepare an Excel file with paper IDs and their BibTeX entries (see [bibtex_mapping_of_ids.xlsx](./interconnections_datasets/bibtex_mapping_of_ids.xlsx)) that will be needed to map the extracted metadata from the references to the paper of your corpus. Then you can run the GROBID server via Docker (typically on port 8070). For the citations, the notebook uses a confidence-based approach for citation matching, automatically accepting high-confidence matches while flagging uncertain ones for manual review in an Excel file. After reviewing the uncertain matches, run the final cells to create the completed matrices saved as CSV files.
 
-Additionally you may want to configure the Mail-Server to your liking. Change the following code snippet for this use case:
-```python
-# Configure Flask-Mail
-app.config['MAIL_SERVER'] = 'your-smtp-server.example.com'  # Replace with your SMTP server
-app.config['MAIL_PORT'] = 587  # Common port for TLS
-app.config['MAIL_USE_TLS'] = True
-app.config['MAIL_USERNAME'] = 'your-email@example.com'  # Replace with your email
-app.config['MAIL_PASSWORD'] = 'your-password'  # Replace with your password
-app.config['MAIL_DEFAULT_SENDER'] = 'default-sender' # Replace with the default sender
+Additionally you may want to configure the Mail-Server to your liking. The configuration is pulled from your .env file. It has the following parameters:
+```bash
+MAIL_SERVER = "your-smtp-server.example.com"  # Replace with your SMTP server
+MAIL_PORT = 587
+MAIL_USE_TLS = True
+MAIL_USERNAME = "your-email@example.com"
+MAIL_PASSWORD = "your-password"
+MAIL_DEFAULT_SENDER = "default-sender'
+RECIPIENTS= "an-email@example.come"
 ```
 If you are unsure about the some of the configurations, please refer to the [Flask Mail Documentation](https://pypi.org/project/Flask-Mail/).