converts long chain paragraphs into short and precise summary Text summarisation file flow
- Make all the required file using python by utilising os path and logging modules
- Fill the requirement .txt
- Fill the setup.py
- If the software is needed I will install on spot
- Fill logging/init.py for custom logging
-
- Update config.yaml
- Params.yaml
- Entity
- Update configuration manager in src config
- Update components
- Update pipeline
- Update main.py
- Update app.py
- Note—> after I updated the config file with data ingestion I skipped the updation of params because I do not have any params yet
- After importing constant fill the constant/init.py
- After defiing the configuration master fill params.yaml with dummy value so that it doesn’t return any error
- After expermenting in the trails.ipynb in research folder copy paste the codes according the workflow mentioned above
- In components you have to create a data_ingestion.ipynb file to paste the component code
- In the model trainer stage the params.yaml should be filled with the trainable parameters The CI/CD deployment on AWS is yet to be updated