Hey I have asked you for an app idea and you gave me this idea and plan. WE were doing the plan but i needed to reset you because of the token limit. We have completed the first task. We created models and dao's for them. ProductOrder, ProductOrderDao, ProductionLine, ProductionLineDao, InventoryLevel, InventoryLevelDao, SupplierLeadTime, SupplierLeadTimeDao. Now did we finish task 1? If so lets carry on with 2

The idea you gave: One app idea could be a smart production scheduling and forecasting tool. This app would use machine learning algorithms to analyze historical production data and generate accurate production schedules and forecasts. It could also integrate with other production-related data such as inventory levels and supplier lead times to help optimize the scheduling process. The app could generate alerts and recommendations for production planners to help them make more informed decisions when managing their production schedules. Additionally, it could provide real-time updates and notifications to operators and plant managers to keep them informed of any production delays or issues that may arise.

The plan you gave: Great, here’s a plan to get you started:

1. Define your data model: Start by defining the data model for your app, including the entities you need to store data for production scheduling and forecasting. For example, you may need to store data on product orders, production lines, inventory levels, and supplier lead times.

2. Set up your Flask app: Create a new Flask app, set up your virtual environment, and install the necessary dependencies. You may also want to set up a development database to test your APIs.

3. Create your API endpoints: Define the endpoints you need for your app, including endpoints for GET, POST, PUT, and DELETE requests for managing production data. For example, you may need endpoints for adding new product orders, modifying production schedules, or retrieving inventory levels.

4. Implement authentication and authorization: Add authentication and authorization features to your API to secure access to production data. You can use Flask-Login or Flask-Security to handle user authentication.

5. Add machine learning features for forecasting: Once you have set up the basic structure of your app, you can start exploring how you can add machine learning features to your forecasting tool. You may want to use a library like TensorFlow, Keras or PyTorch for this.

6. Test your app: Use automated testing frameworks like Pytest to test your app thoroughly.

7. Deploy to production: Once you have tested your app successfully, you can deploy it to production. You can use services like Heroku or AWS to deploy your Flask app.