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FlexFlow is a smart scheduler and journaling app powered by machine learning. The user inputs their tasks and it plans out their daily schedule, stress relievers and deadlines. Tasks are analyzed using our model to determine their complexity and time commitment in order to effectively create the most functional schedule.

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dhruva-r/flexflow

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Abstract

As stress, anxiety, and depression continue to rise, it is more important than ever to develop better tools to treat mental health. A common source of poor mental health and stress is the high expectation of productivity placed on students and professionals. Specifically, many people find it difficult to organize time in their demanding schedules. FlexFlow is a smart scheduler and journaling app powered by language processing. The app recognizes the diverse nature of tasks including: difficulty, time commitments, and importance. FlexFlow strategically allocates work-time and breaks to create custom dynamic schedules that factor the user's mood, energy and stress levels. The journaling feature helps users track changes and reflect on daily experiences. Using the journal, FlexFlow suggests activities including exercise, meditation and stretches tailored to the user. The app will use machine learning models to evaluate user’s tasks and determine the estimated complexity and time commitment. FlexFlow intelligently modifies the schedule if tasks are completed sooner or require more time. This helps conserve the time and energy needed to stay organized. The addition of a journaling feature also allows the app to further customize the types of breaks that are suggested.

Detailed Design Document

For more information regarding the design details of flexflow you may refer to this https://docs.google.com/document/d/1PG-6SDR-jn7mCZcn6cfMjm8JwQcMDRdpMykTqr5cIzs/edit?usp=sharing

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FlexFlow is a smart scheduler and journaling app powered by machine learning. The user inputs their tasks and it plans out their daily schedule, stress relievers and deadlines. Tasks are analyzed using our model to determine their complexity and time commitment in order to effectively create the most functional schedule.

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