Personal Planner

Overview

The Personal Planner project integrates various personal data sources such as planners, fitness trackers, financial apps, and more, into a Large Language Model (LLM) to generate personalized reports. This approach aims to provide a unified view of your daily activities, health metrics, and other personal data for more informed and tailored decision-making.

Note: This is a version that is modified to hide all private information and credentials and is not runnable. The private version is also setup to run on GitHub actions each morning.

Features

• Weather Data Integration: Fetches current weather and forecasts for your location. (openweather api)
• Fitness Data Analysis: Summarizes your daily fitness activities. (google fit data accessed via nocode.com)
• Task Management: Keeps track of your planner tasks and deadlines. (usemotion.com api)
• Personalized Reports: Generates tailored daily briefings using an LLM. (openai api)

Sample Output

Getting Started

Prerequisites

Access to APIs (OpenWeather, Google Fit accessed via NoCode, Motion Planner, OpenAI GPT).

Installation

Clone the repository:

git clone https://github.com/ncapek/personal_planner.git

Install required dependencies:

pip install -r requirements.txt

Configuration Set up your API keys and endpoints in config.py.

To run the planner:

python main.py

How It Works

Data Retrieval: The application fetches data from the specified APIs.

Data Processing: Cleans and formats the data for input into the LLM.

Querying the LLM: Sends the compiled data to an LLM (like OpenAI's GPT) for generating the report.

Output: Displays a personalized daily briefing based on the processed data.

Customization

Modify config.py to personalize API endpoints and keys. Adjust the data extraction and processing methods for tailored outputs.