This Flask application leverages OpenAI's GPT for auto-summarizing and smart tagging of notes, and a machine learning model for task prioritization. It provides a user-friendly interface for note analysis, organization, and exportation.
- Auto-Summarize Notes: Summarizes notes using OpenAI's GPT.
- Smart Tagging: Generates relevant tags for each note.
- Task Prioritization: Prioritizes notes using a machine learning model.
- Database Integration: Stores and manages notes in a SQLite database.
- Export Functionality: Allows exporting notes to a CSV file.
- Python 3
- Flask
- OpenAI API key
- Pandas
- NumPy
- scikit-learn
- SQLite3
- Clone or download the application.
- Install the required Python packages:
pip install Flask pandas numpy scikit-learn openai
- Set your OpenAI API key in the script.
Create a SQLite database named notes.db
with a table structure suitable for storing notes. Example SQL:
CREATE TABLE notes (
id INTEGER PRIMARY KEY,
title TEXT,
text TEXT,
labels TEXT
);
- Start the Flask application:
python app.py
- Access the web interface at
http://localhost:5000
. - Input notes for analysis, tagging, and prioritization.
- View and download the organized notes.
GET /
: Home page.POST /analyze
: Processes and organizes the input notes.GET /download
: Downloads the notes as a CSV file.
- Set
openai.api_key
to your OpenAI API key. - Customize the machine learning model and data processing as needed.
- Efficient note-taking and summarization for meetings and lectures.
- Organizing and prioritizing tasks and to-do lists.
- Exporting notes for use in other applications like Google Keep.
This is a demonstration application. Modify and use it according to your requirements and OpenAI usage policies.