Prettygraph

An experimental UI that transforms text into knowledge graphs.

CommonProductProgrammingKnowledge GraphText Analysis

Prettygraph is a Python-based web application developed by @yoheinakajima, showcasing a new UI paradigm for dynamically converting text input into knowledge graphs. This project is a quick prototype aimed at providing a simple UI idea, generating knowledge graphs by highlighting the text in the UI in real-time.

Text to Graph Generation: Converts user input text into a knowledge graph.
Dynamic UI Updates: Each text input ending with a period updates the graph.
Color-Coded Visualization: Nodes and edges in the graph are colored for visual distinction.
Real-Time Updates: The graph is updated in real-time after each period
providing an interactive experience.
Dependency Management: Dependency management is facilitated using Poetry
simplifying project setup.
Environment Variables Configuration: The OPENAI_API_KEY environment variable must be set to run the application.
Open Source License: The project follows the MIT License
offering open-source code.

Researchers and Data Scientists: Utilize Prettygraph to rapidly convert textual data into visualized knowledge graphs for analysis and comprehension.
Educators: Use Prettygraph as a tool to present complex concepts and relationships during teaching.
Developers: Prettygraph offers an experimental starting point for developers seeking to integrate knowledge graph generation into their applications.

Convert the abstract of a research paper into a knowledge graph to allow researchers to quickly grasp the key points of the article.
In the education field
convert textbook content into graphs to assist students in memorizing and understanding.
In business intelligence
convert market research reports into graphs to reveal market trends and competitive relationships.

Clone Repository: Use the git command to clone Prettygraph's codebase locally.
Navigate to Project Directory: Use the command line to navigate to the cloned Prettygraph project folder.
Install Dependencies: Use the Poetry command to install the dependencies required for the project.
Configure Environment Variables: Create a .env file in the project root directory and add OPENAI_API_KEY.
Run Application: Use 'poetry run python main.py' to start the Flask application.

Visit

Prettygraph Visit Over Time

Monthly Visits

490881889

Bounce Rate

37.92%

Page per Visit

5.6

Visit Duration

00:06:18

Prettygraph Visit Trend

Prettygraph Visit Geography

Prettygraph Traffic Sources

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