https://github.com/drogbadvc/st_graphistry_link
Graphistry Visualization Tool in Streamlit
https://github.com/drogbadvc/st_graphistry_link
graph screaming-frog streamlit visualization
Last synced: about 2 months ago
JSON representation
Graphistry Visualization Tool in Streamlit
- Host: GitHub
- URL: https://github.com/drogbadvc/st_graphistry_link
- Owner: drogbadvc
- Created: 2024-11-22T10:31:49.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-25T13:59:32.000Z (over 1 year ago)
- Last Synced: 2025-01-27T16:48:16.024Z (over 1 year ago)
- Topics: graph, screaming-frog, streamlit, visualization
- Language: Python
- Homepage:
- Size: 869 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Graphistry + Screaming Frog Visualization Tool
This project provides an interactive tool for visualizing relationships between entities using [Graphistry](https://hub.graphistry.com/). It is designed for CSV data exported from Screaming Frog, containing `Source` and `Target` columns, it leverages Streamlit for a user-friendly web interface.
---
## Features
- Upload and process CSV files with `Source` and `Target` (or `Destination`) columns.
- Visualize relationships using Graphistry.
- Filter and display unique links.
- Scalable processing for large datasets with Polars or Pandas.
---
## Installation
1. Clone the repository:
```bash
git clone https://github.com/drogbadvc/st_graphistry_link.git
cd graphistry-project
```
2. Create a virtual environment (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Start the Streamlit app:
```bash
streamlit run main.py
```
2. Open your browser and go to http://localhost:8501.
3. Upload a CSV file and generate a Graphistry visualization.
## Project Structure
* main.py: The main Streamlit app.
* graphistry_utils.py: Helper functions for Graphistry visualization.
* data_processing.py: Functions for cleaning and validating CSV data.
* ui_components.py: Streamlit UI elements.
* requirements.txt: Dependencies for the project.