streamlit-json-tip 0.2.3

A Streamlit custom component for viewing JSON with interactive tooltips and tags

Streamlit JSON Tip

A Streamlit custom component for viewing JSON data with interactive tooltips and tags for individual fields.

Features

• 🔍 Interactive JSON Viewer: Expand/collapse objects and arrays
• 📝 Interactive Tooltips: Add contextual help for any field with professional Tippy.js tooltips
• 🏷️ Field Tags: Categorize fields with colored tags (PII, CONFIG, etc.)
• 🎯 Field Selection: Click on fields to get detailed information
• 🎨 Syntax Highlighting: Color-coded JSON with proper formatting
• 📱 Responsive Design: Works well in different screen sizes

Installation

From PyPI (Recommended)

pip install streamlit-json-tip

From TestPyPI (Latest Development Version)

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ streamlit-json-tip

Development Setup

1. Clone this repository:

   git clone https://github.com/isaac/streamlit-json-tip.git
   cd streamlit-json-tip
   

2. Set up development environment with uv:

   # Install uv if you haven't already
   curl -LsSf https://astral.sh/uv/install.sh | sh
   
   # Create virtual environment and install all dependencies (including dev dependencies)
   uv sync
   

3. Run the example app:

   uv run streamlit run example_app.py
   

Usage

import streamlit as st
from streamlit_json_tip import json_viewer

# Your JSON data
data = {
    "user": {
        "id": 123,
        "name": "John Doe",
        "email": "[e-mail]"
    }
}

# Help text for specific fields
help_text = {
    "user.id": "Unique user identifier",
    "user.name": "Full display name",
    "user.email": "Primary contact email"
}

# Tags for categorizing fields
tags = {
    "user.id": "ID",
    "user.name": "PII",
    "user.email": "PII"
}

# Display the JSON viewer
selected = json_viewer(
    data=data,
    help_text=help_text,
    tags=tags,
    height=400
)

# Handle field selection
if selected:
    st.write(f"Selected field: {selected['path']}")
    st.write(f"Value: {selected['value']}")
    if selected.get('help_text'):
        st.write(f"Help: {selected['help_text']}")

Parameters

• data (dict): The JSON data to display
• help_text (dict, optional): Dictionary mapping field paths to help text
• tags (dict, optional): Dictionary mapping field paths to tags/labels
• height (int, optional): Height of the component in pixels (default: 400)
• key (str, optional): Unique key for the component

Field Path Format

Field paths use dot notation for objects and bracket notation for arrays:

• "user.name" - Object field
• "items[0].title" - Array item field
• "settings.preferences.theme" - Nested object field

Development

Frontend Development

1. Set up the development environment (see Development Setup above)

2. Navigate to the frontend directory:

   cd streamlit_json_tip/frontend
   

3. Install frontend dependencies:

   npm install
   

4. Start development server:

   npm start
   

5. In your Python code, set _RELEASE = False in __init__.py

6. Run the example app in another terminal:

   uv run streamlit run example_app.py
   

Building for Production

1. Build the frontend:

   cd streamlit_json_tip/frontend
   npm run build
   

2. Set _RELEASE = True in __init__.py

3. Build the Python package:

   uv run python -m build
   

4. Upload to PyPI:

   uv run python -m twine upload dist/*
   

Build Scripts

The project includes convenient uv scripts for common development tasks:

Frontend Development

uv run build-frontend        # Build React frontend for production

Package Building

uv run clean                 # Clean build artifacts
uv run build                 # Clean + build Python package
uv run build-check           # Build + validate package with twine

Publishing

uv run publish-test          # Build + upload to TestPyPI
uv run publish               # Build + upload to PyPI

Complete Workflow

uv run release-test          # Build frontend + publish to TestPyPI
uv run release               # Build frontend + publish to PyPI (production)

For a complete release, simply run:

uv run release

This will build the frontend, package the Python distribution, validate it, and upload to PyPI.

License

MIT License