title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned	short_description
Mbti Pocketflow	🔥	pink	gray	gradio	5.38.2	app.py	false	PocketFlow application for conducting Myers-Briggs + llm

MBTI Personality Questionnaire

A PocketFlow-based application for conducting Myers-Briggs Type Indicator (MBTI) personality assessments with both traditional scoring and AI analysis.

Features

• 20/40/60-question MBTI questionnaire with selectable length for accuracy
• Traditional scoring algorithm for baseline personality type determination
• AI-powered analysis using LLM for detailed personality insights with question references
• Interactive Gradio web interface with auto-save and progress tracking
• HTML report generation with clickable question references and comprehensive analysis
• Data export/import for saving and resuming questionnaires
• CLI and test modes for different use cases
• 🆕 MCP Server for LLMs to take MBTI tests themselves via Model Context Protocol

Project Structure

├── utils/
│   ├── call_llm.py          # LLM integration (Gemini)
│   ├── questionnaire.py     # Question sets (20/40/60) and loading/saving
│   ├── mbti_scoring.py      # Traditional MBTI scoring
│   ├── report_generator.py  # HTML report generation with markdown support
│   └── test_data.py         # Test data generation
├── nodes.py                 # PocketFlow nodes (LoadQuestionnaire, LLMAnalysis, etc.)
├── flow.py                  # PocketFlow flow definition
├── app.py                   # **Main Gradio web interface with LLM**
├── pf_cli.py                # PocketFlow CLI interface
├── README.md                # Main README
├── server.py                # FastMCP server for LLMs to take MBTI tests
├── MCP_README.md            # MCP README
├── Dockerfile               # Dockerfile for MCP server deployment
└── requirements.txt         # Dependencies

Quick Start

1. Web Interface (Recommended)

# Install dependencies
pip install -r requirements.txt

# Set up Gemini API key
export GEMINI_API_KEY="your-api-key-here"
# Or on Windows:
set GEMINI_API_KEY=your-api-key-here

# Run Gradio web interface
python app.py

Then open http://127.0.0.1:7860 in your browser.

2. Command Line Interface

# Run CLI questionnaire
python pf_cli.py

# Run with test data
python pf_cli.py --test --test-type INTJ

# Import previous questionnaire
python pf_cli.py --import-file questionnaire.json

3. MCP Server (For LLMs)

# Install MCP server dependencies
pip install -r requirements.txt

# Set up API key
export GEMINI_API_KEY="your-api-key-here"

# Run MCP server
python server.py

Allows LLMs to take MBTI tests via Model Context Protocol. See MCP_README.md for details.

4. Live demo on HF Spaces

https://huggingface.co/spaces/Fancellu/mbti-pocketflow

Usage Examples

Gradio Web Interface

python app.py

• Interactive web interface at http://127.0.0.1:7860
• Question length selection (20/40/60 questions)
• Auto-save responses as you navigate
• Progress tracking and export functionality
• AI analysis with clickable question references
• HTML report generation with comprehensive insights
• Load/save questionnaires for resuming later

Command Line Interface

python pf_cli.py

• Complete PocketFlow architecture
• Traditional scoring with optional LLM analysis
• Automatic report generation
• Data import/export in JSON format

Test Modes

# CLI test with specific MBTI type
python pf_cli.py --test --test-type ENFP

Import/Export

# Export: Questionnaire data automatically saved as:
# mbti_questionnaire_pf_partial_[COUNT]q_[TIMESTAMP].json

# Import: Load previous questionnaire
python pf_cli.py --import-file questionnaire.json

MBTI Types Supported

The application recognizes all 16 MBTI personality types:

Analysts: INTJ, INTP, ENTJ, ENTP
Diplomats: INFJ, INFP, ENFJ, ENFP
Sentinels: ISTJ, ISFJ, ESTJ, ESFJ
Explorers: ISTP, ISFP, ESTP, ESFP

Key Features

Question Sets

• 20 questions: Quick assessment (5 per dimension)
• 40 questions: Balanced assessment (10 per dimension)
• 60 questions: Comprehensive assessment (15 per dimension)

AI Analysis

• Question-specific insights with clickable references like Q1
• Out-of-character response detection and explanations
• Evidence-based analysis citing specific question responses
• Behavioral pattern identification
• Strengths and growth areas based on actual responses

Web Interface Features

• Auto-save responses on navigation
• Progress tracking with completion indicators
• Export progress at any time (partial questionnaires)
• Load previous sessions to continue where you left off
• Immediate download of reports and data

Development

Adding New Features

2. Implement utility functions in utils/
3. Create or modify nodes in nodes.py
4. Update flow in flow.py
5. Test with CLI and web interface

Dependencies

Required:

• Python 3.8+
• gradio>=4.0.0 (web interface)
• google-genai>=0.3.0 (LLM analysis)
• beautifulsoup4 (HTML parsing)
• markdown (report generation)

Optional:

• pydantic (enhanced data validation)

See requirements.txt for complete list.

File Overview

• gradio_pf_llm.py - Main web interface with full LLM analysis
• pf_cli.py - Command line interface using PocketFlow architecture
• nodes.py - PocketFlow node implementations
• flow.py - PocketFlow pipeline definition
• utils/ - Core utility functions (questionnaire, scoring, reports, LLM)

License

This project follows PocketFlow's open-source approach for educational and research purposes.