metadata

title: Mbti Pocketflow
emoji: 🔥
colorFrom: pink
colorTo: gray
sdk: gradio
sdk_version: 5.38.2
app_file: app.py
pinned: false
short_description: 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

Project Structure

/mbt/
├── 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
└── 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

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

Implement utility functions in utils/
Create or modify nodes in nodes.py
Update flow in flow.py
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.