Personify - AI-Powered Personality Test App

A beautiful Flutter application that uses machine learning to determine personality types (Introvert vs Extrovert) based on user responses to psychological questions.

Features

• AI-Powered Analysis: Uses TensorFlow Lite model trained on personality data
• Beautiful UI/UX: Modern, animated interface with smooth transitions
• Multiple Screens: Welcome screen, interactive test, and detailed results
• Real-time Progress: Visual progress tracking during the test
• Detailed Results: Confidence scores, descriptions, and personality traits
• Cross-platform: Runs on iOS and Android

Technologies Used

• Flutter: Cross-platform mobile app framework
• TensorFlow Lite: On-device machine learning inference
• Provider: State management
• Go Router: Navigation and routing
• Google Fonts: Beautiful typography
• Animated Text Kit: Text animations
• Staggered Animations: Smooth UI transitions

Project Structure

lib/
├── main.dart                 # App entry point and routing
├── models/                   # Data models
│   ├── personality_question.dart
│   └── personality_result.dart
├── providers/                # State management
│   └── personality_provider.dart
├── screens/                  # UI screens
│   ├── welcome_screen.dart
│   ├── personality_test_screen.dart
│   └── results_screen.dart
├── services/                 # Business logic
│   └── ml_service.dart
└── widgets/                  # Reusable UI components
    ├── animated_button.dart
    ├── gradient_background.dart
    ├── question_card.dart
    ├── result_card.dart
    └── trait_chip.dart

assets/
└── models/                   # ML model files
    ├── personality_model.tflite
    └── preprocessing_params.json

model_training/               # Python ML training scripts
└── train_personality_model.py

Getting Started

Prerequisites

• Flutter SDK (>=3.8.1)
• Dart SDK
• Android Studio / Xcode for mobile development
• Python 3.x (for model training)

Installation

1. Clone the repository

   git clone <repository-url>
   cd personify

2. Install Flutter dependencies

   flutter pub get

3. Run the app

   flutter run

Model Training (Optional)

If you want to retrain the model with your own data:

1. Install Python dependencies

   pip install tensorflow pandas scikit-learn numpy

2. Train the model

   cd model_training
   python train_personality_model.py

3. Copy new model files to assets

   cp personality_model.tflite ../assets/models/
   cp preprocessing_params.json ../assets/models/

How It Works

Data Collection

The app asks users 7 key questions about their personality traits:

• Time spent alone preferences
• Stage fear levels
• Social event attendance
• Outdoor activity preferences
• Energy after socializing
• Preferred friend circle size
• Social media posting frequency

Machine Learning Model

• Architecture: Neural network with dense layers (64→32→16→1)
• Training: Achieved 91.3% accuracy on personality classification
• Features: 7 normalized input features
• Output: Binary classification (Introvert vs Extrovert) with confidence score

Mobile Integration

• TensorFlow Lite: Optimized model for mobile inference
• Preprocessing: Real-time feature scaling and normalization
• Results: Detailed personality analysis with traits and descriptions

App Screens

1. Welcome Screen

• Animated app introduction
• Feature highlights
• Beautiful gradient background
• "Start Test" call-to-action

2. Personality Test

• 7 interactive questions
• Visual progress indicator
• Smooth transitions between questions
• Answer selection with immediate feedback

3. Results Screen

• Personality type announcement
• Confidence score visualization
• Detailed description
• Key personality traits
• Option to retake test

Performance & Optimization

• Model Size: ~50KB TensorFlow Lite model
• Inference Time: <100ms on average mobile device
• Memory Usage: Minimal memory footprint
• Animations: 60fps smooth animations with proper disposal

Testing

Run the test suite:

flutter test

Building for Production

Android

flutter build apk --release

iOS

flutter build ios --release

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Personality dataset for training the ML model
• Flutter community for excellent packages
• TensorFlow team for mobile ML capabilities

personify