CogniConnect 🧠

A full-stack AI-powered memory support application designed to help individuals with dementia and Alzheimer's maintain connections with their memories through intelligent video analysis, face recognition, and interactive cognitive exercises. Built with Next.js, Flask, and OpenAI's GPT-4 Vision API.

✨ Features

🎥 Video Memory Processing

Automatic Video Upload: Upload video journals with metadata (title, date, description, emotions, people)
Smart Chunking: Splits videos into 10-second chunks for efficient processing
Audio Transcription: Extracts and transcribes audio using speech recognition
Face Detection: Automatic face recognition in video frames using face_recognition library
Frame Extraction: ORB keypoint detection for extracting unique frames
AI Analysis: GPT-4 Vision API analyzes video content and generates contextual Q&A

🔍 Memory Explorer

Semantic Search: Natural language queries to find specific memories
Video Playback: Sequential playback of video chunks with auto-advance
Random Folder Selection: Randomized video folder selection while maintaining chunk order
Transcript Display: Real-time synchronized transcripts during playback
Face Recognition Tags: Visual indicators of recognized people in videos

🧩 Cognitive Quiz System

Activity 1: Face Recognition

Present faces from videos and known contacts
Track correct/incorrect selections
Scoring: (correct - incorrect) / total × 100
Mix of real faces and distractor images

Activity 2: Name-Face Matching

Interactive bubble-clicking game
Match names to correct faces
Dynamic name generation for similar distractors
Binary scoring: 100 points for correct match

Activity 3: Context Questions

AI-generated questions about video memories
GPT-4 powered answer verification
Sequential question flow with manual navigation
Score averaging: (sum of all question scores / 5) × 10

Comprehensive Scoring

Combines all 3 activities into unified performance metric
Detailed breakdown per activity
Historical tracking with timestamps
Visual feedback and progress indicators

📊 Performance Dashboard

Memory Trends Graph: Weekly performance visualization
Activity Breakdown: Individual scores for each quiz activity
Statistics Cards:
- Average score tracking
- Recent performance metrics
- Improvement indicators
- Completion counts
Monthly Analysis: Week-by-week performance within current month
Real-time Updates: Dynamic data refresh after each quiz

👥 Faces Management

Known Faces Directory: Upload and manage familiar faces
Dynamic Loading: Automatic detection of uploaded face images
Name Normalization: Converts filenames to display names (e.g., jawed_karim.jpg → "Jawed Karim")
Static Distractors: Pre-loaded faces for quiz variety
Grid Display: Visual grid of all known faces with names

📝 Emergency Contacts

Quick Access: Important contact information display
Contact Cards: Name, relationship, phone, email, address
Important Notes: Critical information and emergency instructions
Theme Support: Light/dark mode compatibility

🌓 Theme System

Dark/Light Mode Toggle: Seamless theme switching
System Preference Detection: Automatic theme based on OS settings
Persistent Preferences: Theme choice saved in localStorage
Comprehensive Coverage: All pages and components theme-aware
Smooth Transitions: Animated icon changes and color transitions

🛠️ Tech Stack

Backend

Runtime: Python 3.13+
Framework: Flask 3.x with CORS support
AI/ML:
- OpenAI GPT-4 Vision API
- face_recognition library (dlib-based)
- SpeechRecognition for audio transcription
Video Processing:
- MoviePy 2.x for video manipulation
- OpenCV (cv2) for frame extraction
- ORB keypoint detection for unique frame filtering
Data Storage: JSON-based file storage
- qas.json: Quiz questions and answers
- video_journals.json: Video metadata
- performance_data.json: Quiz performance history
Utilities:
- python-dotenv for environment variables
- NumPy for numerical operations

Frontend

Framework: Next.js 14.0.3 (App Router)
UI Library: React 18.2.0
Styling:
- Tailwind CSS 3.3.0
- Tailwind Animate for transitions
- Custom CSS variables for theming
Component Libraries:
- Radix UI (Dialog, Popover, Select, Tabs, etc.)
- Lucide React for icons
- shadcn/ui component patterns
Charting: Chart.js 4.4.0 with react-chartjs-2
Theme Management: next-themes 0.2.1
Date Handling: date-fns 2.30.0
Image Optimization: Sharp 0.32.6 (Next.js Image)
TypeScript: Full type safety with TS 5

🚀 Getting Started

Prerequisites

Python 3.13+ installed
Node.js 20+ installed
Redis Server (optional, for future pub/sub features)
OpenAI API Key (for GPT-4 Vision)

Installation

Clone the repository

git clone https://github.com/InfiKnight-TGP/Cogni-Connect.git
cd Cogni-Connect

Backend Setup

cd backend

# Create virtual environment
python -m venv .venv

# Activate virtual environment
.venv\Scripts\Activate.ps1

# Install dependencies
pip install -r requirements.txt

# Create .env file
# Add your OpenAI API key:
# OPENAI_API_KEY=your_api_key_here

Frontend Setup

cd frontend

# Install dependencies
npm install

Running the Application

Option 1: Quick Start (Two Terminals)

Terminal 1 - Backend:

cd "D:\Cogni Connect\backend"
python app.py

Terminal 2 - Frontend:

cd "D:\Cogni Connect\frontend"
npm run dev

Option 2: Development Mode

Backend (Terminal 1):

cd backend
.venv\Scripts\Activate.ps1
python app.py

Expected output:

* Serving Flask app 'app'
* Debug mode: on
* Running on http://127.0.0.1:5000

Frontend (Terminal 2):

cd frontend
npm run dev

Expected output:

▲ Next.js 14.0.3
- Local: http://localhost:3000
✓ Ready in 5s

Access the Application

Main App: http://localhost:3000
API Server: http://localhost:5000

📁 Project Structure

Cogni-Connect/
├── backend/
│   ├── app.py                          # Flask API server
│   ├── process_video.py                # Video processing pipeline
│   ├── requirements.txt                # Python dependencies
│   ├── .env                            # Environment variables (API keys)
│   ├── content/                        # Uploaded video files
│   ├── known_faces/                    # Known face images
│   │   ├── jawed_karim.jpg
│   │   ├── bharath.jpg
│   │   ├── dinesh.jpg
│   │   └── ... (more faces)
│   ├── frames/                         # Extracted video frames
│   │   └── [video_name]_chunks/
│   ├── video_chunks/                   # Processed video chunks
│   │   └── [video_name]_chunks/
│   │       ├── chunk_0000_0010.mp4
│   │       ├── chunk_0000_0010_faces.json
│   │       └── full_transcript.json
│   ├── qas.json                        # Generated Q&A data
│   ├── video_journals.json             # Video metadata
│   └── performance_data.json           # Quiz scores
│
├── frontend/
│   ├── app/
│   │   ├── layout.tsx                  # Root layout with theme provider
│   │   ├── page.tsx                    # Home/Dashboard
│   │   ├── globals.css                 # Global styles + Tailwind
│   │   ├── api/
│   │   │   ├── accuracy/route.tsx      # AI answer scoring
│   │   │   ├── questions/route.tsx     # Quiz questions API
│   │   │   ├── semSearch/route.tsx     # Semantic search
│   │   │   └── videos/route.tsx        # Video list API
│   │   ├── contacts/
│   │   │   └── page.tsx                # Emergency contacts
│   │   ├── dashboard/
│   │   │   ├── page.tsx                # Performance dashboard
│   │   │   └── graph.tsx               # Memory trends chart
│   │   ├── explore/
│   │   │   └── page.tsx                # Memory explorer (video player)
│   │   ├── faces/
│   │   │   └── page.tsx                # Known faces grid
│   │   ├── quiz/
│   │   │   └── page.tsx                # Quiz orchestrator
│   │   └── upload/
│   │       └── page.tsx                # Video upload interface
│   ├── components/
│   │   ├── nav.tsx                     # Navigation bar
│   │   ├── theme-toggle.tsx            # Dark/light mode toggle
│   │   ├── videoModal.tsx              # Video playback modal
│   │   ├── quiz/
│   │   │   ├── start.tsx               # Quiz landing page
│   │   │   ├── personSelect.tsx        # Activity 1: Face recognition
│   │   │   ├── namePeople.tsx          # Activity 2: Name matching
│   │   │   ├── contextQuestions.tsx    # Activity 3: Context Q&A
│   │   │   ├── datePicker.tsx          # Date selection component
│   │   │   └── bubbles/
│   │   │       └── index.tsx           # Bubble clicking game
│   │   └── ui/
│   │       ├── button.tsx              # shadcn button
│   │       ├── card.tsx                # shadcn card
│   │       ├── dialog.tsx              # shadcn dialog
│   │       ├── input.tsx               # shadcn input
│   │       └── ... (more UI components)
│   ├── lib/
│   │   ├── types.ts                    # TypeScript type definitions
│   │   └── utils.ts                    # Utility functions
│   ├── public/
│   │   ├── known-faces/                # Publicly accessible face images
│   │   └── video_chunks/               # Public video chunks
│   ├── next.config.js                  # Next.js configuration
│   ├── tailwind.config.ts              # Tailwind CSS config
│   ├── tsconfig.json                   # TypeScript config
│   └── package.json                    # Node dependencies
│
├── HOW_TO_RUN.md                       # Setup instructions
└── README.md                           # This file

🎯 Configuration

Backend Configuration (app.py)

UPLOAD_FOLDER = 'content'              # Video uploads directory
KNOWN_FACES_FOLDER = 'known_faces'     # Known faces directory
MAX_CONTENT_LENGTH = 500 * 1024 * 1024 # 500MB max upload size
ALLOWED_EXTENSIONS = {'mp4', 'avi', 'mov', 'mkv', 'webm'}
ALLOWED_IMAGE_EXTENSIONS = {'jpg', 'jpeg', 'png'}

Frontend Configuration (next.config.js)

images: {
  remotePatterns: [
    {
      protocol: 'http',
      hostname: 'localhost',
      port: '5000',
      pathname: '/known-faces/**',
    },
  ],
}

Environment Variables

Create .env in backend/ folder:

OPENAI_API_KEY=your_openai_api_key_here

📡 API Endpoints

Video Management

POST /upload                           # Upload video with metadata
GET  /video_chunks                     # Get all video chunks
GET  /api/videos                       # Get video list (frontend API)

Face Recognition

GET  /known-faces                      # Get all known faces
POST /known-faces                      # Upload new face image
GET  /known-faces/<filename>           # Serve face image

Quiz System

GET  /api/questions                    # Get quiz questions
POST /api/accuracy                     # Verify answer with AI
POST /performance                      # Save quiz performance
GET  /performance                      # Get performance history

Memory Search

POST /api/semSearch                    # Semantic video search

💻 Usage Examples

Upload a Video Memory

Navigate to "Upload Video" page
Fill in video details:
- Title, date, description
- Emotions experienced
- People present
Select video file
Click "Upload"
Video processes automatically in background

Use Memory Explorer

Go to "Memory Explorer" page
Enter search query (e.g., "family dinner", "birthday party")
Click "Query" button
Watch relevant video chunks play sequentially
View synchronized transcripts

Take Cognitive Quiz

Navigate to "Quiz" page
Choose an activity:
- Activity 1: Identify faces from your memories
- Activity 2: Match names to the correct faces
- Activity 3: Answer context questions about videos
Complete all activities for comprehensive score
View results on Performance Dashboard

View Performance Dashboard

Go to "Dashboard" page
View stats cards:
- Average score
- Recent performance
- Activity breakdowns
Analyze Memory Trends graph
- Weekly performance visualization
- Current month focus
Track improvement over time

Manage Known Faces

Navigate to "Faces" page
View all known faces in grid layout
Upload new faces:
- Use "Upload Face" button
- Name format: firstname_lastname.jpg
System automatically normalizes names for display

🏗️ Architecture Highlights

Video Processing Pipeline

Upload: User submits video with metadata
Chunking: Video split into 10-second segments
Transcription: Audio extracted and converted to text
Frame Extraction: ORB keypoint detection identifies unique frames
Face Recognition: Detects and identifies people in frames
AI Analysis: GPT-4 Vision generates contextual Q&A
Storage: Chunks, transcripts, and faces saved to JSON

Face Recognition System

Training: Loads all images from known_faces/ directory
Encoding: Uses dlib's face encoding (128-dimensional vectors)
Detection: Scans video frames for faces
Matching: Compares detected faces to known encodings
Threshold: Configurable tolerance for face matching accuracy

Quiz Scoring Algorithm

Activity 1 (Face Recognition):

score = ((correct_count - incorrect_count) / total_faces) × 100

Activity 2 (Name Matching):

score = 100 if correct else 0

Activity 3 (Context Questions):

question_score = AI_verification_score (0-10)
activity_score = (sum(all_question_scores) / total_questions) × 10

Combined Score:

final_score = (activity1 + activity2 + activity3) / 3

Theme System

CSS Variables: Light/dark color schemes defined in globals.css
next-themes: Manages theme state and persistence
System Detection: Auto-detects OS preference on first visit
LocalStorage: Persists user theme choice
No Flash: Prevents theme flicker on page load

Video Randomization Strategy

Folder-Level: Randomizes which video folder plays first
Chunk-Level: Maintains sequential order within each video
Algorithm: Shuffles folders array before concatenating chunks
Result: Different video order each page load, but coherent playback

🎨 Recent Enhancements

Quiz System Overhaul

✅ Comprehensive scoring combining all 3 activities
✅ Sequential question flow with manual navigation
✅ AI-powered answer verification using GPT-4
✅ Fixed bubble click handler with proper dependencies
✅ Activity score breakdown in performance data
✅ Finish button only appears after all questions answered

Dashboard Improvements

✅ Stats cards repositioned above Memory Trends graph
✅ Weekly calculation based on actual calendar weeks
✅ Filters performance data by current month only
✅ Dynamic week generation (Week 1-5)
✅ Real-time updates after quiz completion

Theme System Implementation

✅ Dark/light mode toggle in navigation
✅ System preference detection
✅ Persistent theme storage
✅ Complete dark mode styling across all pages
✅ Smooth animated transitions
✅ Fixed search inputs, cards, and all UI elements

Memory Explorer Updates

✅ Random video folder selection
✅ Sequential chunk playback within videos
✅ Fixed dark mode styling for search interface
✅ Improved video loading performance

📈 Performance Metrics

Typical performance on modern hardware:

Video Upload: ~2-5 seconds (depends on file size)
Video Processing: ~30-60 seconds per minute of video
Face Recognition: ~100-200ms per frame
Transcript Generation: ~5-10 seconds per chunk
AI Analysis: ~3-5 seconds per Q&A generation
Semantic Search: ~1-2 seconds
Quiz Loading: <500ms
Dashboard Rendering: <300ms

🧪 Testing

Currently testing is done manually. Future enhancements will include:

Jest/React Testing Library for frontend components
pytest for backend API endpoints
Integration tests for video processing pipeline
E2E tests with Playwright/Cypress
Performance benchmarking scripts

🚢 Deployment

Production Build

Frontend:

cd frontend
npm run build
npm start

Backend:

cd backend
# Set production environment variables
# Use production WSGI server (gunicorn, waitress)
pip install gunicorn
gunicorn app:api

Environment Variables (Production)

OPENAI_API_KEY=your_production_api_key
FLASK_ENV=production
PORT=5000
NODE_ENV=production

Deployment Checklist

🔮 Future Enhancements

Phase 1: Core Improvements

Database Integration: PostgreSQL for persistent storage
User Authentication: Multi-user support with login system
Cloud Storage: S3/Azure for video and image storage
WebSocket Support: Real-time updates and notifications
Mobile App: React Native application
Offline Mode: Progressive Web App (PWA) capabilities

Phase 2: Advanced Features

Voice Commands: Voice-activated memory search
Calendar Integration: Link memories to calendar events
Medication Reminders: Smart notification system
Caregiver Dashboard: Separate interface for caregivers
Social Sharing: Share memories with family members
Memory Games: Additional cognitive exercises
AR/VR Support: Immersive memory experiences
Multi-language Support: Internationalization

Phase 3: AI Enhancements

Emotion Detection: Analyze emotions in videos
Object Recognition: Tag objects in video scenes
Scene Classification: Categorize video content
Predictive Analytics: Identify cognitive patterns
Personalized Recommendations: Suggest relevant memories
Natural Conversations: ChatGPT-style memory chat
Auto-captioning: Generate video captions
Smart Highlights: Auto-create memory compilations

Phase 4: Clinical Features

Progress Tracking: Long-term cognitive assessment
Medical Integration: Connect with healthcare systems
Clinical Reports: Generate reports for doctors
Medication Tracking: Monitor medication adherence
Appointment Reminders: Healthcare appointment system
Emergency Alerts: Automatic caregiver notifications
Research Tools: Anonymized data for dementia research

🤝 Contributing

Contributions are welcome! This project is designed to help individuals with cognitive challenges and their families.

How to Contribute

Fork the repository
Create a feature branch
```
git checkout -b feature/amazing-feature
```
Make your changes
Test thoroughly
Commit your changes
```
git commit -m 'Add amazing feature'
```
Push to the branch
```
git push origin feature/amazing-feature
```
Open a Pull Request

Contribution Guidelines

Follow existing code style and conventions
Add comments for complex logic
Update documentation for new features
Test all changes before submitting
Be respectful and constructive in discussions

📖 Learning Resources

🐛 Known Issues

Video processing can be slow for long videos (>5 minutes)
Face recognition accuracy depends on image quality
Large video files may cause memory issues
AI-generated Q&A quality varies with video content
Theme flash may occur on slow connections

🔒 Privacy & Security

All data stored locally (no cloud sync by default)
OpenAI API calls are encrypted via HTTPS
No user data collected or shared
Videos and faces remain on local server
API key stored in environment variables (never committed)

📜 License

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

🙏 Acknowledgments

OpenAI - GPT-4 Vision API for intelligent analysis
dlib Team - Face recognition algorithms
Next.js Team - Modern React framework
Tailwind CSS - Utility-first styling
shadcn/ui - Beautiful UI components
Radix UI - Accessible component primitives
Face Recognition Library - Simple face recognition for Python

📞 Support

For issues, questions, or feature requests:

GitHub Issues: Open an issue
Email: Contact the maintainers for urgent issues

Built with ❤️ to help individuals with dementia and Alzheimer's stay connected to their memories

CogniConnect - Connecting Minds, Preserving Memories

Name		Name	Last commit message	Last commit date
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frontend		frontend
README.md		README.md

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