Architecture Overview

Architecture Overview

System Architecture

The AI Analysis for LeetCode project follows a modular architecture designed for scalability and maintainability.

graph TD
    A[Client Browser] --> B[Flask Web Server]
    B --> C[Analytics Core]
    C --> D[Pattern Analyzer]
    C --> E[Skill Analyzer]
    C --> F[Learning Path Analyzer]
    B --> G[GraphQL Client]
    G --> H[LeetCode API]
    C --> I[Cache Layer]
    I --> J[(In-Memory Cache)]

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Core Components

1. Web Server (api/app.py)

• Flask-based web application
• Handles HTTP requests and responses
• Serves the web interface
• Routes requests to appropriate analytics modules

2. Analytics Core (api/core/analytics/)

The analytics core consists of several specialized modules:

Pattern Analyzer (pattern_analyzer.py)

• Analyzes problem-solving patterns
• Identifies strengths and weaknesses
• Tracks progress over time
• Generates pattern-based insights

Skill Analyzer (skill_analyzer.py)

• Evaluates technical skills
• Maps problems to skill categories
• Measures proficiency levels
• Suggests skill improvement areas

Learning Path Analyzer (learning_path_analyzer.py)

• Creates personalized learning paths
• Recommends problems based on skill level
• Adapts recommendations based on progress
• Optimizes learning trajectory

3. Data Management

GraphQL Client (api/GQLQuery.py)

• Manages LeetCode API interactions
• Handles authentication
• Implements rate limiting
• Formats API responses

Data Formatter (api/data_formatter.py)

• Transforms raw API data
• Standardizes data structures
• Prepares data for analysis
• Handles data validation

Cache Layer (api/core/utils/cache.py)

• Implements in-memory caching
• Reduces API calls
• Improves response times
• Manages cache invalidation

Data Flow

1. Request Flow

sequenceDiagram
    participant User
    participant Web Server
    participant Analytics Core
    participant LeetCode API
    
    User->>Web Server: Request Analysis
    Web Server->>Analytics Core: Process Request
    Analytics Core->>LeetCode API: Fetch Data
    LeetCode API-->>Analytics Core: Return Data
    Analytics Core->>Analytics Core: Analyze Data
    Analytics Core-->>Web Server: Return Results
    Web Server-->>User: Display Results

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2. Analysis Pipeline

graph LR
    A[Raw Data] --> B[Data Formatter]
    B --> C[Cache Layer]
    C --> D[Analytics Modules]
    D --> E[Results Generation]
    E --> F[Response Formatting]

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Technical Stack

Backend

• Python 3.8+
• Flask web framework
• GraphQL client
• NumPy/Pandas for data processing
• In-memory caching

Frontend

• HTML/CSS templates
• JavaScript for interactivity
• Bootstrap for responsive design

Security Considerations

1. API Security

   • Rate limiting implementation
   • Request validation
   • Error handling

2. Data Protection

   • Secure data transmission
   • Cache expiration policies
   • No storage of sensitive data

Performance Optimization

1. Caching Strategy

   • In-memory cache for frequent requests
   • Configurable cache duration
   • Smart cache invalidation

2. Request Optimization

   • Batch API requests
   • Parallel processing where applicable
   • Response compression

Future Scalability

1. Horizontal Scaling

   • Stateless application design
   • Containerization support
   • Load balancer ready

2. Modular Design

   • Pluggable analytics modules
   • Extensible architecture
   • Easy integration of new features

Development Workflow

1. Code Organization

ai-analysis-for-leetcode/
├── api/
│   ├── app.py
│   ├── GQLQuery.py
│   ├── data_formatter.py
│   └── core/
│       ├── analytics/
│       │   ├── pattern_analyzer.py
│       │   ├── skill_analyzer.py
│       │   └── learning_path_analyzer.py
│       └── utils/
│           └── cache.py
├── docs/
└── tests/

2. Development Process
   • Feature branching
   • Code review requirements
   • Automated testing
   • Documentation updates