Code Efficiency Studio

Full-stack application that analyzes code efficiency, generates optimization suggestions, and tracks CO₂ emissions impact. Supports single file analysis (paste or upload) and full project analysis via ZIP upload.

Features

Single File Analysis

• Paste or Upload Code: Paste code directly or upload individual code files (.py, .java, .js, .jsx, .html, .css)
• Multi-Language Support: Python, Java, JavaScript, HTML, and CSS
• Complexity Analysis: Heuristic analysis of loops, conditionals, functions, duplicates, and code complexity
• AI-Powered Suggestions: Optional Ollama-powered DeepSeek suggestions (local deepseek-coder:1.3b) with deterministic fallback
• CO₂ Impact Tracking: Estimates energy consumption and CO₂ emissions before and after optimization
• CodeCarbon Integration: Real-time measurements of actual backend energy/CO₂ usage per analysis

Project Analysis

• ZIP Upload: Upload entire project folders (up to 250 MB) as ZIP files
• Multi-File Analysis: Analyzes all supported files in the project
• Interconnection Detection: Automatically detects file dependencies and interconnections
  • Python: import and from ... import statements
  • Java: import statements
  • JavaScript: import, require(), dynamic imports
  • HTML: <script src> and <link href> tags
  • CSS: @import statements
• Aggregate Metrics: Project-wide statistics including total LOC, complexity, and languages used
• Optimization Suggestions: AI suggestions for top complexity files

Dashboard & History

• Impact Dashboard: Visual charts showing CO₂ saved and compile time improvements over time
• History Tracking: SQLite-based history log with recent analyses panel
• Real-time Updates: Dashboard refreshes automatically after each analysis

Project Layout

Code-Efficiency-Analyser/
├── backend/                    # Flask API + analysis modules
│   ├── analysis/              # Code analysis modules
│   │   ├── complexity.py      # Complexity heuristics
│   │   ├── co2.py             # CO₂ estimation
│   │   ├── suggestions.py     # AI suggestion engine
│   │   └── project_analyzer.py # Multi-file project analysis
│   ├── services/              # Backend services
│   │   ├── history_store.py   # SQLite history storage
│   │   ├── tracking.py        # CodeCarbon integration
│   │   └── ollama_client.py   # Ollama API client
│   ├── app.py                 # Flask application
│   └── requirements.txt       # Python dependencies
└── frontend/                   # Static UI
    ├── index.html             # Main analysis page
    ├── dashboard.html         # Dashboard page
    ├── main.js                # Analysis page logic
    ├── dashboard.js           # Dashboard logic
    └── styles.css             # Styling (white/blue theme)

Backend Setup

1. Create a virtual environment and install dependencies:

   cd Code-Efficiency-Analyser/backend
   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -r requirements.txt

2. Optional: Setup Ollama for AI suggestions

   • Install Ollama
   • Pull the DeepSeek model:

     ollama pull deepseek-coder:1.3b

   • The backend points to http://127.0.0.1:11434 by default
   • Override with export OLLAMA_BASE_URL=http://host:port if needed
   • Note: The app works without Ollama using heuristic fallbacks

3. CodeCarbon Configuration

   • CodeCarbon runs automatically in process
   • Set COUNTRY_ISO_CODE environment variable for region-specific factors
   • Otherwise, global defaults are used

4. Run the Flask API:

   cd backend
   flask --app app run --port 5000 --debug

   Or directly:

   python app.py

Frontend Usage

1. Open the application:

   • Simply open frontend/index.html in a web browser
   • Or serve via HTTP server:

     cd frontend
     python3 -m http.server 8000
     # Then open http://localhost:8000 in browser

2. Update backend URL (if needed):

   • Edit BACKEND_URL in frontend/main.js if API runs on different host/port
   • Default: http://localhost:5000

API Endpoints

POST /api/analyze

Analyze a single code snippet.

Request:

{
  "code": "string",
  "language": "python" | "java" | "javascript" | "html" | "css"
}

Response:

{
  "analysis": {
    "before": { /* complexity metrics */ },
    "after": { /* optimized metrics */ },
    "delta": { /* differences */ }
  },
  "co2": {
    "before": {"energy_kwh": 0.0, "co2_kg": 0.0},
    "after": {"energy_kwh": 0.0, "co2_kg": 0.0},
    "energy_saved_kwh": 0.0
  },
  "session_emissions": {"energy_kwh": 0.0, "co2_kg": 0.0, "duration_s": 0.0},
  "suggestion": {
    "summary": "Optimization summary",
    "confidence": "high/medium/low",
    "analysis_insights": [ /* array of insights */ ],
    "ai_model_used": "deepseek-coder:1.3b" | null,
    "used_fallback": false,
    "alternative_code": "optimized code"
  },
  "alternative_code": "string",
  "history": [ /* recent analyses */ ]
}

POST /api/analyze-project

Analyze an entire project from a ZIP file.

Request: multipart/form-data with file field (ZIP archive, max 250 MB)

Response:

{
  "project_analysis": {
    "files": { /* file path -> analysis data */ },
    "interconnections": [ /* dependency graph */ ],
    "summary": {
      "total_files": 10,
      "total_lines_of_code": 1500,
      "total_complexity": 45.2,
      "languages": ["python", "javascript"],
      "interconnection_count": 8
    }
  },
  "co2": { /* aggregate CO₂ impact */ },
  "session_emissions": { /* measured emissions */ },
  "suggestions": [ /* top file suggestions */ ]
}

GET /api/history

Returns the 25 most recent analyses from SQLite log.

GET /api/dashboard

Returns aggregated dashboard statistics for visualization.

GET /api/health

Health check endpoint.

Data Storage

• SQLite Database: backend/data/history.db
• Automatically created on first run
• Stores all analysis results, metrics, and emissions data

Technology Stack

• Backend: Flask (Python)
• Frontend: Vanilla HTML/CSS/JavaScript (no build step)
• Database: SQLite
• Energy Tracking: CodeCarbon
• AI Suggestions: Ollama (optional, with heuristic fallback)
• Visualization: Chart.js (dashboard only)

Color Scheme

The application uses a clean white background with blue accents:

• Background: Pure white (#ffffff)
• Primary Text: Dark gray (#1e2937)
• Accents: Blue shades (#2563eb, #3b82f6)
• Borders: Light gray (#cbd5e1, #e2e8f0)
• Cards: Light gray background (#f8fafc)

Future Enhancements

• Support for more languages (TypeScript, C++, Go, etc.)
• AST-based code optimization
• Real-time collaboration features
• Export analysis reports (PDF/JSON)
• Integration with CI/CD pipelines
• Advanced dependency visualization