🚀 DecenTrack Python Simulator + ML Integration

This is the Python-based backend for DecenTrack, migrated from the original Solidity + Sepolia setup. It replaces Ethereum smart contracts with a FastAPI server + BlockSim-style simulator, and integrates a custom ML model to validate and weight validator uptime reports.

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🧠 Features

🌐 Python Blockchain Simulator

- In-memory chain state (websites, validators, ticks, balances).
- Block production loop with reward distribution.

🤖 ML-Driven Validation

- A trained ML model (ml_engine/model.joblib) is used to score validator ticks.
- Low-quality ticks are rejected automatically.
- Rewards are weighted by ML score.

🔌 FastAPI Endpoints (Ethereum-like)

- /website/create → create a website
- /tx/addTick → submit uptime tick
- /tx/addMultipleTicks → batch tick submission
- /websites, /website/{id} → query websites
- /ticks/{id} → query ticks (with ML weights)
- /validator/register → register validator
- /me/pendingPayout, /me/payouts → validator rewards

📊 Frontend Compatibility

- API mirrors the original Solidity contract functions.
- Works with the existing Next.js + Tailwind UI.

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📂 Project Structure

.
├── ml_engine/
│   ├── __init__.py
│   ├── features.py        # Feature engineering for ML
│   ├── model.py           # MLEngine wrapper
│   └── model.joblib       # Trained ML model (generated)
├── sim/
│   ├── __init__.py
│   ├── api.py             # FastAPI server
│   ├── node.py            # Blockchain node logic
│   ├── state.py           # Chain state (websites, validators, reports)
│   └── models.py          # Pydantic request/response models
├── blocksim/              # Optional BlockSim experiment code
├── train_model.py         # Script to train and save ML model
├── requirements.txt       # Python dependencies
└── README.md

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⚙️ Setup & Installation

1. Clone the repo

   git clone https://github.com//.git cd

2. Create virtual environment

   python -m venv .venv source .venv/bin/activate # Linux/Mac .venv\Scripts\activate # Windows

3. Install dependencies

   pip install -r requirements.txt

4. Train ML model

   python train_model.py

This generates ml_engine/model.joblib.

5. Run FastAPI server

   uvicorn sim.api:app --host 0.0.0.0 --port 8000 --reload

Server will be available at: 👉 http://localhost:8000 👉 API docs: http://localhost:8000/docs

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🔗 Example API Usage

Create Website

curl -X POST "http://localhost:8000/website/create?owner=0xabc" \
  -H "Content-Type: application/json" \
  -d '{"url":"https://github.com","contactInfo":"me@example.com"}'

Submit Tick

curl -X POST "http://localhost:8000/tx/addTick?validator=0xval1" \
  -H "Content-Type: application/json" \
  -d '{"websiteId":"1","status":0,"latency":250}'

Get Ticks

curl "http://localhost:8000/ticks/1?n=5"

Response includes ML weight:

{
  "status": "Success",
  "data": [
    {
      "validator": "0xval1",
      "createdAt": 1735710732,
      "status": 0,
      "latency": 250,
      "location": "sim-location",
      "ml_weight": 0.87
    }
  ]
}

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🧩 Integration with Frontend

• Replace ethers.js calls in your Next.js frontend with fetch calls to this FastAPI server.
• Example:
  • contract.addTick(...) → POST /tx/addTick
  • contract.getAllWebsites() → GET /websites
• The API responses are shaped to match the Solidity contract outputs, so minimal frontend changes are needed.

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📊 BlockSim Experiments (Optional)

You can run the BlockSim simulation to test consensus + ML integration:

python -m blocksim.run_sim

This prints block counts, reports, and validator balances.

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📝 Notes

• ML model is pluggable: retrain with new dataset → replace model.joblib.
• Default ML threshold = 0.3 (ticks below this score are rejected).
• Rewards are distributed proportionally to ML weights.

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✨ Author

Developed with ❤️ by Abhishek B R CSE Student | Full-stack & Blockchain Developer | Learning AI, Golang, Rust