Currency Exchange Prediction Microservice with AI Integration

A microservice that leverages AI to forecast currency exchange rates by analyzing financial news. The system integrates Meta-LLaMA (LLaMA-3.2-1B-Instruct) for AI predictions and NewsAPI for real-time financial news data.

🚀 Features

• Real-time Currency Conversion: Fetch and convert exchange rates between multiple currencies
• AI-Powered Forecasting: Utilize Meta-LLaMA model for exchange rate predictions
• Automated News Analysis: Real-time financial news retrieval and processing with relevancy scoring
• Secure API Management: Protected storage for API credentials
• Ballerina Architecture: Robust microservice structure with error handling and logging
• Historical Data Storage: Systematic storage of AI forecasts and news articles

🏗️ Architecture

.
├── ai_module/
│   ├── hf_token.txt              # Hugging Face API credentials
│   └── llama_api_server.py       # AI prediction service
├── ballerina_code/
│   ├── currency_service.bal      # Main Ballerina service
│   └── collected_ai_forecasts/   # Forecast storage
├── news_api/
│   ├── news_token.txt           # NewsAPI credentials
│   └── news_api_server.py       # News fetching service
└── collected_news/              # News article storage

📋 Prerequisites

Required Accounts

• Hugging Face Account
  • Create account at Hugging Face
  • Request special access for Meta-LLaMA models
  • Generate API token
• NewsAPI Account
  • Register at NewsAPI
  • Obtain API key
• Ballerina Installation
  • Follow official Ballerina installation guide
  • Required version: 2023R1 or higher

🛠️ Installation

1. Clone Repository

   git clone https://github.com/WWI2196/iwb151-fouett-bytes.git
   cd currency-exchange-prediction

2. Install Python Dependencies

   pip install torch transformers flask huggingface-hub newsapi-python python-dateutil requests logging

🚦 Usage

1. Launch AI Service

   python ai_module/llama_api_server.py

   Runs on port 5001

2. Start News Service

   python news_api/news_api_server.py

   Runs on port 5002

3. Run Ballerina Service

   bal run ballerina_code/currency_service.bal

Important: Once both Python and Ballerina services are running, AI predictions will be automatically saved to the collected_ai_forecasts folder in the main directory.

⚙️ Configuration

API Configuration

1. Place your Hugging Face token in ai_module/hf_token.txt
2. Store NewsAPI key in news_api/news_token.txt

Model Customization

• Modify the model ID in llama_api_server.py to use alternative models
• Current model: "meta-llama/Llama-3.2-1B-Instruct"
• Configuration: torch.bfloat16, CPU inference, max_tokens=4000

News API Settings

• Articles fetched from last 7 days
• Relevancy scoring based on categories:
  • Monetary Policy
  • Economic Indicators
  • Currency-Specific News
  • Market Sentiment
  • Geopolitical Events
  • Technical Analysis

📊 Data Flow

1. NewsAPI service fetches latest financial news
2. News data is processed and stored in collected_news
3. AI service analyzes news using Meta-LLaMA
4. Predictions are stored in collected_ai_forecasts
5. Ballerina service orchestrates the entire workflow

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open Pull Request

📝 License

Distributed under the MIT License. See LICENSE for more information.