🤖 Bots

AI-powered application prototypes with model integration, APIs, databases, and deployable interfaces.

A collection of intelligent bots built with state-of-the-art transformer models and real-time data processing pipelines, ready for production deployment.

📋 Table of Contents

Overview
Projects
Tech Stack
Quick Start
Project Structure
Features
Installation
Usage
Deployment
Development
Contributing
License

🎯 Overview

Bots is a comprehensive repository showcasing multiple AI-powered bot implementations:

T5 Bot: Advanced text-to-text generation (summarization, translation) using Google's T5 model
CandleBot: Real-time forex candlestick data collection and persistence with intelligent failover

Each bot demonstrates production-ready patterns including REST APIs, database integration, containerization, and scalable deployment configurations.

🚀 Projects

T5 Bot

A full-stack NLP application that uses Google's T5 (Text-to-Text Transfer Transformer) model to perform advanced text operations.

Capabilities:

📝 Text Summarization
🌍 Multi-language Translation (English, French, German)
🔄 Chained Operations (summarize → translate)

Architecture:

Backend: FastAPI with async support
Frontend: React 18 with Vite
Database: PostgreSQL
ML Framework: PyTorch + Hugging Face Transformers

Key Files:

T5/T5_Bot/app.py - Flask application with T5 endpoints
T5/requirements.txt - Python dependencies
T5/docker-compose.yml - Full stack orchestration

CandleBot

A specialized bot for capturing financial market data in real-time from MetaTrader 5, with built-in resilience for network failures.

Capabilities:

📊 Real-time 1-minute candlestick data collection from EURUSD
💾 Persistent storage to SQL Server
🔄 Automatic offline mode with sync on reconnection
📝 Comprehensive logging and error tracking

Key Files:

CandleBot/save_realtime_candles.py - Main data collection script

🛠️ Tech Stack

Backend

Python 3.10+ - Core language
FastAPI - Modern async web framework
Flask - Lightweight alternative for T5_Bot
PyTorch 2.1+ - Deep learning framework
Transformers 4.30+ - NLP models (Hugging Face)
SQLAlchemy 2.0+ - ORM for database interactions
Uvicorn - ASGI server

Database

PostgreSQL 15 - Primary data store
SQL Server - CandleBot data persistence

Frontend

React 18 - UI library
Vite - Build tool and dev server
Axios - HTTP client
React Router - Client-side routing

DevOps & Tools

Docker - Containerization
Docker Compose - Multi-container orchestration
pytest - Testing framework
Black - Code formatting
Flake8 - Linting
mypy - Type checking

⚡ Quick Start

T5 Bot (Docker Compose - Recommended)

# Clone the repository
git clone https://github.com/Maee127/Bots.git
cd Bots/T5

# Configure environment
cp .env.example .env
# Edit .env with your settings (DB credentials, API host, debug mode, etc.)

# Start all services (Postgres, API, Frontend)
docker-compose up --build

# Services will be available at:
# - API: http://localhost:8000
# - Frontend: http://localhost:5173
# - PostgreSQL: localhost:5432

T5 Bot (Local Development)

cd Bots/T5

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env

# Run FastAPI server
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

# In another terminal, start frontend
cd frontend
npm install
npm run dev

CandleBot

cd Bots/CandleBot

# Install dependencies
pip install MetaTrader5 pyodbc pandas

# Configure SQL Server connection in save_realtime_candles.py
# Update the connection string with your database credentials

# Run the bot
python save_realtime_candles.py

📁 Project Structure

Bots/
├── T5/                              # T5 Text-to-Text Bot
│   ├── T5_Bot/                      # Flask application
│   │   ├── app.py                   # Main Flask app with ML endpoints
│   │   ├── templates/               # HTML templates
│   │   │   └── index.html           # Web interface
│   │   ├── static/                  # Frontend assets
│   │   │   ├── style.css            # Styling
│   │   │   ├── script.js            # Client-side logic
│   │   │   └── assets/              # Images and resources
│   │   └── requirements.txt         # Project dependencies
│   ├── frontend/                    # React application (Vite)
│   │   ├── src/                     # React components
│   │   ├── public/                  # Static assets
│   │   ├── vite.config.js           # Vite configuration
│   │   └── package.json             # NPM dependencies
│   ├── app/                         # FastAPI application (planned)
│   │   └── main.py                  # FastAPI entry point
│   ├── requirements.txt             # Python dependencies
│   ├── pyproject.toml               # Project configuration
│   ├── setup.py                     # Python package setup
│   ├── Dockerfile                   # Multi-stage build
│   ├── docker-compose.yml           # Full stack composition
│   ├── .env.example                 # Environment variables template
│   └── README.md                    # T5 Bot documentation
│
├── CandleBot/                       # Forex Data Collection Bot
│   ├── save_realtime_candles.py     # Main data collection script
│   ├── logs/                        # Log directory
│   └── EURUSD_candle_logger.log     # Log file
│
├── README.md                        # This file
└── .gitignore                       # Git ignore rules

✨ Features

T5 Bot

✅ Text Summarization - Condense long texts while preserving meaning
✅ Multi-language Translation - Translate between English, French, and German
✅ Chained Operations - Summarize then translate in one workflow
✅ REST API - FastAPI endpoints for programmatic access
✅ Web UI - React-based user interface
✅ Database Integration - Store and retrieve operations
✅ Containerized - Docker support for easy deployment
✅ Scalable - Async processing with uvicorn

CandleBot

✅ Real-time Data - 1-minute candlestick data from MetaTrader 5
✅ Persistent Storage - SQL Server integration
✅ Offline Resilience - JSON fallback when database is unavailable
✅ Auto-sync - Automatic data synchronization on reconnection
✅ Comprehensive Logging - Detailed logs for monitoring and debugging
✅ Error Handling - Graceful failure recovery

📦 Installation

Prerequisites

Python 3.10+ - Core runtime
Node.js 16+ - Frontend build tools
Docker & Docker Compose - For containerized deployment
PostgreSQL - For T5 Bot database (or use Docker image)
SQL Server - For CandleBot (optional, uses offline fallback if unavailable)

Step-by-Step Setup

1. Clone the Repository

git clone https://github.com/Maee127/Bots.git
cd Bots

2. T5 Bot Setup

Option A: Docker Compose (Recommended)

cd T5
cp .env.example .env
docker-compose up --build

Option B: Local Development

cd T5

# Python setup
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Frontend setup
cd frontend
npm install

# Back to T5 root
cd ..

3. CandleBot Setup

cd CandleBot
pip install MetaTrader5 pyodbc pandas
# Configure database connection in save_realtime_candles.py

🎮 Usage

T5 Bot - Text Summarization

Via REST API:

curl -X POST http://localhost:8000/api/summarize \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Your long text here that you want to summarize..."
  }'

Via Web UI:

Navigate to http://localhost:5173
Enter text in the input area
Click "Summarize in Original Language"
View results in the output box

T5 Bot - Translation

Via REST API:

curl -X POST http://localhost:8000/api/translate \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Hello, how are you?",
    "source_language": "English",
    "target_language": "French"
  }'

Via Web UI:

Select source and target languages
Enter text
Click "Translate Original Text"
View translated result

T5 Bot - Chained Operations

Summarize text
Translate the summary to another language
Click "Translate Summary"

CandleBot - Data Collection

cd CandleBot
python save_realtime_candles.py

What it does:

Connects to MetaTrader 5
Fetches EURUSD 1-minute candles
Stores in SQL Server
Falls back to JSON file if database unavailable
Syncs offline data when connection restored
Logs all operations to EURUSD_candle_logger.log

🚢 Deployment

Docker Deployment (T5 Bot)

Build Image:

cd T5
docker build -t bots:latest .

Run Container:

docker run -p 8000:8000 \
  -e DATABASE_URL="postgresql://user:pass@postgres:5432/bots" \
  -e DEBUG=false \
  bots:latest

Docker Compose (Full Stack)

cd T5
docker-compose up -d

Environment Configuration (.env):

# Database
DB_USER=bots_user
DB_PASSWORD=secure_password_here
DB_NAME=bots

# API
PYTHON_ENV=production
DEBUG=false
API_HOST=0.0.0.0
API_PORT=8000

# Frontend
VITE_API_URL=https://your-api-domain.com

Cloud Deployment

The repository is structured for easy deployment to:

AWS (ECS, Lambda, RDS)
Google Cloud (Cloud Run, Cloud SQL)
Azure (Container Instances, PostgreSQL)
Heroku (with Procfile)
Kubernetes (with Helm charts)

🛠️ Development

Running Tests

cd T5

# Run all tests
pytest

# Run with coverage
pytest --cov=app

# Run specific test file
pytest tests/test_bot.py

Code Quality

cd T5

# Format code with Black
black .

# Lint with Flake8
flake8 .

# Type check with mypy
mypy .

# Frontend linting
cd frontend
npm run lint
npm run format

Development Server with Hot Reload

cd T5

# Terminal 1: Backend
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

# Terminal 2: Frontend
cd frontend
npm run dev

API Documentation

Once the FastAPI server is running:

Swagger UI: http://localhost:8000/docs
ReDoc: http://localhost:8000/redoc

🤝 Contributing

We welcome contributions! Please follow these steps:

Fork the repository

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

Create a feature branch
```
git checkout -b feature/amazing-feature
```
Make your changes
- Write clean, well-documented code
- Add tests for new functionality
- Follow the existing code style (Black, Flake8)
Commit your changes
```
git commit -m 'Add amazing feature'
```
Push to your fork
```
git push origin feature/amazing-feature
```
Open a Pull Request
- Provide a clear description of changes
- Link any related issues
- Include screenshots for UI changes

Code Style Guidelines

Use Black for Python formatting
Use Prettier for JavaScript formatting
Write type hints for Python functions
Add docstrings to classes and functions
Keep lines under 88 characters (Black default)

📝 License

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

You are free to use, modify, and distribute this software for any purpose, including commercial use.

👤 Author

Maee127 - GitHub Profile

🙏 Acknowledgments

Hugging Face Transformers - NLP model library
FastAPI - Web framework
React - UI library
PyTorch - Deep learning framework
Docker - Containerization platform
PostgreSQL - Database
Vite - Frontend build tool

💬 Support & Issues

Report a Bug: Open an Issue
Request a Feature: Create a Discussion
Ask a Question: GitHub Discussions

📊 Project Stats

Language: Python (58%), CSS (16.1%), HTML (10%), JavaScript (9.1%), Dockerfile (6.8%)
Repository: Maee127/Bots
License: MIT
Status: Active Development

🗺️ Roadmap

Enhanced FastAPI backend with full OpenAPI documentation
Advanced model fine-tuning and custom training pipelines
Real-time WebSocket support for live updates
Multi-model orchestration (BERT, GPT, T5 variants)
Production-grade monitoring and observability
Kubernetes deployment manifests
CI/CD pipeline with GitHub Actions
Performance optimization and caching layer

Made by MAedeh Torkian: Maee127


This README includes:
- ✅ Clear project overview
- ✅ Detailed quick start guides for both projects
- ✅ Complete tech stack documentation
- ✅ Installation and setup instructions
- ✅ Usage examples with code snippets
- ✅ Deployment guidelines
- ✅ Development workflow
- ✅ Contributing guidelines
- ✅ Professional structure and formatting
- ✅ Links to resources and support

You can now copy this and create your `README.md` file in the root of your repository!

Name		Name	Last commit message	Last commit date
CandleBot		CandleBot
T5		T5
.gitignore		.gitignore
README.md		README.md

Latest commit

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Repository files navigation

🤖 Bots

📋 Table of Contents

🎯 Overview

🚀 Projects

T5 Bot

CandleBot

🛠️ Tech Stack

Backend

Database

Frontend

DevOps & Tools

⚡ Quick Start

T5 Bot (Docker Compose - Recommended)

T5 Bot (Local Development)

CandleBot

📁 Project Structure

✨ Features

T5 Bot

CandleBot

📦 Installation

Prerequisites

Step-by-Step Setup

1. Clone the Repository

2. T5 Bot Setup

3. CandleBot Setup

🎮 Usage

T5 Bot - Text Summarization

T5 Bot - Translation

T5 Bot - Chained Operations

CandleBot - Data Collection

🚢 Deployment

Docker Deployment (T5 Bot)

Docker Compose (Full Stack)

Cloud Deployment

🛠️ Development

Running Tests

Code Quality

Development Server with Hot Reload

API Documentation

🤝 Contributing

Code Style Guidelines

📝 License

👤 Author

🙏 Acknowledgments

💬 Support & Issues

📊 Project Stats

🗺️ Roadmap

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