project-details.md

Medical Interpreter Bot

The Medical Interpreter Bot is a tool designed to enhance the efficiency and accuracy of medical interpreters during consultations. This smart Discord bot provides instant access to a comprehensive database of medical terms across various specialties, ensuring that interpreters can quickly find the information they need without the hassle of manual searches.

Scope and Objectives

Scope Develop a smart Discord bot designed to assist medical interpreters by providing and organizing commonly used medical terms in specific specialties. The bot will support real-time queries, allow for the easy addition of new terms, and provide relevant terms and definitions based on user input without requiring explicit commands.

Specific Objectives

• Enable quick and intuitive access to medical terms for interpreters during consultations.
• Automatically analyze and organize new information provided by users.
• Provide specialty-specific term lists.
• Allow for easy expansion and updates to the database.
• Ensure the Discord bot is user-friendly and accessible.
• Incorporate best programming practices for maintainability and scalability.

Requirements

Technical Requirements

• Python 3.6+

Libraries

• Discord Bot Framework: discord.py
• Database Management: SQLite, SQLAlchemy for ORM (Optional for enhanced functionality)
• Data Manipulation: pandas
• Machine Learning: scikit-learn
• Natural Language Processing: spaCy or NLTK, transformers
• Generative AI: transformers (Hugging Face)
• Scheduling: schedule
• Environment Variables: dotenv
• Version Control: Git
• HTTP Requests: requests for API calls (Optional)
• Logging: logging for detailed log management
• Testing: pytest for unit and integration tests

Functional Requirements

• Natural Language Understanding (NLU): To provide seamless interaction, the bot will incorporate NLU capabilities to recognize medical terms and context without the need for explicit commands, using advanced NLP frameworks such as spaCy or transformers. This will enable the bot to understand and respond appropriately to user inputs in real-time.

• User Input Analysis: The bot will analyze user inputs to identify new medical terms and their definitions. This functionality will be enhanced over time by implementing machine learning models using scikit-learn, ensuring continuous improvement in the accuracy and comprehensiveness of term recognition.

• Generative AI Capabilities: Leveraging the power of transformers, the bot will implement generative AI capabilities to create definitions and explanations for medical terms. Additionally, models will be developed to generate content specific to medical specialties based on user queries and context, providing tailored and relevant information.

• Automated Database Updates: To maintain an up-to-date repository of medical terms, the bot will automatically update the database with new terms and definitions identified through user interactions. Efficient database management will be achieved using SQLAlchemy, ensuring smooth and reliable data operations.

• Specialty Prediction: The bot will include a prediction model to suggest relevant medical specialties based on the definitions of terms provided by users. This model, developed and trained using scikit-learn, will enhance the bot’s ability to provide contextually appropriate information and support specialized medical interpretations.

Non-Functional Requirements

• User-Friendly Interface: The design of the bot’s interface will focus on being intuitive and user-friendly, facilitating seamless interaction for users. Special attention will be given to ensuring that the interface is accessible and easy to navigate, accommodating users with varying levels of technical expertise.

• Secure Data Handling and Storage: To protect user information, the bot will implement secure data handling practices, including the encryption of sensitive data stored in the database. This will ensure compliance with privacy regulations and safeguard user data from unauthorized access.

• Scalable Architecture: The bot will be designed with a scalable architecture to accommodate future enhancements and increased user demand. Consideration will be given to using cloud services for scalable hosting solutions, ensuring that the bot can grow and adapt as needed.

• Coding Standards and Best Practices: Adhering to coding standards and best practices, the bot’s development will focus on maintainable and scalable code. Version control will be implemented using Git to manage code changes effectively, facilitating collaboration and ensuring the reliability of the bot’s functionality.

Software Requirements

• Operating System: Compatible with Windows.
• Database: SQLite for local development, with the option to migrate to a more scalable solution like PostgreSQL or MySQL for production.
• Cloud Services: Optional integration with cloud platforms (e.g., AWS, Google Cloud) for hosting and scaling.
• Integrated Development Environment (IDE): VSCode
• API Integration: Optional integration with external medical databases for real-time updates.

Libraries and Frameworks

• API Documentation: sphinx for generating project documentation.
• CI/CD Tools: Optional integration with CI/CD tools like Jenkins or GitHub Actions for continuous integration and deployment.
• Web Framework: Flask or FastAPI for developing a web interface (if required).

Testing and Quality Assurance

• Unit Testing: Implement unit tests using pytest to ensure individual components function as expected.
• Integration Testing: Develop integration tests to validate the interaction between different components of the bot.
• Performance Testing: Conduct performance tests to ensure the bot can handle multiple queries simultaneously without degradation in performance.
• Security Testing: Perform security testing to identify and mitigate vulnerabilities in the bot and its data handling processes.
• User Acceptance Testing (UAT): Engage with end-users (medical interpreters) for UAT to gather feedback and ensure the bot meets user requirements.

Best Practices

Project Organization:

• Maintain a clear and consistent directory structure.
• Use version control (Git) and maintain a clear commit history.
• Keep project documentation up-to-date, including setup guides and developer notes.

Coding Standards:

• Follow PEP 8 style guide.
• Use linters like flake8 or pylint to enforce coding standards.
• Document modules, classes, and functions with clear docstrings.
• Write modular code with single responsibility principles.
• Employ type hints to improve code readability and maintainability.

Error Handling and Logging:

• Implement robust error handling with try-except blocks.
• Use the logging module for logging events and errors.
• Configure logging levels (DEBUG, INFO, WARNING, ERROR, CRITICAL).
• Ensure that log files are rotated and archived properly to avoid disk space issues.

Database Management:

• Use ORM (Object-Relational Mapping) like SQLAlchemy for database interactions.
• Ensure data integrity with clear schemas and constraints.
• Validate inputs to prevent inconsistent data.
• Implement database migrations to handle schema changes safely.

NLU Model:

• Choose appropriate NLP tools (spaCy, NLTK, transformers) for natural language understanding.
• Train the model with a diverse and representative dataset.
• Evaluate model performance with cross-validation and other metrics.
• Regularly retrain the model with new data.
• Use data augmentation techniques to enhance the training dataset.

Generative AI Capabilities:

• Implement transformers for generating definitions and explanations.
• Fine-tune pre-trained models to ensure they are relevant to the medical domain.
• Regularly update and improve generative models based on user feedback and new data.

Automation and Scheduling:

• Use the schedule library for automated updates and maintenance.
• Handle network issues and partial failures gracefully in automation scripts.
• Implement retry mechanisms for failed tasks to ensure reliability.

Security Best Practices:

• Store sensitive information in environment variables using a .env file.
• Sanitize user inputs to prevent injection attacks.
• Ensure secure communication channels (e.g., HTTPS) for data transmission.
• Regularly update dependencies to patch security vulnerabilities.

Testing and Deployment:

• Write unit tests using unittest or pytest.
• Aim for high test coverage.
• Set up CI/CD pipelines (e.g., GitHub Actions, Travis CI) for automated testing and deployment.
• Perform integration and end-to-end testing to ensure system reliability.
• Use mock data to test different scenarios without compromising user data.

User Experience:

• Design intuitive and consistent interactions.
• Provide clear and concise responses.
• Collect user feedback and incorporate it into improvements.
• Conduct usability testing to identify and address user experience issues.

Architecture

The Medical Interpreter Bot architecture will follow a modular design, integrating various components to handle different functionalities efficiently. The architecture will be scalable, secure, and maintainable, supporting both current needs and future enhancements.

Client Layer (Discord Bot Interface) This layer interacts with users through the Discord platform. It handles user inputs and displays bot responses.

Application Layer This layer contains the core logic of the bot, including Natural Language Understanding (NLU), generative AI capabilities, user input analysis, and specialty prediction.

Subcomponents include

• NLU Component: Handles the understanding of user queries using spaCy or transformers.
• Generative AI Component: Generates definitions and explanations using transformers.
• Prediction Component: Suggests medical specialties based on user queries using scikit-learn.
• Database Management: Manages interaction with the database using SQLAlchemy.

Data Layer

• This layer manages data storage and retrieval. It uses SQLite for local development and can be upgraded to PostgreSQL or MySQL for production environments.
• Contains schemas, constraints, and migration scripts to ensure data integrity and consistency.

Integration Layer

• This layer handles integration with external services, such as API calls to external medical databases if required.
• Manages environment variables and secure data handling.

Components

Discord Bot Interface (discord.py)

• Manages interactions between the bot and Discord users.
• Sends and receives messages from users.

NLU Component (spaCy, transformers)

• Parses and understands user inputs.
• Identifies medical terms and contextual information from queries.

Generative AI Component (transformers)

• Generates definitions and explanations for medical terms.
• Fine-tunes models to ensure relevance to the medical domain.

User Input Analysis (scikit-learn)

• Analyzes new terms and their definitions from user inputs.
• Continuously improves the accuracy of term recognition through machine learning models.

Prediction Component (scikit-learn)

• Predicts relevant medical specialties based on the context and definitions provided by users.

Database Management (SQLAlchemy, SQLite)

• Manages database operations, including CRUD operations.
• Ensures data integrity and consistency.

Automation and Scheduling (schedule)

• Automates updates and maintenance tasks.
• Manages scheduled tasks and handles failures gracefully.

Logging and Error Handling (logging)

• Logs events and errors for monitoring and debugging.
• Implements robust error handling mechanisms.

Flow

User Interaction:

• The user sends a query to the bot through the Discord interface.
• The bot receives the query and forwards it to the application layer.

Natural Language Understanding:

• The NLU component processes the query to extract medical terms and contextual information.
• Relevant entities and context are identified using spaCy or transformers.

Set Current Interaction

• User informs the bot about the current medical interaction (e.g., "Cardiology appointment").
• Bot prepares and displays a list of common terms related to the specified specialty.

User Input Analysis:

• The query is analyzed to identify any new medical terms and their definitions.
• The analysis component uses machine learning models to enhance term recognition.

Generative AI Response:

• If the query requires a generated definition or explanation, the generative AI component creates a response.
• The response is based on models fine-tuned with relevant medical data.

Specialty Prediction:

• The prediction component suggests relevant medical specialties based on the context of the query.
• The prediction model is trained using scikit-learn.

Database Operations:

• The database management component updates the database with new terms and definitions.
• Ensures that all data operations are secure and consistent.

Response to User:

• The bot formulates a response using the processed information.
• Sends the response back to the user through the Discord interface.

Automation and Logging:

• Scheduled tasks and maintenance activities are handled by the automation component.
• All activities and errors are logged for monitoring and debugging purposes.

Algorithms and Math

NLU Model Training - Text Preprocessing

import spacy
from transformers import AutoTokenizer

nlp = spacy.load("en_core_web_sm")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

def preprocess_text(text):
    doc = nlp(text)
    tokens = [token.text for token in doc]
    token_ids = tokenizer.convert_tokens_to_ids(tokens)
    return token_ids

Training Model (scikit-learn example)

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression

vectorizer = TfidfVectorizer()
classifier = LogisticRegression()

# Assuming X_train and y_train are the training data and labels
X_train_tfidf = vectorizer.fit_transform(X_train)
classifier.fit(X_train_tfidf, y_train)

# Prediction
def predict_specialty(query):
    query_tfidf = vectorizer.transform([query])
    return classifier.predict(query_tfidf)

Generative AI Model - Fine-Tuning Transformers

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, Trainer, TrainingArguments

model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
tokenizer = AutoTokenizer.from_pretrained("t5-small")

# Assuming datasets are prepared
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()

Specialty Prediction Model - Training and Evaluation

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2)
classifier.fit(X_train, y_train)
predictions = classifier.predict(X_test)
accuracy = accuracy_score(y_test, predictions)

Database Operations - CRUD Operations (SQLAlchemy)

from sqlalchemy import create_engine, Column, String, Integer, Text
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

engine = create_engine('sqlite:///medical_terms.db')
Base = declarative_base()

class MedicalTerm(Base):
    __tablename__ = 'terms'
    id = Column(Integer, primary_key=True)
    term = Column(String, unique=True, nullable=False)
    definition = Column(Text, nullable=False)

Base.metadata.create_all(engine)

Session = sessionmaker(bind=engine)
session = Session()

def add_term(term, definition):
    new_term = MedicalTerm(term=term, definition=definition)
    session.add(new_term)
    session.commit()

def get_term(term):
    return session.query(MedicalTerm).filter_by(term=term).first()

Wireframes

-Discord Chat Interface:

• Welcome screen

---------------------------------------
|                                     |
| Welcome to the Medical Interpreter  |
| Bot! How can I assist you today?    |
|                                     |
| [Query Medical Term]  [Add New Term]|
| [Specialty Information]             |
| [Set Current Interaction]  [Help]   |
|                                     |
---------------------------------------

• Set Current Interaction Screen

--------------------------------------
|                                    |
| Enter the specialty of the current |
| medical interaction (e.g.,         |
| Cardiology):                       |
|                                    |
| [User Input Field]                 |
|                                    |
| [Submit]                           |
|                                    |
--------------------------------------

• Probable Terms List Screen

--------------------------------------
|                                    |
| Common Terms for Cardiology:       |
| - Heart Murmur (Soplo Cardíaco):   |
|   An unusual sound heard during    |
|   a heartbeat.                     |
| - Angioplasty (Angioplastia):      |
|   A procedure to restore blood     |
|   flow through the artery.         |
| - Myocardial Infarction (Infarto   |
|   de Miocardio): A heart attack.   |
|                                    |
| [Main Menu]                        |
|                                    |
--------------------------------------

• Query Medical Term Screen

--------------------------------------
|                                    |
| Enter your medical term or question|
| here:                              |
|                                    |
| [User Input Field]                 |
|                                    |
| [Submit]                           |
|                                    |
--------------------------------------

• Add New Term Screen

--------------------------------------
|                                    |
| Term: [User Input]                 |
|                                    |
| Definition: [User Input]           |
|                                    |
| [Submit]                           |
|                                    |
| [Add Another Term]  [Main Menu]    |
|                                    |
--------------------------------------

• Specialty Information Screen

--------------------------------------
|                                    |
| Enter the term for which you need  |
| specialty information:             |
|                                    |
| [User Input Field]                 |
|                                    |
| [Submit]                           |
|                                    |
| [More Information]  [Main Menu]    |
|                                    |
--------------------------------------

• Help Screen

---------------------------------------
|                                     |
| Commands:                           |
| - Query Medical Term: Ask for       |
|   definitions of medical terms.     |
| - Add New Term: Add new medical     |
|   terms to the database.            |
| - Specialty Information: Get info   |
|   about medical specialties.        |
| - Set Current Interaction: Specify  |
|   the current medical specialty.    |
| - Help: Display this help message.  |
|                                     |
| [Query Medical Term]  [Add New Term]|
| [Specialty Information]             |
| [Set Current Interaction]           |
| [Main Menu]                         |
|                                     |
---------------------------------------

Plan for development

We are going to use a transformative approach inspired by the Yin-Yang concept, structuring the development in cycles of pre-co-requisite programming blocks, moving from the simplest to the most complex. For doing this we will be structuring the development in cycles and using Git branches, we can ensure that each phase is developed and tested independently before being merged into the main branch. This approach minimizes redundancy, ensures continuous integration, and facilitates incremental improvements. Each cycle builds upon the previous one, resulting in a robust, scalable, and user-friendly Medical Interpreter Bot.

Why Use Branches?

• Isolation: Branches allow you to work on features or fixes in isolation from the main codebase.
• Collaboration: Multiple developers can work on different features simultaneously without interfering with each other.
• Experimentation: You can create experimental branches to try out new ideas without affecting the stable code.

Here’s a structured development plan divided into Alpha and Beta prototypes, each building upon the previous cycle.

Cycle 1 : Foundation and Basic Features

Alpha Prototype 1.1: Initial Setup and Basic Input Tasks

• Set up the development environment.
• Create a basic Python project structure.
• Implement user input for medical specialty.
• Develop basic functions for querying medical terms and definitions.
• Basic user interface within Discord for input and displaying results.

Project Setup

• Set up the development environment.
• Initialize a Git repository and create a virtual environment.
• Install necessary dependencies and create requirements.txt.

Basic Bot Functionality

• Create bot.py to handle basic Discord interactions.
• Implement command handling and basic responses.

Database Setup

• Create database.py to manage SQLite database interactions.
• Set up schemas and basic CRUD operations.

Final Product

• Basic Discord Bot: A functional Discord bot that can handle basic commands and interactions.
• Database Setup: An SQLite database initialized with a basic schema for storing medical terms and definitions.
• Basic Command Handling: The bot can respond to simple user queries with predefined responses.

Beta Prototype 1.2: Basic Term and Definition Management

Tasks

• Extend querying functionality to include English-Spanish translations.
• Implement functions for adding new terms and definitions.
• Enhance the Discord interface to display translations alongside definitions.
• Conduct initial user testing for feedback on input and display functionalities.

Natural Language Understanding

• Implement nlu_model.py using spaCy or transformers.
• Develop basic NLU functions to recognize medical terms.

Basic User Input Analysis

• Implement functions to identify new terms and their definitions.
• Update the database with new terms.

Testing and Feedback

• Write unit tests for bot.py, database.py, and nlu_model.py.
• Conduct initial user testing for feedback on input and basic functionalities.

Final Product

• NLU Integration: The bot can recognize basic medical terms using spaCy or transformers.
• User Input Analysis: The bot can analyze user inputs to identify new terms and update the database.
• Basic Testing and Feedback: Initial unit tests for core functionalities and user feedback gathered to improve input handling.

Core Functionality and Setup Branch for the cycle 1 Branch: feature/cycle-1-setup Sub-branches:

• feature/bot-initialization
• feature/database-setup
• feature/basic-term-lookup
• feature/env-configuration

Cycle 2: Advanced Features and NLU Integration

Alpha Prototype 2.1: Advanced NLU and User Input Analysis Tasks

• Implement spaCy for advanced natural language understanding (NLU).
• Develop functions for recognizing terms and context without explicit commands.
• Enhance the Discord interface to provide context-aware suggestions.
• Add export functionality to save term queries and definitions to a file.

Advanced NLU

• Improve the NLU model to handle more complex queries.
• Train the model with a dataset of medical terms.

User Interface Enhancements

• Enhance the Discord interface to provide context-aware suggestions.
• Implement user-friendly features for better interaction.

Final Product

• Advanced NLU Capabilities: Improved NLU model that can handle more complex queries and provide context-aware suggestions.
• Enhanced User Interface: Better user interaction within Discord, making it easier to query and receive definitions.
• Automated Updates: Basic scripts for automated updates to the database with new terms and definitions.

Beta Prototype 2.2: Generative AI Capabilities Tasks

• Implement transformers for generating definitions and explanations.
• Fine-tune models to ensure relevance to the medical domain.
• Enhance the Discord interface to display generated definitions with context.
• Conduct user testing to gather feedback on NLU and generative AI capabilities.

Generative AI Integration

• Implement transformers for generating definitions and explanations.
• Fine-tune models to ensure relevance to the medical domain.

Automated Database Updates

• Create a script for automated updates and maintenance.
• Ensure efficient and reliable database operations.

Testing and User Feedback

• Conduct integration tests to validate new functionalities.
• Gather feedback from a broader user base to identify areas for improvement.

Final Product

• Generative AI Integration: The bot can generate detailed definitions and explanations for medical terms using transformers.
• Enhanced Database Management: Efficient and reliable database operations with automated updates.
• Advanced Testing and User Feedback: Integration tests for new functionalities and broader user feedback gathered to refine features.

Advanced Natural Language Understanding (NLU) Branch | Cycle 2 Branch: feature/cycle-2-nlu Sub-branches

• feature/nlu-integration
• feature/data-manipulation
• feature/specialty-specific-terms

Cycle 3: Specialty Prediction and User Experience Enhancements

Alpha Prototype 3.1: Specialty Prediction Model Tasks

• Develop and train models using scikit-learn to predict relevant medical specialties based on user queries.
• Implement functions for suggesting specialties.
• Enhance the Discord interface to display predicted specialties.
• Conduct initial user testing for feedback on prediction functionalities.

Prediction Model Development

• Develop and train models using scikit-learn to predict relevant medical specialties based on user queries.
• Implement functions for suggesting specialties.

User Experience Enhancements

• Design intuitive and consistent interactions within Discord.
• Provide clear and concise responses.

Final Product

• Specialty Prediction: The bot can suggest relevant medical specialties based on user queries using a trained scikit-learn model.
• Improved User Experience: More intuitive and consistent interactions, providing clear and concise responses.
• Security Enhancements: Initial implementation of security measures for data handling and storage.

Beta Prototype 3.2: Enhanced User Experience and Security Tasks

• Design intuitive and consistent interactions within Discord.
• Provide clear and concise responses.
• Implement security measures for data handling and storage.
• Conduct performance testing to ensure the bot can handle multiple queries simultaneously.

Security Enhancements

• Implement security measures for data handling and storage.
• Ensure compliance with relevant regulations (e.g., HIPAA).

Performance Optimization

• Conduct performance tests to ensure the bot can handle multiple queries simultaneously.
• Optimize code for better performance and scalability.

Testing and Final Adjustments

• Perform comprehensive testing (unit, integration, and performance).
• Implement final adjustments based on testing results and user feedback.

Final Product

• Comprehensive Security Measures: Robust security protocols for data protection and compliance with relevant regulations.
• Optimized Performance: The bot can handle multiple queries simultaneously without performance degradation.
• Comprehensive Testing: Thorough testing of all features, including performance and security.

Generative AI and Machine Learning Enhancements Branch for cylce 3 Branch: feature/cycle-3-ml-ai Sub-branches:

• feature/ml-models
• feature/generative-ai
• feature/automatic-analysis

Cycle 4: Comprehensive Testing and Finalization

Alpha Prototype 4.1: Comprehensive Testing Tasks

• Conduct thorough testing of all features (unit tests, integration tests).
• Ensure compatibility across different devices and operating systems.
• Optimize code for performance and scalability.
• Prepare documentation and user guides.

Thorough Testing

• Conduct thorough testing of all features (unit tests, integration tests).
• Ensure compatibility across different devices and operating systems.

Documentation

• Prepare comprehensive documentation, including setup guides and user manuals.
• Ensure all code is well-documented with clear comments and docstrings.

Final Product

• Thoroughly Tested Product: Comprehensive unit and integration tests ensuring all features work as expected.
• Comprehensive Documentation: Detailed documentation including setup guides, user manuals, and developer notes.
• Final Adjustments: Implemented changes based on testing results and user feedback.

Beta Prototype 4.2: Finalization and Deployment Tasks

• Finalize UI/UX based on comprehensive feedback.
• Ensure security measures are in place (authentication, data protection).
• Conduct final user acceptance testing (UAT).
• Deploy the application and monitor initial user interactions for any issues.

Final Adjustments and Deployment

• Implement changes based on final feedback.
• Deploy the bot to a cloud service or local server for production use.

Market Launch

• Officially launch the bot to the target market.
• Monitor performance and gather ongoing feedback for future improvements.

Market Validation

• Analyze user adoption and feedback to assess market viability.
• Identify potential opportunities for further enhancements or new features.

Final Product

• Market-Ready Bot: Fully functional and optimized bot ready for deployment to a production environment.
• Market Launch: The bot is officially launched to the target market, and user adoption is monitored.
• Market Validation: Initial analysis of user adoption and feedback to assess market viability and plan for future improvements.

User Experience and Expansion Branch for cycle 4 Branch: feature/cycle-4-user-experience Sub-branches:

• feature/user-friendly-interface
• feature/database-expansion
• feature/scalability-maintenance

Project structure

MEDICAL-INTERPRETER-BOT/
├── .github/
│   └── workflows/
│       └── python-package.yml
├── config/
├── data/
│   ├── medical_terms.db
│   └── terms.csv
├── docs/
├── logs/
├── scripts/
├── src/
│   ├── bot.py
│   ├── database.py
│   ├── main.py
│   ├── nlu_model.py
│   └── term_management.py
├── tests/
│   ├── unit/
│   │   ├── test_bot.py
│   │   ├── test_database.py
│   │   └── test_nlu_model.py
│   └── integration/
├── venv/
│   ├── Include/
│   ├── Lib/
│   ├── Scripts/
│   └── pyvenv.cfg
├── .env
├── .gitignore
├── .gitlab-ci.yml
├── LICENSE
├── project-details.md
├── README.md
├── requirements.txt
├── requirements-dev.txt
├── sync_repos.sh
├── Dockerfile
└── docker-compose.yml

Folder and File Details

• .github/ workflows/: This directory contains GitHub Actions workflow files for Continuous Integration/Continuous Deployment (CI/CD) processes.
• python-package.yml: Configuration file for running tests and other CI tasks using GitHub Actions.
• config/ This directory is for storing configuration files. It can include environment-specific settings, API keys, and other configuration data.
• data/ This directory contains data files used by the project.
• medical_terms.db: SQLite database file storing medical terms and definitions.
• terms.csv: CSV file containing a list of medical terms and definitions.
• docs/ This directory is for project documentation. It can include user guides, developer guides, architecture diagrams, and any other relevant documentation.
• logs/ This directory stores log files generated by the application. Log files help in debugging and monitoring the bot's activities.
• scripts/ This directory contains utility scripts for various tasks such as database migrations, data cleaning, or setup scripts.
• src/ This directory contains the source code of the application.
• bot.py: Manages Discord bot interactions and command handling.
• database.py: Handles all database operations using SQLAlchemy or direct SQLite interactions.
• main.py: The entry point for running the bot and integrating all modules.
• nlu_model.py: Implements the natural language understanding model using spaCy or transformers.
• term_management.py: Manages term additions, updates, and deletions.
• tests/ This directory contains test files for the application.
• unit/ Contains unit tests for individual components.
• test_bot.py: Unit tests for bot.py.
• test_database.py: Unit tests for database.py.
• test_nlu_model.py: Unit tests for nlu_model.py.
• integration/ Contains integration tests to validate the interaction between different components.
• venv/ This directory contains the virtual environment for the project, which includes all the project dependencies.
• Include/ Header files for the Python environment.
• Lib/ Libraries and packages installed in the virtual environment.
• Scripts/ Executable scripts for the virtual environment.
• pyvenv.cfg Configuration file for the virtual environment.
• .env This file contains environment variables for the project, such as API keys and database URLs. It is typically excluded from version control for security reasons.
• .gitignore This file specifies files and directories that should be ignored by Git, such as venv/, .env, and other temporary files.
• .gitlab-ci.yml Configuration file for GitLab CI/CD pipelines, specifying how the application should be built, tested, and deployed.
• LICENSE This file contains the license information for the project, specifying how the project can be used by others.
• project-details.md This file provides detailed information about the project, including scope, objectives, requirements, and development plans.
• README.md This file provides an overview of the project, including setup instructions, usage guidelines, and other essential information.
• requirements.txt This file lists the Python dependencies required for the project.
• requirements-dev.txt This file lists the Python dependencies required for development, such as testing and debugging tools.
• sync_repos.sh A script to synchronize repositories, useful for keeping the project up-to-date with remote changes.
• Dockerfile/ This file contains instructions for building a Docker image for the application.
• docker-compose.yml This file contains instructions for setting up and running multiple Docker containers for the application, useful for development and testing environments.

Git

• sync_repos.sh: Use for full repository syncs.
• sync_branch.sh: Use for syncing individual branches.
• sync_feature.sh: Use for syncing all feature branches.
• sync_hotfix.sh: Use for syncing all hotfix branches.
• merge_features.sh: Use for merging features in main

The provided scripts incorporate good practices, including error handling, debugging output, and clear messaging.

When to Use Each Script

sync_repos.sh

1. For updating all default branches Run the sync_repos.sh script without any arguments to update all default branches:

./sync_repos.sh

2. For updating specific branches: Pass the branch names as arguments to the sync_repos.sh script to update specific branches:

./sync_repos.sh feature/bot-initialization feature/database-setup

sync_branch.sh

• Purpose: Syncs a single specified branch.
• Use Case: Use this script when you need to sync a specific branch, for example, if you’ve made changes to one particular branch and want to ensure it’s up-to-date across both repositories.
• Frequency: Use as needed, particularly when working on or updating specific branches.
• Comand

./sync_branch.sh <branch-name>

ej. --- ./sync_branch.sh feature/add-new-functionality

sync_feature.sh

• Purpose: Syncs all feature branches.
• Use Case: Use this script when you want to focus on syncing all your feature branches separately from main and develop branches.
• Frequency: Use this periodically, especially during active feature development.
• Comand

./sync_feature.sh

sync_hotfix.sh

• Purpose: Syncs all hotfix branches.
• Use Case: Use this script when you want to ensure hotfix branches are quickly synced due to their critical nature.
• Frequency: Use this immediately after creating or updating a hotfix branch to ensure critical fixes are applied across both repositories.
• Comand

./sync_hotfix.sh

merge_features.sh

• Purpose: The merge_features.sh script is designed to automate the process of merging all feature branches into the main branch. This script ensures that all the development work done in various feature branches is consolidated into the main branch in a controlled and consistent manner.
• USe Case: At the end of a development cycle, when all features have been developed and tested, this script can be used to merge these feature branches into main.
• Frequency: Run the script whenever you need to merge accumulated feature work into the main branch, such as before significant testing phases or deployments.
• Comand

./merge_features.sh

Advantages of Using Separate Scripts

• Modularity: Each script handles a specific task, making the code easier to understand and maintain.
• Reusability: You can reuse the sync_branch.sh script independently if you need to sync a specific branch manually.
• Easier Debugging: When issues arise, it's easier to identify and fix problems within a smaller, focused script.