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This Project Maintain by Github Action (CI/CD) for NLP Model Deployment and API Testing.

Account

• Email: nlp.aiub.101@gmail.com
• Password: Nlp101!##

NLP Project Timeline

ID	Phase	Start Date	End Date	Status
1	Planning	April 13	April 16	🟡
6	Coding	April 13	April 28	🎯
2	Design	April 17	April 19	⏳
3	Backend	April 20	April 22	⏳
4	Frontend	April 23	April 25	⏳
5	Testing	April 26	April 27	⏳
6	Delivery	April 28	April 28	⏳

Completed (✅), In Progress (🟡), Pending (⏳), Milestone (🎯)

Course Details

• Name: Natural Language Processing (NLP)
• Code: CSC 4233
• Institution: American International University-Bangladesh (AIUB)
• Semester: Spring 2025-2026
• Instructor: Dr. Md. Saef Ullah Miah

System Requirements

• Docker Desktop: Multi-container orchestration
• Oparating System: Windows 10/11 (64-bit)
• RAM: Minimum 8GB.
• CPU: Intel i5/Ryzen 5 or higher
• GPU: NVIDIA with CUDA support.

Mandatory VS Code Extension

• FastAPI Extension, DotENV, ESLint, ES7+ (Must)
• Rainbow CSV, Ruff, Shell Runner (Must)
• vscode-pdf, WSL, markdownlint, npm Intellisense (Must)
• Tailwind CSS IntelliSense, Tailwind Fold, ty (Optional)
• Jupyter, Jupyter Cell Tags, Jupyter Keymap, Jupyter Notebook Renderers, Jupyter Slide Show (Must)
• Pylance, Pytest IntelliSense, Python, Python Debugger, Python Environments (Must)

For Docker and Containerization

• Docker, Docker DX, Docker Extension Pack, Docker Run (Must)
• Container Tools, Dev Containers, DevDb (Must)
• Docker-IPython, Docker-Live (Optional)

Docker Setup

1. ⚒️ Build Docker Contaner

docker build -t nlp:latest .

2. ✨ Run Docker Contaner

docker run -d -p 8000:8000 nlp:latest

3. ⚖️ Check Libraries

docker exec -it nlp-container bash && 
pip list | grep -E 'transformers|torch|torch-geometric|fastapi|tensorflow|sklearn|nltk'

4. 🌐 Activate NLP Service

docker compose up -d --build && docker compose ps

5. 🎦 Watch NLP Server Logs

docker compose logs -f nlp &&
docker compose logs -f jupyter

6. 🌐 Access Point for NLP Server

• Fastapi
• Jupyter Notebook

7. Test API (Unit Test)

cd tests & pytest test_api.py
# or in docker
docker compose exec nlp sh -lc "cd /app && PYTHONPATH=/app pytest -q"

7. Test API (Manualy Tested)

Checking FastAPI Endpoints Behaver and system is working Properly or not.

8. Create Next.js Frontend

• creat a Next.js frontend to interact with the FastAPI backend and display results.
• make sure to handle CORS and API calls properly.
• Chacking Data Is Coming or not from FastAPI to Next.js

9. Deploy to Cloud

• Backend: FastAPI
• Frontend: Next.js
• Database: Supabase
• Vector DB: Pinecone
• Model Hosting: HuggingFace or Replicate

10. Make Presentation Slides

• Create a presentation summarizing the project, including architecture, implementation details, and results.
• Use tools like PowerPoint, Google Slides, or Canva for design.
• Prepare to present the project to the class, highlighting key features and challenges faced during development.
• Practice the presentation to ensure clarity and confidence during delivery.
• Include a demo of the application if possible, showcasing its functionality and user interface.
• Be ready to answer questions from the audience about the project, its implementation, and potential future improvements.