Serving Machine Learning Models through RESTish APIs using Flask in Python

Key	Value
Course Code	BBT 4206
Course Name	BBT 4206: Business Intelligence II (Week 4-6 of 13)
Semester	January to April 2026
Lecturer	Allan Omondi
Contact	aomondi@strathmore.edu
Note	The lecture contains both theory and practice. This notebook forms part of the practice. It is intended for educational purposes only. Recommended citation: BibTex

Technology Stack

Repository Structure

.
├── LICENSE
├── Procfile
├── README.md
├── RecommendedCitation.bib
├── admin_instructions
│   ├── instructions_for_postlab_cleanup.md
│   ├── instructions_for_project_setup.md
│   └── instructions_for_python_installation.md
├── api.py
├── app_server_reverse_proxy_server_setup.md
├── assets
│   └── images
│       ├── Hf-logo-with-title.svg
│       ├── Render-logo-Black.png
│       ├── Streamlit-logo-primary-colormark-darktext.png
│       └── ssh_student_at_localhost_p_2222.jpeg
├── cleanup_instructions.md
├── container-volumes
│   ├── nginx
│   │   └── nginx.conf
│   └── ubuntu
├── docker-compose-dev.yaml
├── docker-compose-prod.yaml
├── docker-compose.yaml
├── dockerfiles
│   ├── Dockerfile.flask-gunicorn-app
│   ├── Dockerfile.nginx
│   └── ubuntu
│       ├── Dockerfile.ubuntu
│       └── entrypoint.sh
├── env.example
├── frontend
│   ├── Proxies.png
│   ├── RequestFlow.jpg
│   ├── RequestFlow.png
│   ├── api_consumer.py
│   ├── api_consumer_from_dev_flask.py
│   ├── api_test_DT_classifier.html
│   ├── api_test_DT_regressor.html
│   └── index.html
├── huggingface-spaces-using-gradio
│   ├── app.py
│   └── requirements.txt
├── lab_submission_instructions.md
├── model
│   ├── decisiontree_classifier_baseline.pkl
│   ├── decisiontree_regressor_optimum.pkl
│   ├── knn_classifier_optimum.pkl
│   ├── label_encoders_1b.pkl
│   ├── label_encoders_2.pkl
│   ├── label_encoders_4.pkl
│   ├── label_encoders_5.pkl
│   ├── naive_Bayes_classifier_optimum.pkl
│   ├── onehot_encoder_3.pkl
│   ├── random_forest_classifier_optimum.pkl
│   ├── scaler_4.pkl
│   ├── scaler_5.pkl
│   └── support_vector_classifier_optimum.pkl
├── publicly_serving_the_model_for_validation_by_domain_experts.md
├── requirements
│   ├── base.txt
│   ├── colab.txt
│   ├── constraints.txt
│   ├── dev.inferred.txt
│   ├── dev.lock.txt
│   ├── dev.txt
│   └── prod.txt
├── rules
├── runtime.txt
└── streamlit-sharing-using-streamlit
    ├── app.py
    └── requirements.txt

15 directories, 58 files

Setup Instructions

• Setup Instructions

Lab Manual

Refer to the files below, in the order specified, for more details:

1. api_consumer.py
2. api.py
3. api_consumer_from_dev_flask.py
4. api_test_DT_classifier.html
5. api_test_DT_regressor.html
6. Reverse Proxy Server and Application Server Setup
7. Publicly Serving the Model for Validation by Domain Experts

Lab Submission Instructions

• Lab Submission Instructions

Cleanup Instructions (to be done after submitting the lab)

• Cleanup Instructions