Initial Commit

Author: pH-7

.gitignore

@@ -0,0 +1,70 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# mypy
+.mypy_cache/
+.dmypy.json
+
+# VS Code
+.vscode/
+
+# Docker
+*.log
+
+# Model artifacts
+models/*.joblib
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Environment variables
+.env
+.env.*

Dockerfile

@@ -0,0 +1,9 @@
+
+FROM python:3.11-slim
+
+WORKDIR /app
+COPY . .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -0,0 +1,49 @@
+
+# Logistic Regression ML Pipeline with FastAPI 🚀
+
+A clean and modern ML microservice for logistic regression using FastAPI. Built with scalability, explainability, and deployability in mind.
+
+## 🔧 Project Structure
+
+```
+logistic-regression-fastapi/
+│
+├── app/
+│   ├── main.py           # FastAPI app entrypoint
+│   ├── model.py          # Model loading and prediction logic
+│   └── schemas.py        # Pydantic request/response models
+│
+├── models/
+│   └── logistic_model.joblib   # Pretrained logistic regression model
+│
+├── notebooks/
+│   └── train_model.ipynb       # Jupyter notebook for training and evaluation
+│
+├── Dockerfile           # Containerisation setup
+└── requirements.txt     # Python dependencies
+```
+
+## 🧪 Training
+
+The `notebooks/train_model.ipynb` trains a simple logistic regression classifier and exports the model.
+
+## ▶️ Run API
+
+```bash
+uvicorn app.main:app --reload
+```
+
+## 🐳 Docker
+
+```bash
+docker build -t logistic-api .
+docker run -d -p 8000:8000 logistic-api
+```
+
+## ✨ Author
+
+[![Pierre-Henry Soria](https://avatars0.githubusercontent.com/u/1325411?s=200)](https://ph7.me)
+
+Made with ❤️ by **[Pierre-Henry Soria](https://pierrehenry.be)** — an AI Data Scientist & Senior Software Engineer. Incredibly passionate about AI, machine learning, data science, and emerging technologies. I could happily talk all night about programming and IT with anyone who’s keen. Roquefort 🧀, ristretto ☕️, and dark chocolate lover! 😋
+
+[![@phenrysay](https://img.shields.io/badge/x-000000?style=for-the-badge&logo=x)](https://x.com/phenrysay)  [![GitHub](https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/pH-7)  [![YouTube](https://img.shields.io/badge/YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white)](https://youtu.be/cWBuZ4DXGK4)  [![Bluesky](https://img.shields.io/badge/bluesky-1e90ff?style=for-the-badge&logo=data:image/svg+xml;base64,PHN2ZyBmaWxsPSIjMDAwMDAwIiBoZWlnaHQ9IjI0cHgiIHZpZXdCb3g9IjAgMCAzMiAzMiIgd2lkdGg9IjI0cHgiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PHBhdGggZD0iTTMwIDZsLTIuOTk5LTEuNjY2TDMyIDMuMzQgMjMuMTg5IDAgMTYuMDA2IDUuMzQgOC44MTMgMCAwIDMuMzQgNC45OTkgNC4zMzQgMCA2bDUuMDAxIDQuODAzTDQgMjAuODFWMjRsNS4wMDEtMS42NjZMMTYgMjhMMjIuOTk5IDIyLjM0IDMyIDI0di0zLjE4OUwyNy4wMDIgMTIgMzAgNiIgLz48L3N2Zz4=)](https://bsky.app/profile/ph7s.bsky.social "Bluesky Profile")

app/main.py

@@ -0,0 +1,11 @@
+
+from fastapi import FastAPI
+from app.model import predict
+from app.schemas import PredictRequest, PredictResponse
+
+app = FastAPI()
+
+@app.post("/predict", response_model=PredictResponse)
+def predict_endpoint(req: PredictRequest):
+    prediction = predict(req.features)
+    return PredictResponse(prediction=prediction)

app/model.py

@@ -0,0 +1,11 @@
+
+import joblib
+import numpy as np
+from pathlib import Path
+
+model_path = Path(__file__).parent.parent / "models" / "logistic_model.joblib"
+model = joblib.load(model_path)
+
+def predict(features: list) -> int:
+    X = np.array(features).reshape(1, -1)
+    return int(model.predict(X)[0])

app/schemas.py

@@ -0,0 +1,9 @@
+
+from pydantic import BaseModel
+from typing import List
+
+class PredictRequest(BaseModel):
+    features: List[float]
+
+class PredictResponse(BaseModel):
+    prediction: int

notebooks/train_model.ipynb

@@ -0,0 +1,7 @@
+
+fastapi
+uvicorn
+joblib
+scikit-learn
+numpy
+pydantic