Initial Commit

Author: pH-7

Dockerfile

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+FROM python:3.10
+
+WORKDIR /app
+
+COPY requirements.txt requirements.txt
+RUN pip install -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

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+# 🚀 Python GML ML Pipeline
+
+✅ Logistic Regression churn prediction  
+✅ FastAPI REST endpoint  
+✅ OpenAI summarisation for human-readable explanations
+
+---
+
+## 🔧 How to run
+
+1. **Install requirements:**
+
+```bash
+pip install -r requirements.txt
+```
+
+2. **Run locally:**
+
+```bash
+uvicorn app:app --reload
+```
+
+3. **Test with:**
+
+```bash
+curl -X POST http://localhost:8000/predict -H "Content-Type: application/json" -d '{"feature1": value, "feature2": value, ...}'
+```
+
+---
+
+> Replace `YOUR_API_KEY` in `app.py` with your actual OpenAI key.
+
+---
+
+## 📂 Project Structure
+
+```
+Python_GML_ML_Pipeline/
+├── app.py
+├── requirements.txt
+├── logistic_model.pkl
+├── scaler.pkl
+└── README.md
+```
+
+---
+
+## ✨ Author
+
+[![Pierre-Henry Soria](https://avatars0.githubusercontent.com/u/1325411?s=200)](https://ph7.me "Pierre-Henry Soria, Software Developer")
+
+Made with ❤️ by **[Pierre-Henry Soria](https://pierrehenry.be)**. A super passionate & enthusiastic Problem-Solver / Senior Software Engineer. Also a true cheese 🧀, ristretto ☕️, and dark chocolate lover! 😋
+
+[![@phenrysay](https://img.shields.io/badge/x-000000?style=for-the-badge&logo=x)](https://x.com/phenrysay "Follow Me on X")  
+[![pH-7](https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white)](https://github.com/pH-7 "My GitHub")  
+[![YouTube Video](https://img.shields.io/badge/YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white)](https://youtu.be/cWBuZ4DXGK4 "YouTube SucceedAI Video")
+
+---
+
+## 📌 Notes
+
+- `logistic_model.pkl` and `scaler.pkl` are **placeholders**. Train and export your own models using `joblib.dump`.
+- This project is a **modern, production-ready ML pipeline**, showcasing deployment and explainability best practices for 2025 and beyond.
+
+---
+
+### 🧠 Final Wise Principle
+
+> **“AI models become valuable when they’re deployable, explainable, and integrated into real products that create business value.”**

app.py

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+import pandas as pd
+import joblib
+from fastapi import FastAPI
+import openai
+
+app = FastAPI()
+
+# Load model and scaler
+model = joblib.load('logistic_model.pkl')
+scaler = joblib.load('scaler.pkl')
+
+# Set OpenAI API key
+openai.api_key = "YOUR_API_KEY"
+
+
+@app.post('/predict')
+def predict(data: dict):
+    df = pd.DataFrame([data])
+    scaled = scaler.transform(df)
+    prob = model.predict_proba(scaled)[:, 1][0]
+
+    # Call OpenAI for summarisation
+    summary = summarise_prediction(prob, data)
+
+    return {
+        'churn_probability': prob,
+        'explanation': summary
+    }
+
+
+def summarise_prediction(probability, input_data):
+    prompt = f"""
+You are an AI assistant. The model predicted a churn probability of {probability:.2f} for this customer with the following features:
+
+{input_data}
+
+Write a short, clear summary explaining this result to a business stakeholder in simple language.
+"""
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": prompt}],
+        max_tokens=100
+    )
+    return response['choices'][0]['message']['content']

logistic_model.pkl

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+fastapi
+uvicorn
+pandas
+joblib
+scikit-learn
+openai