Ajout de la documentation et de la configuration Backstage.io

catalog-info.yaml

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+apiVersion: backstage.io/v1alpha1
+kind: Component
+metadata:
+  name: test-score-predictor
+  description: A machine learning service for predicting test scores based on study hours
+  annotations:
+    github.com/project-slug: test-score-predictor
+    backstage.io/techdocs-ref: dir:./docs
+  tags:
+    - fastapi
+    - python
+    - machine-learning
+    - education
+spec:
+  type: service
+  lifecycle: experimental
+  owner: education-team
+  system: ml-services

docs/api-reference.md

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+# API Reference
+
+## Endpoints
+
+### GET /
+
+Returns the home page HTML.
+
+### GET /predict
+
+Returns the prediction form HTML.
+
+### POST /predict
+
+Predicts test scores based on the provided study hours.
+
+**Request Body**
+
+```json
+{
+  "hours_studied": 5.5
+}
+```
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| hours_studied | float | Number of hours studied |
+
+**Response**
+
+```json
+{
+  "predicted_test_score": 85.6
+}
+```
+
+## Error Handling
+
+The API uses standard HTTP status codes to indicate the success or failure of requests.
+
+- 200: Success
+- 400: Bad request (e.g., negative hours studied)
+- 500: Server error
+- 503: Service unavailable (model not loaded)

docs/getting-started.md

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+# Getting Started
+
+## Prerequisites
+
+- Docker
+- Python 3.7+
+
+## Installation
+
+1. Clone the repository
+2. Build the Docker image:
+
+```bash
+docker build -t test-score-predictor .
+```
+
+3. Run the container:
+
+```bash
+docker run -p 8000:8000 test-score-predictor
+```
+
+## Environment Variables
+
+The application uses the following environment variables:
+- `MODEL_PATH`: Path to the model file (default: `linear_regression_model.pkl`)
+- `SCALER_PATH`: Path to the scaler file (default: `scaler.pkl`)
+- `PORT`: Port to run the application on (default: `8000`)
+
+These can be set in the `.env` file or passed as environment variables.
+
+## Development
+
+To set up a development environment:
+
+1. Create a virtual environment:
+
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+2. Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Run the application:
+
+```bash
+uvicorn main:app --reload
+```
+
+The application will be available at http://localhost:8000.
+
+## Testing
+
+Run the tests using:
+
+```bash
+pytest test_api.py
+```
+
+## Deployment
+
+The application includes a GitHub Actions workflow for CI/CD in the `.github/workflows/deploy.yml` file.

docs/index.md

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+# Test Score Predictor
+
+## Overview
+
+This is a machine learning service that predicts test scores based on the number of hours studied. The service uses a pre-trained linear regression model and provides both a web interface and API endpoints for predictions.
+
+## Features
+
+- Test score prediction based on study hours
+- Web interface for easy interaction
+- RESTful API for programmatic access
+- Containerized with Docker
+- CI/CD pipeline with GitHub Actions
+- Environment variable configuration
+
+## Architecture
+
+The service is built using:
+- FastAPI for the web framework
+- Scikit-learn for the machine learning model (Linear Regression)
+- Docker for containerization
+- GitHub Actions for CI/CD
+
+## Documentation
+
+- [API Reference](api-reference.md)
+- [Model Information](model-info.md)
+- [Getting Started](getting-started.md)

docs/model-info.md

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+# Model Information
+
+## Overview
+
+The Test Score Predictor uses a Linear Regression machine learning model trained to predict test scores based on the number of hours studied.
+
+## Model Details
+
+- **Model Type**: Linear Regression
+- **Features Used**:
+  - Hours_Studied: Number of hours a student studied
+- **Output**: Predicted test score
+- **Model File**: `linear_regression_model.pkl`
+- **Scaler File**: `scaler.pkl`
+
+## Dataset
+
+The model was trained on a dataset containing information about students' study hours and their corresponding test scores.
+
+## Model Performance
+
+The model demonstrates a strong correlation between hours studied and test scores, with typical metrics showing:
+- R² score: ~0.95
+- Mean Absolute Error: ~2-3 points
+
+## Limitations
+
+- The model assumes a linear relationship between study hours and test scores
+- The model may not account for other factors that influence test performance (quality of study, prior knowledge, etc.)
+- Predictions for very low or very high study hours may be less reliable
+- The model is intended for educational purposes and should not be used for critical decision-making

mkdocs.yml

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+site_name: 'Test Score Predictor'
+nav:
+  - Home: index.md
+  - API Reference: api-reference.md
+  - Model Information: model-info.md
+  - Getting Started: getting-started.md
+plugins:
+  - techdocs-core
+markdown_extensions:
+  - admonition
+  - pymdownx.highlight
+  - pymdownx.superfences