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: streamlit-score-predictor
+  description: A Streamlit application for predicting test scores based on study hours
+  annotations:
+    github.com/project-slug: streamlit-score-predictor
+    backstage.io/techdocs-ref: dir:./docs
+  tags:
+    - streamlit
+    - python
+    - machine-learning
+    - education
+spec:
+  type: website
+  lifecycle: experimental
+  owner: education-team
+  system: ml-services

docs/application-guide.md

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+# Application Guide
+
+## Using the Streamlit Application
+
+The Streamlit Score Predictor is a simple web application that allows users to predict test scores based on the number of hours studied.
+
+### Interface Overview
+
+The application consists of:
+1. A title and description
+2. A number input field for entering hours studied
+3. A "Predict" button
+4. A results section that displays the predicted test score
+
+### Making Predictions
+
+1. Enter the number of hours studied in the input field
+   - Use the up/down arrows or type directly in the field
+   - The minimum value is 0
+   - You can increment by 1.0 hour using the arrows
+
+2. Click the "Predict" button
+
+3. View the predicted test score
+   - The prediction will appear below the button
+   - The score is displayed with two decimal places
+
+### Error Handling
+
+If there is an issue with the prediction (e.g., invalid input or model error), an error message will be displayed.

docs/getting-started.md

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+# Getting Started
+
+## Prerequisites
+
+- Python 3.7+
+- Streamlit
+- Scikit-learn
+- Joblib
+
+## Installation
+
+1. Clone the repository
+2. Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Running the Application
+
+Run the Streamlit application with:
+
+```bash
+streamlit run streamlit.py
+```
+
+The application will open in your default web browser, typically at http://localhost:8501.
+
+## Development
+
+To modify the application:
+
+1. Edit the `streamlit.py` file to change the UI or functionality
+2. The application will automatically reload when you save changes
+
+## Deployment
+
+Streamlit applications can be deployed in several ways:
+
+1. **Streamlit Sharing**: A free hosting service for Streamlit apps
+2. **Heroku**: Deploy using a Procfile and requirements.txt
+3. **Docker**: Containerize the application for deployment anywhere
+
+For deployment, ensure that the model and scaler files are included with the application code.

docs/index.md

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+# Streamlit Score Predictor
+
+## Overview
+
+This is a Streamlit application that predicts test scores based on the number of hours studied. The application uses a pre-trained linear regression model to make predictions.
+
+## Features
+
+- Simple, user-friendly web interface built with Streamlit
+- Test score prediction based on study hours
+- Interactive input controls
+- Immediate prediction results
+
+## Architecture
+
+The application is built using:
+- Streamlit for the web interface
+- Scikit-learn for the machine learning model (Linear Regression)
+- Joblib for model loading
+
+## Documentation
+
+- [Application Guide](application-guide.md)
+- [Model Information](model-info.md)
+- [Getting Started](getting-started.md)

docs/model-info.md

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+# Model Information
+
+## Overview
+
+The Streamlit 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: 'Streamlit Score Predictor'
+nav:
+  - Home: index.md
+  - Application Guide: application-guide.md
+  - Model Information: model-info.md
+  - Getting Started: getting-started.md
+plugins:
+  - techdocs-core
+markdown_extensions:
+  - admonition
+  - pymdownx.highlight
+  - pymdownx.superfences