chore: Remove remaining docs from git

DB_SCHEMA.md

@@ -1,45 +0,0 @@
-# Database Schema Documentation
-
-The application uses a single table `prediction_logs` to store all prediction requests and results.
-
-## Table: `prediction_logs`
-
-| Column Name | Type | Description |
-| :--- | :--- | :--- |
-| `id` | Integer | Primary Key, Auto-increment |
-| `timestamp` | DateTime | Timestamp of the prediction (UTC) |
-| `prediction` | Integer | Predicted class (0 or 1) |
-| `probability` | Float | Probability of the positive class (optional) |
-| `age` | Integer | Age of the employee |
-| `genre` | String | Gender (M/F) |
-| `revenu_mensuel` | Integer | Monthly income |
-| `statut_marital` | String | Marital status |
-| `departement` | String | Department |
-| `poste` | String | Job title |
-| `nombre_experiences_precedentes` | Integer | Number of previous companies |
-| `nombre_heures_travailless` | Integer | Hours worked |
-| `annee_experience_totale` | Integer | Total working years |
-| `annees_dans_l_entreprise` | Integer | Years at current company |
-| `annees_dans_le_poste_actuel` | Integer | Years in current role |
-| `satisfaction_employee_environnement` | Integer | Environment satisfaction (1-4) |
-| `note_evaluation_precedente` | Integer | Previous performance rating |
-| `niveau_hierarchique_poste` | Integer | Job level |
-| `satisfaction_employee_nature_travail` | Integer | Job involvement (1-4) |
-| `satisfaction_employee_equipe` | Integer | Relationship satisfaction (1-4) |
-| `satisfaction_employee_equilibre_pro_perso` | Integer | Work-life balance (1-4) |
-| `note_evaluation_actuelle` | Integer | Current performance rating |
-| `heure_supplementaires` | String | Overtime (Yes/No) |
-| `augementation_salaire_precedente` | String | Percent salary hike |
-| `nombre_participation_pee` | Integer | Stock option level |
-| `nb_formations_suivies` | Integer | Training times last year |
-| `nombre_employee_sous_responsabilite` | Integer | Number of subordinates |
-| `distance_domicile_travail` | Integer | Distance from home |
-| `niveau_education` | Integer | Education level (1-5) |
-| `domaine_etude` | String | Education field |
-| `ayant_enfants` | String | Has children (Yes/No) |
-| `frequence_deplacement` | String | Business travel frequency |
-| `annees_depuis_la_derniere_promotion` | Integer | Years since last promotion |
-| `annes_sous_responsable_actuel` | Integer | Years with current manager |
-
-## Relationships
-No relationships as this is a single-table logging schema for this POC.

SETUP_GUIDE.md

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-# Environment & Deployment Setup Guide
-
-## 1. Verify Local Environment
-
-### Installed Packages
-Your environment has all necessary packages installed (`fastapi`, `uvicorn`, `xgboost`, `sqlalchemy`, etc.).
-You can verify this anytime with:
-```bash
-pip list | grep -E "fastapi|uvicorn|xgboost|sqlalchemy|pandas"
-```
-
-### Local Server
-Your local server is running successfully at `http://127.0.0.1:8000`.
-Health check:
-```bash
-curl http://127.0.0.1:8000/health
-```
-
-## 2. Hugging Face Credentials
-
-To deploy your application to Hugging Face Spaces via GitHub Actions, you need to configure some secrets.
-
-### A. Get your Hugging Face Token (HF_TOKEN)
-This token allows GitHub to push code to your Space.
-
-1.  Log in to your [Hugging Face account](https://huggingface.co/).
-2.  Go to **Settings** > **Access Tokens** (https://huggingface.co/settings/tokens).
-3.  Click **"New token"**.
-4.  Name it (e.g., `github-actions`).
-5.  Select **"Write"** permissions (Crucial for deployment).
-6.  Click **"Generate a token"** and copy it.
-7.  **Action**: Add this as a Secret in your GitHub Repository (`Settings` > `Secrets and variables` > `Actions` > `New repository secret`) with the name `HF_TOKEN`.
-
-### B. Create the Space
-1.  Go to [Hugging Face Spaces](https://huggingface.co/spaces).
-2.  Click **"Create new Space"**.
-3.  Enter a name (e.g., `prediction-api`).
-4.  Select **Docker** as the SDK.
-5.  Click **"Create Space"**.
-6.  **Action**: Note down the `HF_USERNAME` (your username) and `SPACE_NAME` (the name you gave). Add these as Secrets in GitHub as well.
-
-### C. Configure Space Secrets (Environment Variables)
-These are the variables your running application needs (Database, API Key).
-
-1.  In your Space, go to **Settings**.
-2.  Scroll down to **"Variables and secrets"**.
-3.  Click **"New secret"**.
-4.  Add the following:
-    *   `API_KEY`: A strong password you choose (e.g., `my-super-secret-key-123`).
-    *   `DATABASE_URL`:
-        *   **Option 1 (External PostgreSQL)**: `postgresql://user:password@host:port/dbname` (If you have a hosted DB like Supabase, Neon, or AWS RDS).
-        *   **Option 2 (Internal SQLite - NOT Recommended for Prod)**: `sqlite:///./data.db` (Note: Data will be lost if the Space restarts unless you use persistent storage).
-        *   **Option 3 (Persistent Storage)**: If you enable Persistent Storage in HF Space settings, map it to `/data` and use `sqlite:////data/db.sqlite`.
-
-## 3. Summary of Secrets needed in GitHub
-| Secret Name | Value |
-| :--- | :--- |
-| `HF_TOKEN` | Your Write-access HF Token |
-| `HF_USERNAME` | Your HF Username |
-| `SPACE_NAME` | The name of your Space |
-
-## 4. Summary of Secrets needed in Hugging Face Space
-| Secret Name | Value |
-| :--- | :--- |
-| `API_KEY` | Your chosen API key |
-| `DATABASE_URL` | Connection string to your database |

VERIFICATION_GUIDE.md

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-# Project Verification Guide
-
-Follow these steps to verify the entire project functionality.
-
-## 1. Environment Setup
-Ensure dependencies are installed:
-```bash
-pip install -r requirements.txt
-```
-
-## 2. Database Initialization
-Initialize the database tables:
-```bash
-PYTHONPATH=. python scripts/create_db.py
-```
-
-## 3. Run the API
-Start the FastAPI server (if not already running):
-```bash
-uvicorn app.main:app --reload
-```
-
-## 4. End-to-End Verification
-Run the verification script to test the API and Database logging:
-```bash
-PYTHONPATH=. python scripts/verify_deployment.py
-```
-This script will:
-1. Check the `/health` endpoint.
-2. Send a prediction request to `/predict`.
-3. Verify the response contains a prediction.
-4. Query the database to ensure the prediction was logged.
-
-## 5. Run Automated Tests
-Execute the test suite to verify all components:
-```bash
-pytest
-```
-
-## 6. Docker Verification
-Build the Docker image to ensure containerization works:
-```bash
-docker build -t ml-api .
-```