setup api

.github/workflows/ci.yaml

@@ -0,0 +1,80 @@
+name: CI
+
+on:
+  push:
+    branches: [ main, dev, test, "feature/*", "fix/*" ]
+  pull_request:
+    branches: [ main, dev, test ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+
+    services:
+      postgres:
+        image: postgres:16
+        env:
+          POSTGRES_USER: ci
+          POSTGRES_PASSWORD: ci
+          POSTGRES_DB: futurisys_ci
+
+        ports:
+          - 5432:5432
+        options: >-
+          --health-cmd="pg_isready -U ci -d futurisys_ci"
+          --health-interval=5s
+          --health-timeout=5s
+          --health-retries=20
+
+    env:
+      DATABASE_URL: postgresql+asyncpg://ci:ci@localhost:5432/futurisys_ci
+      HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      APP_ENV: test
+
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install Poetry
+        uses: abatilo/actions-poetry@v3
+        with:
+          poetry-version: "1.8.3"
+
+      - name: Cache Poetry virtualenv
+        uses: actions/cache@v4
+        with:
+          path: |
+            ~/.cache/pypoetry
+            .venv
+          key: ${{ runner.os }}-poetry-3.12-${{ hashFiles('**/poetry.lock') }}
+
+      - name: Install deps
+        run: poetry install --no-interaction --no-ansi
+
+      - name: Wait for Postgres
+        run: |
+          sudo apt-get update && sudo apt-get install -y postgresql-client
+          for i in {1..30}; do
+            pg_isready -h localhost -p 5432 -U ci -d futurisys_ci && break
+            sleep 2
+          done
+
+      - name: Run Alembic migrations
+        run: PYTHONPATH=./src poetry run alembic upgrade head
+
+      - name: Lint (ruff)
+        run: |
+          poetry run python -m pip install ruff
+          poetry run ruff check .
+
+      - name: Tests (pytest + coverage)
+        run: |
+          poetry run python -m pip install pytest pytest-cov
+          poetry run pytest

.github/workflows/deploy.yaml

@@ -0,0 +1,84 @@
+name: Deploy to Hugging Face
+
+on:
+  workflow_run:
+    workflows: ["CI"]          
+    branches: [ main ]         
+    types: [completed]
+  workflow_dispatch: {}
+
+concurrency:
+  group: deploy-${{ github.event.workflow_run.head_branch || 'main' }}
+  cancel-in-progress: true
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    if: ${{ github.event.workflow_run.conclusion == 'success' && github.event.workflow_run.event == 'push' }}
+
+    env:
+      HF_TOKEN: ${{ secrets.HF_TOKEN }}
+      HF_SPACE_URL: ${{ secrets.HF_SPACE_URL }}
+      HF_GIT_EMAIL: ${{ secrets.HF_GIT_EMAIL || 'actions@github.com' }}
+      HF_GIT_NAME:  ${{ secrets.HF_GIT_NAME  || 'github-actions' }}
+      DATABASE_URL: ${{ secrets.DATABASE_URL }}
+
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          lfs: true  
+
+      - name: Guard secrets
+        if: ${{ !env.HF_TOKEN || !env.HF_SPACE_URL }}
+        run: |
+          echo "HF secrets manquants, on skip le déploiement."
+          exit 0
+
+      - name: Configure Git identity (local)
+        shell: bash
+        run: |
+          git config user.email "${HF_GIT_EMAIL:-actions@github.com}"
+          git config user.name  "${HF_GIT_NAME:-github-actions}"
+
+      - name: Convert binaries to Git LFS
+        shell: bash
+        run: |
+          set -e
+          git lfs install
+          git lfs track "*.joblib" "*.pkl" "*.pt" "*.onnx"
+          git add .gitattributes
+          git commit -m "ci: track ML artifacts with LFS for Space push" || true
+
+         
+          git lfs migrate import --everything --include="*.joblib,notebook/df.joblib,*.pkl,*.pt,*.onnx"
+
+          git lfs status || true
+
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install Poetry
+        uses: abatilo/actions-poetry@v3
+        with:
+          poetry-version: "1.8.3"
+
+      - name: Install deps (with dev)
+        run: poetry install --no-interaction --no-root
+
+      - name: Run Alembic migrations
+        env:
+          DATABASE_URL: ${{ env.DATABASE_URL }}
+        run: |
+          echo "Migrating DB: $DATABASE_URL"
+          PYTHONPATH=./src poetry run alembic upgrade head
+
+      - name: Push to Space
+        run: |
+          SPACE_URL_AUTH=$(echo "$HF_SPACE_URL" | sed "s#https://#https://user:${HF_TOKEN}@#")
+          git remote add space "$SPACE_URL_AUTH" || git remote set-url space "$SPACE_URL_AUTH"
+          git push space HEAD:main --force

Dockerfile

@@ -0,0 +1,37 @@
+FROM python:3.12-slim
+
+ENV PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    PORT=7860
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Créer l'utilisateur exigé par Spaces et passer en non-root
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+WORKDIR /app
+
+# Installer Poetry 
+RUN pip install --no-cache-dir "poetry==1.8.3" && poetry --version
+
+COPY --chown=user pyproject.toml poetry.lock* /app/
+
+# Création env poetry
+RUN poetry config virtualenvs.create true \
+ && poetry config virtualenvs.in-project true \
+ && poetry install --no-interaction --no-ansi --only main
+
+# Ajouter le venv au PATH pour trouver uvicorn/python
+ENV PATH="/app/.venv/bin:$PATH"
+
+# Copier le code
+COPY --chown=user src /app/src
+
+EXPOSE 7860
+
+# Lancer FastAPI
+CMD ["uvicorn", "main:app", "--app-dir", "src", "--host", "0.0.0.0", "--port", "7860"]

alembic.ini

@@ -0,0 +1,147 @@
+# A generic, single database configuration.
+
+[alembic]
+# path to migration scripts.
+# this is typically a path given in POSIX (e.g. forward slashes)
+# format, relative to the token %(here)s which refers to the location of this
+# ini file
+script_location = %(here)s/alembic
+
+# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
+# Uncomment the line below if you want the files to be prepended with date and time
+# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
+# for all available tokens
+# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
+
+# sys.path path, will be prepended to sys.path if present.
+# defaults to the current working directory.  for multiple paths, the path separator
+# is defined by "path_separator" below.
+prepend_sys_path = .
+
+
+# timezone to use when rendering the date within the migration file
+# as well as the filename.
+# If specified, requires the python>=3.9 or backports.zoneinfo library and tzdata library.
+# Any required deps can installed by adding `alembic[tz]` to the pip requirements
+# string value is passed to ZoneInfo()
+# leave blank for localtime
+# timezone =
+
+# max length of characters to apply to the "slug" field
+# truncate_slug_length = 40
+
+# set to 'true' to run the environment during
+# the 'revision' command, regardless of autogenerate
+# revision_environment = false
+
+# set to 'true' to allow .pyc and .pyo files without
+# a source .py file to be detected as revisions in the
+# versions/ directory
+# sourceless = false
+
+# version location specification; This defaults
+# to <script_location>/versions.  When using multiple version
+# directories, initial revisions must be specified with --version-path.
+# The path separator used here should be the separator specified by "path_separator"
+# below.
+# version_locations = %(here)s/bar:%(here)s/bat:%(here)s/alembic/versions
+
+# path_separator; This indicates what character is used to split lists of file
+# paths, including version_locations and prepend_sys_path within configparser
+# files such as alembic.ini.
+# The default rendered in new alembic.ini files is "os", which uses os.pathsep
+# to provide os-dependent path splitting.
+#
+# Note that in order to support legacy alembic.ini files, this default does NOT
+# take place if path_separator is not present in alembic.ini.  If this
+# option is omitted entirely, fallback logic is as follows:
+#
+# 1. Parsing of the version_locations option falls back to using the legacy
+#    "version_path_separator" key, which if absent then falls back to the legacy
+#    behavior of splitting on spaces and/or commas.
+# 2. Parsing of the prepend_sys_path option falls back to the legacy
+#    behavior of splitting on spaces, commas, or colons.
+#
+# Valid values for path_separator are:
+#
+# path_separator = :
+# path_separator = ;
+# path_separator = space
+# path_separator = newline
+#
+# Use os.pathsep. Default configuration used for new projects.
+path_separator = os
+
+# set to 'true' to search source files recursively
+# in each "version_locations" directory
+# new in Alembic version 1.10
+# recursive_version_locations = false
+
+# the output encoding used when revision files
+# are written from script.py.mako
+# output_encoding = utf-8
+
+# database URL.  This is consumed by the user-maintained env.py script only.
+# other means of configuring database URLs may be customized within the env.py
+# file.
+sqlalchemy.url = driver://user:pass@localhost/dbname
+
+
+[post_write_hooks]
+# post_write_hooks defines scripts or Python functions that are run
+# on newly generated revision scripts.  See the documentation for further
+# detail and examples
+
+# format using "black" - use the console_scripts runner, against the "black" entrypoint
+# hooks = black
+# black.type = console_scripts
+# black.entrypoint = black
+# black.options = -l 79 REVISION_SCRIPT_FILENAME
+
+# lint with attempts to fix using "ruff" - use the module runner, against the "ruff" module
+# hooks = ruff
+# ruff.type = module
+# ruff.module = ruff
+# ruff.options = check --fix REVISION_SCRIPT_FILENAME
+
+# Alternatively, use the exec runner to execute a binary found on your PATH
+# hooks = ruff
+# ruff.type = exec
+# ruff.executable = ruff
+# ruff.options = check --fix REVISION_SCRIPT_FILENAME
+
+# Logging configuration.  This is also consumed by the user-maintained
+# env.py script only.
+[loggers]
+keys = root,sqlalchemy,alembic
+
+[handlers]
+keys = console
+
+[formatters]
+keys = generic
+
+[logger_root]
+level = WARNING
+handlers = console
+qualname =
+
+[logger_sqlalchemy]
+level = WARNING
+handlers =
+qualname = sqlalchemy.engine
+
+[logger_alembic]
+level = INFO
+handlers =
+qualname = alembic
+
+[handler_console]
+class = StreamHandler
+args = (sys.stderr,)
+level = NOTSET
+formatter = generic
+
+[formatter_generic]
+format = %(levelname)-5.5s [%(name)s] %(message)s
+datefmt = %H:%M:%S

alembic/README

@@ -0,0 +1 @@
+Generic single-database configuration.

alembic/env.py

@@ -0,0 +1,56 @@
+from __future__ import annotations
+from logging.config import fileConfig
+import os
+
+from alembic import context
+from sqlalchemy.ext.asyncio import create_async_engine, AsyncEngine
+from sqlalchemy import pool
+
+from src.config.db import Base
+from dotenv import load_dotenv, find_dotenv
+load_dotenv(find_dotenv())
+
+config = context.config
+if config.config_file_name is not None:
+    fileConfig(config.config_file_name)
+
+target_metadata = Base.metadata
+
+def get_url() -> str:
+    url = os.getenv("DATABASE_URL")
+    return url
+
+def run_migrations_offline() -> None:
+    context.configure(
+        url=get_url(),
+        target_metadata=target_metadata,
+        literal_binds=True,
+        compare_type=True,
+        dialect_opts={"paramstyle": "named"},
+    )
+    with context.begin_transaction():
+        context.run_migrations()
+
+def do_run_migrations(connection) -> None:
+    context.configure(
+        connection=connection,
+        target_metadata=target_metadata,
+        compare_type=True,  
+    )
+    with context.begin_transaction():
+        context.run_migrations()
+
+async def run_migrations_online() -> None:
+    connectable: AsyncEngine = create_async_engine(
+        get_url(),
+        poolclass=pool.NullPool,
+    )
+    async with connectable.connect() as connection:
+        await connection.run_sync(do_run_migrations)
+    await connectable.dispose()
+
+if context.is_offline_mode():
+    run_migrations_offline()
+else:
+    import asyncio
+    asyncio.run(run_migrations_online())

alembic/versions/99e339b56253_initial_schema.py

@@ -0,0 +1,35 @@
+"""initial schema
+
+Revision ID: 99e339b56253
+Revises: 
+Create Date: 2025-09-15 12:01:54.205742
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+
+# revision identifiers, used by Alembic.
+revision: str = '99e339b56253'
+down_revision: Union[str, Sequence[str], None] = None
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+   op.create_table(
+        "ml_models",
+        sa.Column("id", postgresql.UUID(as_uuid=True), primary_key=True, nullable=False),
+        sa.Column("name", sa.String(length=100), nullable=False, unique=True),
+        sa.Column("description", sa.Text(), nullable=True),
+        sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("NOW()"), nullable=False),
+        sa.Column("is_active", sa.Boolean(), server_default=sa.text("TRUE"), nullable=False),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("ml_models")
+

docker-compose.yml

@@ -0,0 +1,19 @@
+services:
+  db:
+    image: postgres:16
+    container_name: pret-db
+    environment:
+      POSTGRES_USER: pret
+      POSTGRES_PASSWORD: pret_pass
+      POSTGRES_DB: pret
+    ports:
+      - "5432:5432"
+    volumes:
+      - pgdata:/var/lib/postgresql/data
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U pret -d pret"]
+      interval: 5s
+      timeout: 5s
+      retries: 10
+volumes:
+  pgdata:

poetry.lock

@@ -823,6 +823,19 @@ docs = ["myst-parser (==0.18.0)", "sphinx (==5.1.1)"]
 ssh = ["paramiko (>=2.4.3)"]
 websockets = ["websocket-client (>=1.3.0)"]
 
+[[package]]
+name = "dotenv"
+version = "0.9.9"
+description = "Deprecated package"
+optional = false
+python-versions = "*"
+files = [
+    {file = "dotenv-0.9.9-py2.py3-none-any.whl", hash = "sha256:29cf74a087b31dafdb5a446b6d7e11cbce8ed2741540e2339c69fbef92c94ce9"},
+]
+
+[package.dependencies]
+python-dotenv = "*"
+
 [[package]]
 name = "executing"
 version = "2.2.1"
@@ -873,6 +886,17 @@ files = [
 [package.extras]
 devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
 
+[[package]]
+name = "filelock"
+version = "3.20.0"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.10"
+files = [
+    {file = "filelock-3.20.0-py3-none-any.whl", hash = "sha256:339b4732ffda5cd79b13f4e2711a31b0365ce445d95d243bb996273d072546a2"},
+    {file = "filelock-3.20.0.tar.gz", hash = "sha256:711e943b4ec6be42e1d4e6690b48dc175c822967466bb31c0c293f34334c13f4"},
+]
+
 [[package]]
 name = "flask"
 version = "3.1.2"
@@ -1002,6 +1026,45 @@ files = [
     {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
 ]
 
+[[package]]
+name = "fsspec"
+version = "2025.10.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "fsspec-2025.10.0-py3-none-any.whl", hash = "sha256:7c7712353ae7d875407f97715f0e1ffcc21e33d5b24556cb1e090ae9409ec61d"},
+    {file = "fsspec-2025.10.0.tar.gz", hash = "sha256:b6789427626f068f9a83ca4e8a3cc050850b6c0f71f99ddb4f542b8266a26a59"},
+]
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+dev = ["pre-commit", "ruff (>=0.5)"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+tqdm = ["tqdm"]
+
 [[package]]
 name = "gitdb"
 version = "4.0.12"
@@ -1205,6 +1268,40 @@ files = [
     {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]
 
+[[package]]
+name = "hf-xet"
+version = "1.2.0"
+description = "Fast transfer of large files with the Hugging Face Hub."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "hf_xet-1.2.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:ceeefcd1b7aed4956ae8499e2199607765fbd1c60510752003b6cc0b8413b649"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:b70218dd548e9840224df5638fdc94bd033552963cfa97f9170829381179c813"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7d40b18769bb9a8bc82a9ede575ce1a44c75eb80e7375a01d76259089529b5dc"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:cd3a6027d59cfb60177c12d6424e31f4b5ff13d8e3a1247b3a584bf8977e6df5"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6de1fc44f58f6dd937956c8d304d8c2dea264c80680bcfa61ca4a15e7b76780f"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f182f264ed2acd566c514e45da9f2119110e48a87a327ca271027904c70c5832"},
+    {file = "hf_xet-1.2.0-cp313-cp313t-win_amd64.whl", hash = "sha256:293a7a3787e5c95d7be1857358a9130694a9c6021de3f27fa233f37267174382"},
+    {file = "hf_xet-1.2.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:10bfab528b968c70e062607f663e21e34e2bba349e8038db546646875495179e"},
+    {file = "hf_xet-1.2.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2a212e842647b02eb6a911187dc878e79c4aa0aa397e88dd3b26761676e8c1f8"},
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 redis = ["redis (>=3.0.0)"]
 
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+    {file = "psycopg_binary-3.2.12-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f8107968a9eadb451cfa6cf86036006fdde32a83cd39c26c9ca46765e653b547"},
+    {file = "psycopg_binary-3.2.12-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:15e226f0d8af85cc8b2435b2e9bc6f0d40febc79eef76cf20fceac4d902a6a7b"},
+    {file = "psycopg_binary-3.2.12-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f6ba1fe35fd215813dac4544a5ffc90f13713b29dd26e9e5be97ba53482bf6d6"},
+    {file = "psycopg_binary-3.2.12-cp39-cp39-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:26b5927b5880b396231ab6190ee5c8fb47ed3f459b53504ed5419faaf16d3bfb"},
+    {file = "psycopg_binary-3.2.12-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ab02b7d138768fd6ac4230e45b073f7b9fd688d88c04f24c34df4a250a94d066"},
+    {file = "psycopg_binary-3.2.12-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:acb1811219a4144539f0baee224a11a2aa323a739c349799cf52f191eb87bc52"},
+    {file = "psycopg_binary-3.2.12-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:356b4266e5cde7b5bbcf232f549dedf7fbed4983daa556042bdec397780e044d"},
+    {file = "psycopg_binary-3.2.12-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:489b154891f1c995355adeb1077ee3479e9c9bada721b93270c20243bbad6542"},
+    {file = "psycopg_binary-3.2.12-cp39-cp39-win_amd64.whl", hash = "sha256:294f08b014f08dfd3c9b72408f5e1a0fd187bd86d7a85ead651e32dbd47aa038"},
+]
+
 [[package]]
 name = "ptyprocess"
 version = "0.7.0"
@@ -3213,6 +3440,29 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.14.1"
 
+[[package]]
+name = "pydantic-settings"
+version = "2.11.0"
+description = "Settings management using Pydantic"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "pydantic_settings-2.11.0-py3-none-any.whl", hash = "sha256:fe2cea3413b9530d10f3a5875adffb17ada5c1e1bab0b2885546d7310415207c"},
+    {file = "pydantic_settings-2.11.0.tar.gz", hash = "sha256:d0e87a1c7d33593beb7194adb8470fc426e95ba02af83a0f23474a04c9a08180"},
+]
+
+[package.dependencies]
+pydantic = ">=2.7.0"
+python-dotenv = ">=0.21.0"
+typing-inspection = ">=0.4.0"
+
+[package.extras]
+aws-secrets-manager = ["boto3 (>=1.35.0)", "boto3-stubs[secretsmanager]"]
+azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0)"]
+gcp-secret-manager = ["google-cloud-secret-manager (>=2.23.1)"]
+toml = ["tomli (>=2.0.1)"]
+yaml = ["pyyaml (>=6.0.1)"]
+
 [[package]]
 name = "pygments"
 version = "2.19.2"
@@ -3994,6 +4244,17 @@ test = ["catboost", "causalml", "gpboost", "lightgbm", "ngboost", "numpy (<2.0)"
 test-core = ["mypy", "pytest", "pytest-cov", "pytest-mpl"]
 test-notebooks = ["datasets", "jupyter", "keras", "nbconvert", "nbformat", "nlp", "transformers"]
 
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686"},
+    {file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"},
+]
+
 [[package]]
 name = "six"
 version = "1.17.0"
@@ -4303,6 +4564,24 @@ files = [
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
 test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
+[[package]]
+name = "typer-slim"
+version = "0.20.0"
+description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typer_slim-0.20.0-py3-none-any.whl", hash = "sha256:f42a9b7571a12b97dddf364745d29f12221865acef7a2680065f9bb29c7dc89d"},
+    {file = "typer_slim-0.20.0.tar.gz", hash = "sha256:9fc6607b3c6c20f5c33ea9590cbeb17848667c51feee27d9e314a579ab07d1a3"},
+]
+
+[package.dependencies]
+click = ">=8.0.0"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
+standard = ["rich (>=10.11.0)", "shellingham (>=1.3.0)"]
+
 [[package]]
 name = "typing-extensions"
 version = "4.15.0"
@@ -4491,4 +4770,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "c08e7c5ec4a113a5743f5cc2f688ae67f4254f5951f50f37d80606c3c3efd9f3"
+content-hash = "1b1a0392b846da9cf6def7d64ea811aabe4e7e66028e7655a520764a383a3346"

pyproject.toml

@@ -20,6 +20,13 @@ notebook = "^7.4.7"
 ipykernel = "^6.30.1"
 missingno = "^0.5.2"
 lightgbm = "^4.6.0"
+fastapi = "^0.121.1"
+pydantic = "^2.12.4"
+sqlalchemy = "^2.0.44"
+pydantic-settings = "^2.11.0"
+psycopg = {extras = ["binary"], version = "^3.2.12"}
+huggingface-hub = "^1.1.2"
+dotenv = "^0.9.9"
 
 
 [build-system]

src/config/db.py

@@ -0,0 +1,22 @@
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker, DeclarativeBase
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+class Settings(BaseSettings):
+    DATABASE_URL: str
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore",)
+
+settings = Settings()
+
+class Base(DeclarativeBase):
+    pass
+
+engine = create_engine(settings.DATABASE_URL, echo=True, future=True)
+SessionLocal = sessionmaker(bind=engine, autocommit=False, autoflush=False)
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()

src/controllers/home_controller.py

@@ -0,0 +1,88 @@
+from datetime import datetime
+from typing import List, Optional
+
+from fastapi import APIRouter, Depends, HTTPException, status
+from pydantic import BaseModel, Field
+from sqlalchemy.orm import Session
+
+from config.db import get_db
+from models.ml import MLModel
+
+router = APIRouter(tags=["Models"])
+
+
+class MLModelOut(BaseModel):
+    id: str = Field(..., description="Identifiant unique du modèle (UUID en chaîne).")
+    name: str = Field(..., description="Nom court du modèle.")
+    description: Optional[str] = Field(None, description="Description du modèle.")
+    created_at: Optional[datetime] = Field(
+        None, description="Date de création du modèle (UTC, ISO 8601)."
+    )
+    is_active: bool = Field(..., description="Modèle actif/inactif.")
+    model_config = {"json_schema_extra": {
+        "examples": [{
+            "id": "5b1c7b3a-0000-4000-8000-000000000002",
+            "name": "best_model",
+            "description": "XGB v1",
+            "created_at": "2025-09-15T10:11:03.950802+00:00",
+            "is_active": True
+        }]
+    }}
+
+
+@router.get(
+    "/",
+    response_model=List[MLModelOut],
+    status_code=status.HTTP_200_OK,
+    summary="Lister les modèles ML",
+    description=(
+        "Retourne la liste des modèles disponibles, triés du plus récent au plus ancien.\n\n"
+        "**Remarques**\n"
+        "- Les champs sont mappés depuis la table `ml_models`.\n"
+    ),
+    responses={
+        200: {
+            "description": "Liste des modèles.",
+            "content": {
+                "application/json": {
+                    "example": [
+                        {
+                            "id": "5b1c7b3a-0000-4000-8000-000000000002",
+                            "name": "best_model",
+                            "description": "XGB v1",
+                            "created_at": "2025-09-15T10:11:03.950802+00:00",
+                            "is_active": True
+                        },
+                        {
+                            "id": "5b1c7b3a-0000-4000-8000-000000000001",
+                            "name": "baseline",
+                            "description": "Baseline model",
+                            "created_at": "2025-09-15T10:11:03.950802+00:00",
+                            "is_active": True
+                        }
+                    ]
+                }
+            },
+        },
+        500: {"description": "Erreur serveur lors de la lecture des modèles."},
+    },
+)
+def list_ml_models(db: Session = Depends(get_db)) -> List[MLModelOut]:
+    try:
+        rows = (
+            db.query(MLModel)
+              .order_by(MLModel.created_at.desc())
+              .all()
+        )
+        return [
+            MLModelOut(
+                id=str(r.id),
+                name=r.name,
+                description=r.description,
+                created_at=r.created_at,
+                is_active=r.is_active,
+            )
+            for r in rows
+        ]
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

src/controllers/predict_controller.py

@@ -0,0 +1,163 @@
+from fastapi import APIRouter, Depends, HTTPException, Body, status
+
+from config.db import get_db
+from models.ml import MLModel 
+
+from models.ml_inputs import MLInput
+from models.ml_output import MLOutput
+
+import pandas as pd
+from model_loader import load_model           
+from features import compute_features
+from schemas.PredictItemResult import PredictItemResult
+from schemas.PredictResponse import PredictResponse
+from schemas.PredictRequest import PredictRequest
+from sqlalchemy.orm import Session
+
+router = APIRouter(prefix="/predict", tags=["Prédiction"])
+
+LABELS = {
+    "0": "reste_dans_l_entreprise",
+    "1": "parti_de_l_entreprise",
+}
+
+@router.post(
+    "/",
+    response_model=PredictResponse,
+    status_code=status.HTTP_200_OK,
+    summary="Prédire l’attrition d’un employé",
+    description=(
+        "Calcule la probabilité d’attrition pour chaque entrée fournie.\n\n"
+        "**Notes**\n"
+        "- `model_name` doit référencer un modèle *actif* en base (`MLModel`).\n"
+        "- Les données d’entrée sont persistées (`MLInput`) puis les sorties (`MLOutput`) sont enregistrées.\n"
+        "- En cas d’erreur de features ou de prédiction, la requête retourne **400**.\n"
+    ),
+    responses={
+        200: {"description": "Prédictions calculées avec succès."},
+        400: {"description": "Erreur pendant la préparation des features ou la prédiction."},
+        404: {"description": "Modèle introuvable ou inactif."},
+        500: {"description": "Impossible de charger le modèle/erreur serveur."},
+    },
+)
+def batch_predict(
+    payload: PredictRequest = Body(
+        ...,
+        examples={
+            "cas-minimal": {
+                "summary": "Exemple minimal",
+                "value": {
+                    "model_name": "best_model",
+                    "inputs": [
+                        {
+                            "id_employee": 123,
+                            "age": 35,
+                            "genre": "Homme",
+                            "revenu_mensuel": 4200
+                        }
+                    ],
+                },
+            },
+            "cas-complet": {
+                "summary": "Exemple complet",
+                "value": {
+                    "model_name": "best_model",
+                    "inputs": [
+                        {
+                            "id_employee": 123,
+                            "age": 35,
+                            "genre": "Homme",
+                            "revenu_mensuel": 4200,
+                            "statut_marital": "Célibataire",
+                            "departement": "Ventes",
+                            "poste": "Commercial",
+                            "nombre_experiences_precedentes": 2,
+                            "nombre_heures_travailless": 40,
+                            "annee_experience_totale": 5,
+                            "annees_dans_l_entreprise": 2,
+                            "annees_dans_le_poste_actuel": 1,
+                            "nombre_participation_pee": 1,
+                            "nb_formations_suivies": 3,
+                            "nombre_employee_sous_responsabilite": 0,
+                            "code_sondage": 7,
+                            "distance_domicile_travail": 12,
+                            "niveau_education": 3,
+                            "domaine_etude": "Marketing",
+                            "ayant_enfants": "Non",
+                            "frequence_deplacement": "Rarement",
+                            "annees_depuis_la_derniere_promotion": 0,
+                            "annes_sous_responsable_actuel": 1,
+                            "satisfaction_employee_environnement": 3,
+                            "note_evaluation_precedente": 4,
+                            "niveau_hierarchique_poste": 2,
+                            "satisfaction_employee_nature_travail": 3,
+                            "satisfaction_employee_equipe": 4,
+                            "satisfaction_employee_equilibre_pro_perso": 3,
+                            "eval_number": "E2",
+                            "note_evaluation_actuelle": 4,
+                            "heure_supplementaires": "Non",
+                            "augementation_salaire_precedente": 11
+                        }
+                    ],
+                },
+            },
+        },
+    ),
+    db: Session = Depends(get_db),
+):
+    row = (
+        db.query(MLModel)
+        .filter(MLModel.name == payload.model_name)
+        .first()
+    )
+
+    objs = [MLInput(**x.model_dump()) for x in payload.inputs]
+    db.add_all(objs)
+    db.commit()
+
+    if not row or getattr(row, "is_active", True) is False:
+        raise HTTPException(status_code=404, detail="Modèle introuvable ou inactif")
+
+    try:
+        m = load_model(payload.model_name)
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Chargement du modèle '{payload.model_name}' impossible: {e}",
+        )
+
+    try:
+        df = pd.DataFrame([x.model_dump() for x in payload.inputs])
+        X = compute_features(df)
+
+        results: list[PredictItemResult] = []
+
+        probas = m.predict_proba(X)
+        classes = getattr(m, "classes_", None)
+
+        for idx, p in enumerate(probas):
+            i = int(p.argmax())
+            key = str(classes[i]) if classes is not None else str(i)
+            label = LABELS.get(key, key)
+
+            pred = PredictItemResult(label=label, proba=float(p[i]))
+            results.append(pred)
+
+            db.add(
+                MLOutput(
+                    input_id=objs[idx].id,
+                    prediction=label,
+                    prob=float(p[i]),
+                )
+            )
+
+        db.commit()
+
+    except Exception as e:
+        db.rollback()
+        raise HTTPException(status_code=400, detail=f"Erreur pendant la prédiction: {e}")
+
+    return PredictResponse(
+        model_name=payload.model_name,
+        results=results,
+    )

src/features.py

@@ -0,0 +1,38 @@
+import numpy as np
+import pandas as pd
+
+def _safe_div(a, b):
+    a = pd.to_numeric(a, errors="coerce")
+    b = pd.to_numeric(b, errors="coerce").replace(0, np.nan)
+    return (a / b).fillna(0.0)
+
+def compute_features(df: pd.DataFrame) -> pd.DataFrame:
+    SAT_COLS = [
+        "satisfaction_employee_environnement",
+        "satisfaction_employee_nature_travail",
+        "satisfaction_employee_equipe",
+        "satisfaction_employee_equilibre_pro_perso",
+    ]
+
+    X = df.copy()
+    X["sat_mean"] = X[SAT_COLS].astype(float).mean(axis=1)
+    X["sat_std"]  = X[SAT_COLS].astype(float).std(axis=1, ddof=0)
+    X["delta_eval"] = (
+        X["note_evaluation_actuelle"].astype(float)
+        - X["note_evaluation_precedente"].astype(float)
+    )
+
+    X["ratio_post_stab"]   = _safe_div(X["annes_sous_responsable_actuel"], X["annees_dans_le_poste_actuel"])
+    X["revenu_par_niveau"] = _safe_div(X["revenu_mensuel"], X["niveau_hierarchique_poste"])
+
+    age_bins      = [-np.inf, 25, 35, 45, 60, np.inf]
+    dist_bins     = [-np.inf, 5, 10, 20, np.inf]
+    revenu_bins   = [-np.inf, 2500, 4000, 6000, np.inf]
+    sat_mean_bins = [-np.inf, 2.0, 3.0, 4.0, np.inf]
+
+    X["tranche_age"]      = pd.cut(X["age"].astype(float), age_bins, labels=["<=25","26-35","36-45","46-60","60+"])
+    X["tranche_distance"] = pd.cut(X["distance_domicile_travail"].astype(float), dist_bins, labels=["<=5","6-10","11-20",">20"])
+    X["tranche_revenu"]   = pd.cut(X["revenu_mensuel"].astype(float), revenu_bins, labels=["<=2.5k","2.5-4k","4-6k",">6k"])
+    X["tranche_sat_mean"] = pd.cut(X["sat_mean"], sat_mean_bins, labels=["basse","moyenne","bonne","excellente"])
+
+    return X

src/main.py

@@ -0,0 +1,18 @@
+
+from fastapi import FastAPI
+
+
+from controllers.home_controller import router as ml_home_router
+from controllers.predict_controller import router as predict_router
+
+
+app = FastAPI(title="ML API",
+    description="""
+API d’inférence pour la prédiction d’attrition.
+- **/predict**: prédire un résultat selon le modèle
+- **/models**: lister les modèles disponibles
+""", version="1.0.0")
+
+app.include_router(ml_home_router)
+
+app.include_router(predict_router)

src/model_loader.py

@@ -0,0 +1,35 @@
+import os
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Literal
+from huggingface_hub import hf_hub_download
+import joblib
+
+HF_REPO_ID  = os.getenv("HF_REPO_ID",  "Marintosti/attrition")
+HF_TOKEN    = os.getenv("HF_TOKEN")      
+
+ENV: Literal["dev", "test", "prod"] = os.getenv("APP_ENV", "dev").lower()
+ARTIFACTS_DIR = Path(os.getenv("ARTIFACTS_DIR", "artifacts"))
+
+def _load_local(name: str) -> Any:
+    path = ARTIFACTS_DIR / f"{name}.joblib"
+    if not path.exists():
+        raise FileNotFoundError(
+            f"Modèle local introuvable: {path}. "
+        )
+    return joblib.load(path)
+
+@lru_cache(maxsize=1)
+def load_model(name) -> Any:
+
+    if ENV in ("dev"):
+        return _load_local(name)
+     
+    hf_path = hf_hub_download(
+        repo_id=HF_REPO_ID,
+        filename=f"{name}.joblib",
+        token=HF_TOKEN,
+        local_files_only=False,
+    )
+
+    return joblib.load(hf_path)

src/schemas/ModelFeatures.py

@@ -0,0 +1,36 @@
+from pydantic import BaseModel
+
+class ModelFeatures(BaseModel):
+    id_employee: int
+    age: int
+    genre: str
+    revenu_mensuel: int
+    statut_marital: str
+    departement: str
+    poste: str
+    nombre_experiences_precedentes: int
+    nombre_heures_travailless: int
+    annee_experience_totale: int
+    annees_dans_l_entreprise: int
+    annees_dans_le_poste_actuel: int
+    nombre_participation_pee: int
+    nb_formations_suivies: int
+    nombre_employee_sous_responsabilite: int
+    code_sondage: int
+    distance_domicile_travail: int
+    niveau_education: int
+    domaine_etude: str
+    ayant_enfants: str
+    frequence_deplacement: str
+    annees_depuis_la_derniere_promotion: int
+    annes_sous_responsable_actuel: int
+    satisfaction_employee_environnement: int
+    note_evaluation_precedente: int
+    niveau_hierarchique_poste: int
+    satisfaction_employee_nature_travail: int
+    satisfaction_employee_equipe: int
+    satisfaction_employee_equilibre_pro_perso: int
+    eval_number: str
+    note_evaluation_actuelle: int
+    heure_supplementaires: str
+    augementation_salaire_precedente: int

src/schemas/PredictItemResult.py

@@ -0,0 +1,7 @@
+from typing import Optional
+from pydantic import BaseModel
+
+
+class PredictItemResult(BaseModel):
+    label: str
+    proba: Optional[float] = None

src/schemas/PredictRequest.py

@@ -0,0 +1,8 @@
+from typing import List
+from pydantic import BaseModel
+
+from schemas.ModelFeatures import ModelFeatures
+
+class PredictRequest(BaseModel):
+    model_name: str
+    inputs: List[ModelFeatures]

src/schemas/PredictResponse.py

@@ -0,0 +1,9 @@
+from typing import List
+from pydantic import BaseModel
+
+from schemas.PredictItemResult import PredictItemResult
+
+
+class PredictResponse(BaseModel):
+    model_name: str
+    results: List[PredictItemResult]

src/seeds/ml_models_seed.py

@@ -0,0 +1,38 @@
+import os
+from datetime import datetime, timezone
+from sqlalchemy import create_engine, text
+from sqlalchemy.orm import Session
+
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except Exception:
+    pass
+
+DATABASE_URL = os.environ["DATABASE_URL"]  
+engine = create_engine(DATABASE_URL, future=True)
+
+UPSERT = text("""
+    INSERT INTO ml_models (id, name, description, created_at, is_active)
+    VALUES (:id, :name, :description, :created_at, :is_active)
+    ON CONFLICT (name) DO UPDATE
+      SET description = EXCLUDED.description,
+          is_active  = EXCLUDED.is_active
+""")
+
+def seed_ml_models(session: Session):
+    rows = [
+        {"id": "5b1c7b3a-0000-4000-8000-000000000001", "name": "baseline",   "description": "Baseline model", "is_active": True},
+        {"id": "5b1c7b3a-0000-4000-8000-000000000002", "name": "best_model", "description": "Best model",        "is_active": False},
+    ]
+    now = datetime.now(timezone.utc)
+    for r in rows:
+        session.execute(UPSERT, {**r, "created_at": now})
+
+def main():
+    with Session(engine) as s:
+        seed_ml_models(s)
+        s.commit()
+
+if __name__ == "__main__":
+    main()

tests/functional/test_home.py

@@ -0,0 +1,92 @@
+from fastapi.testclient import TestClient
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+from main import app
+from config.db import get_db
+
+from models.ml import MLModel
+
+import uuid
+from datetime import datetime, timezone
+
+
+def test_list_models_simple(tmp_path):
+    db_path = tmp_path / "testing.db"
+    engine = create_engine(
+        f"sqlite:///{db_path}",
+        connect_args={"check_same_thread": False},
+        future=True,
+    )
+    SQLSession = sessionmaker(bind=engine, autoflush=False, autocommit=False, future=True)
+
+    MLModel.metadata.create_all(engine) 
+
+    session = SQLSession()
+
+    def get_db_override():
+        return session
+
+    app.dependency_overrides[get_db] = get_db_override
+
+    client = TestClient(app, raise_server_exceptions=False)
+
+    created = datetime(2025, 9, 15, 10, 11, 3, 950802, tzinfo=timezone.utc)
+    session.add_all(
+        [
+            MLModel(
+                id=uuid.UUID("5b1c7b3a-0000-4000-8000-000000000001"),
+                name="baseline",
+                description="Baseline model",
+                created_at=created,
+                is_active=True,
+            ),
+            MLModel(
+                id=uuid.UUID("5b1c7b3a-0000-4000-8000-000000000002"),
+                name="best_model",
+                description="XGB v1",
+                created_at=created,
+                is_active=True,
+            ),
+             MLModel(
+                id=uuid.UUID("5b1c7b3a-0000-4000-8000-000000000003"),
+                name="logistic_regression",
+                description="Logistic Regression",
+                created_at=created,
+                is_active=True,
+            ),
+        ]
+    )
+    session.commit()
+
+    resp = client.get("/")
+
+
+    app.dependency_overrides.clear()
+    session.close()
+
+    assert resp.status_code == 200
+    data = resp.json()
+    names = {row["name"] for row in data}
+    assert names == {"baseline", "best_model", 'logistic_regression'}
+
+
+def test_list_models_returns_500_when_db_fails():
+    class BrokenSession:
+        def query(self, *a, **kw):
+            raise RuntimeError("DB is down")
+
+    def get_db_override():
+        yield BrokenSession()
+
+    app.dependency_overrides[get_db] = get_db_override
+    client = TestClient(app, raise_server_exceptions=False)
+
+    resp = client.get("/")
+
+    app.dependency_overrides.clear()
+
+    assert resp.status_code == 500
+    body = resp.json()
+    assert "DB is down" in body["detail"]
+

tests/functional/test_predict.py

@@ -0,0 +1,364 @@
+from fastapi.testclient import TestClient
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+from main import app
+from config.db import get_db
+
+from models.ml import MLModel
+from models.ml_inputs import MLInput
+from models.ml_output import MLOutput
+
+import uuid
+from datetime import datetime, timezone
+
+
+def test_simple_predict(tmp_path):
+    db_path = tmp_path / "testing.db"
+    engine = create_engine(
+        f"sqlite:///{db_path}",
+        connect_args={"check_same_thread": False},
+        future=True,
+    )
+    SQLSession = sessionmaker(bind=engine, autoflush=False, autocommit=False, future=True)
+
+    MLModel.metadata.create_all(engine) 
+    MLInput.metadata.create_all(engine) 
+    MLOutput.metadata.create_all(engine) 
+
+    session = SQLSession()
+
+    def get_db_override():
+        return session
+
+    
+    app.dependency_overrides[get_db] = get_db_override
+
+    client = TestClient(app, raise_server_exceptions=False)
+
+    created = datetime(2025, 9, 15, 10, 11, 3, 950802, tzinfo=timezone.utc)
+    session.add_all(
+        [
+            MLModel(
+                id=uuid.UUID("5b1c7b3a-0000-4000-8000-000000000001"),
+                name="baseline",
+                description="Baseline model",
+                created_at=created,
+                is_active=True,
+            ),
+            MLModel(
+                id=uuid.UUID("5b1c7b3a-0000-4000-8000-000000000002"),
+                name="best_model",
+                description="XGB v1",
+                created_at=created,
+                is_active=True,
+            ),
+             MLModel(
+                id=uuid.UUID("5b1c7b3a-0000-4000-8000-000000000003"),
+                name="logistic_regression",
+                description="Logistic Regression",
+                created_at=created,
+                is_active=True,
+            ),
+        ]
+    )
+    session.commit()
+
+
+    payload = {
+        "model_name": "best_model",
+        "inputs": [{
+            "id_employee": 123,
+            "age": 35,
+            "genre": "Homme",
+            "revenu_mensuel": 4200,
+            "statut_marital": "Célibataire",
+            "departement": "Ventes",
+            "poste": "Commercial",
+            "nombre_experiences_precedentes": 2,
+            "nombre_heures_travailless": 40,
+            "annee_experience_totale": 5,
+            "annees_dans_l_entreprise": 2,
+            "annees_dans_le_poste_actuel": 1,
+            "nombre_participation_pee": 1,
+            "nb_formations_suivies": 3,
+            "nombre_employee_sous_responsabilite": 0,
+            "code_sondage": 7,
+            "distance_domicile_travail": 12,
+            "niveau_education": 3,
+            "domaine_etude": "Marketing",
+            "ayant_enfants": "Non",
+            "frequence_deplacement": "Rarement",
+            "annees_depuis_la_derniere_promotion": 0,
+            "annes_sous_responsable_actuel": 1,
+            "satisfaction_employee_environnement": 3,
+            "note_evaluation_precedente": 4,
+            "niveau_hierarchique_poste": 2,
+            "satisfaction_employee_nature_travail": 3,
+            "satisfaction_employee_equipe": 4,
+            "satisfaction_employee_equilibre_pro_perso": 3,
+            "eval_number": "E2",
+            "note_evaluation_actuelle": 4,
+            "heure_supplementaires": "Non",
+            "augementation_salaire_precedente": 11
+        }]
+    }
+
+    
+    resp = client.post("/predict", json=payload)
+
+    print("STATUS:", resp.status_code)
+    print("BODY:", resp.text)
+
+    app.dependency_overrides.clear()
+    session.close()
+
+    assert resp.status_code == 200
+    data = resp.json()
+    assert data["model_name"] == "best_model"
+    assert isinstance(data["results"], list)
+    assert len(data["results"]) == 1
+
+    result = data["results"][0]
+    assert result["label"] == "reste_dans_l_entreprise"
+    assert isinstance(result["proba"], float)
+    assert 0 <= result["proba"] <= 1
+
+
+def test_not_found_model(tmp_path):
+    db_path = tmp_path / "testing.db"
+    engine = create_engine(
+        f"sqlite:///{db_path}",
+        connect_args={"check_same_thread": False},
+        future=True,
+    )
+    SQLSession = sessionmaker(bind=engine, autoflush=False, autocommit=False, future=True)
+
+    MLModel.metadata.create_all(engine) 
+    MLInput.metadata.create_all(engine) 
+    MLOutput.metadata.create_all(engine) 
+
+    session = SQLSession()
+
+    def get_db_override():
+        return session
+
+    
+    app.dependency_overrides[get_db] = get_db_override
+
+    client = TestClient(app, raise_server_exceptions=False)
+
+    created = datetime(2025, 9, 15, 10, 11, 3, 950802, tzinfo=timezone.utc)
+    session.add_all(
+        [
+            MLModel(
+                id=uuid.UUID("5b1c7b3a-0000-4000-8000-000000000001"),
+                name="baseline",
+                description="Baseline model",
+                created_at=created,
+                is_active=True,
+            ),
+        ]
+    )
+    session.commit()
+
+
+    payload = {
+        "model_name": "best_model",
+        "inputs": [{
+            "id_employee": 123,
+            "age": 35,
+            "genre": "Homme",
+            "revenu_mensuel": 4200,
+            "statut_marital": "Célibataire",
+            "departement": "Ventes",
+            "poste": "Commercial",
+            "nombre_experiences_precedentes": 2,
+            "nombre_heures_travailless": 40,
+            "annee_experience_totale": 5,
+            "annees_dans_l_entreprise": 2,
+            "annees_dans_le_poste_actuel": 1,
+            "nombre_participation_pee": 1,
+            "nb_formations_suivies": 3,
+            "nombre_employee_sous_responsabilite": 0,
+            "code_sondage": 7,
+            "distance_domicile_travail": 12,
+            "niveau_education": 3,
+            "domaine_etude": "Marketing",
+            "ayant_enfants": "Non",
+            "frequence_deplacement": "Rarement",
+            "annees_depuis_la_derniere_promotion": 0,
+            "annes_sous_responsable_actuel": 1,
+            "satisfaction_employee_environnement": 3,
+            "note_evaluation_precedente": 4,
+            "niveau_hierarchique_poste": 2,
+            "satisfaction_employee_nature_travail": 3,
+            "satisfaction_employee_equipe": 4,
+            "satisfaction_employee_equilibre_pro_perso": 3,
+            "eval_number": "E2",
+            "note_evaluation_actuelle": 4,
+            "heure_supplementaires": "Non",
+            "augementation_salaire_precedente": 11
+        }]
+    }
+
+    
+    resp = client.post("/predict", json=payload)
+
+
+    app.dependency_overrides.clear()
+    session.close()
+
+    assert resp.status_code == 404
+    data = resp.json()
+    assert data["detail"] == "Modèle introuvable ou inactif"
+    
+
+def test_inactif_model(tmp_path):
+    db_path = tmp_path / "testing.db"
+    engine = create_engine(
+        f"sqlite:///{db_path}",
+        connect_args={"check_same_thread": False},
+        future=True,
+    )
+    SQLSession = sessionmaker(bind=engine, autoflush=False, autocommit=False, future=True)
+
+    MLModel.metadata.create_all(engine) 
+    MLInput.metadata.create_all(engine) 
+    MLOutput.metadata.create_all(engine) 
+
+    session = SQLSession()
+
+    def get_db_override():
+        return session
+
+    
+    app.dependency_overrides[get_db] = get_db_override
+
+    client = TestClient(app, raise_server_exceptions=False)
+
+    created = datetime(2025, 9, 15, 10, 11, 3, 950802, tzinfo=timezone.utc)
+    session.add_all(
+        [
+            MLModel(
+                id=uuid.UUID("5b1c7b3a-0000-4000-8000-000000000001"),
+                name="baseline",
+                description="Baseline model",
+                created_at=created,
+                is_active=False,
+            ),
+        ]
+    )
+    session.commit()
+
+
+    payload = {
+        "model_name": "baseline",
+        "inputs": [{
+            "id_employee": 123,
+            "age": 35,
+            "genre": "Homme",
+            "revenu_mensuel": 4200,
+            "statut_marital": "Célibataire",
+            "departement": "Ventes",
+            "poste": "Commercial",
+            "nombre_experiences_precedentes": 2,
+            "nombre_heures_travailless": 40,
+            "annee_experience_totale": 5,
+            "annees_dans_l_entreprise": 2,
+            "annees_dans_le_poste_actuel": 1,
+            "nombre_participation_pee": 1,
+            "nb_formations_suivies": 3,
+            "nombre_employee_sous_responsabilite": 0,
+            "code_sondage": 7,
+            "distance_domicile_travail": 12,
+            "niveau_education": 3,
+            "domaine_etude": "Marketing",
+            "ayant_enfants": "Non",
+            "frequence_deplacement": "Rarement",
+            "annees_depuis_la_derniere_promotion": 0,
+            "annes_sous_responsable_actuel": 1,
+            "satisfaction_employee_environnement": 3,
+            "note_evaluation_precedente": 4,
+            "niveau_hierarchique_poste": 2,
+            "satisfaction_employee_nature_travail": 3,
+            "satisfaction_employee_equipe": 4,
+            "satisfaction_employee_equilibre_pro_perso": 3,
+            "eval_number": "E2",
+            "note_evaluation_actuelle": 4,
+            "heure_supplementaires": "Non",
+            "augementation_salaire_precedente": 11
+        }]
+    }
+
+    
+    resp = client.post("/predict", json=payload)
+
+    print("STATUS:", resp.status_code)
+    print("BODY:", resp.text)
+
+    app.dependency_overrides.clear()
+    session.close()
+
+    assert resp.status_code == 404
+    data = resp.json()
+    assert data["detail"] == "Modèle introuvable ou inactif"
+    
+
+def test_list_models_returns_500_when_db_fails():
+    class BrokenSession:
+        def query(self, *a, **kw):
+            raise RuntimeError("DB is down")
+
+    def get_db_override():
+        yield BrokenSession()
+
+    app.dependency_overrides[get_db] = get_db_override
+    client = TestClient(app, raise_server_exceptions=False)
+
+    payload = {
+        "model_name": "baseline",
+        "inputs": [{
+            "id_employee": 123,
+            "age": 35,
+            "genre": "Homme",
+            "revenu_mensuel": 4200,
+            "statut_marital": "Célibataire",
+            "departement": "Ventes",
+            "poste": "Commercial",
+            "nombre_experiences_precedentes": 2,
+            "nombre_heures_travailless": 40,
+            "annee_experience_totale": 5,
+            "annees_dans_l_entreprise": 2,
+            "annees_dans_le_poste_actuel": 1,
+            "nombre_participation_pee": 1,
+            "nb_formations_suivies": 3,
+            "nombre_employee_sous_responsabilite": 0,
+            "code_sondage": 7,
+            "distance_domicile_travail": 12,
+            "niveau_education": 3,
+            "domaine_etude": "Marketing",
+            "ayant_enfants": "Non",
+            "frequence_deplacement": "Rarement",
+            "annees_depuis_la_derniere_promotion": 0,
+            "annes_sous_responsable_actuel": 1,
+            "satisfaction_employee_environnement": 3,
+            "note_evaluation_precedente": 4,
+            "niveau_hierarchique_poste": 2,
+            "satisfaction_employee_nature_travail": 3,
+            "satisfaction_employee_equipe": 4,
+            "satisfaction_employee_equilibre_pro_perso": 3,
+            "eval_number": "E2",
+            "note_evaluation_actuelle": 4,
+            "heure_supplementaires": "Non",
+            "augementation_salaire_precedente": 11
+        }]
+    }
+
+    
+    resp = client.post("/predict", json=payload)
+
+    app.dependency_overrides.clear()
+
+    assert resp.status_code == 500
+

tests/unit/test_features.py

@@ -0,0 +1,17 @@
+import pandas as pd
+import pytest
+from features import compute_features
+
+def test_compute_features_returns_matrix():
+    df = pd.DataFrame([{"age": 35, "genre": "Homme", "revenu_mensuel": 4200, 
+    "satisfaction_employee_environnement": 3,
+    "satisfaction_employee_nature_travail": 3,
+    "satisfaction_employee_equipe": 3,
+    "satisfaction_employee_equilibre_pro_perso": 3, "note_evaluation_actuelle": 2, 'note_evaluation_precedente' : 3, "annes_sous_responsable_actuel" : 2, "annees_dans_le_poste_actuel" : 4, "niveau_hierarchique_poste": 2, "distance_domicile_travail" : 5}])
+    X = compute_features(df)
+    assert hasattr(X, "shape")
+    assert X.shape[0] == 1 
+
+def test_compute_features_raises_on_empty():
+    with pytest.raises(Exception):
+        compute_features(pd.DataFrame())