feat (api/orm) : first version api with orm

.github/workflows/deploy.yaml

@@ -21,6 +21,8 @@ jobs:
       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
@@ -55,6 +57,25 @@ jobs:
 
           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: |

.gitignore

@@ -219,4 +219,6 @@ __marimo__/
 *.joblib
 *.pkl
 *.pt
-*.onnx
+*.onnx
+
+/artifacts

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
+
+from alembic import context
+from sqlalchemy.ext.asyncio import create_async_engine, AsyncEngine
+from sqlalchemy import pool
+
+from src.config.db import Base, settings
+
+# Alembic Config
+config = context.config
+if config.config_file_name is not None:
+    fileConfig(config.config_file_name)
+
+target_metadata = Base.metadata
+
+def get_url() -> str:
+    return settings.DATABASE_URL
+
+def run_migrations_offline() -> None:
+    """Mode offline : pas d'engine, juste l'URL."""
+    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:
+    """Configuration commune (online) une fois connecté."""
+    context.configure(
+        connection=connection,
+        target_metadata=target_metadata,
+        compare_type=True,  
+    )
+    with context.begin_transaction():
+        context.run_migrations()
+
+async def run_migrations_online() -> None:
+    """Mode online : engine async."""
+    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/script.py.mako

@@ -0,0 +1,28 @@
+"""${message}
+
+Revision ID: ${up_revision}
+Revises: ${down_revision | comma,n}
+Create Date: ${create_date}
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+${imports if imports else ""}
+
+# revision identifiers, used by Alembic.
+revision: str = ${repr(up_revision)}
+down_revision: Union[str, Sequence[str], None] = ${repr(down_revision)}
+branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
+depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    ${upgrades if upgrades else "pass"}
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    ${downgrades if downgrades else "pass"}

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")
+

alembic/versions/ecd589af543e_ml_inputs.py

@@ -0,0 +1,74 @@
+"""ml inputs
+
+Revision ID: ecd589af543e
+Revises: 99e339b56253
+Create Date: 2025-09-15 13:47:50.220857
+
+"""
+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 = 'ecd589af543e'
+down_revision: Union[str, Sequence[str], None] = '99e339b56253'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "ml_inputs",
+        sa.Column("id", postgresql.UUID(as_uuid=True), primary_key=True, nullable=False),
+        sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("NOW()"), nullable=False),
+
+        sa.Column("id_employee", sa.Integer(), nullable=False),
+        sa.Column("age", sa.Integer(), nullable=False),
+
+        sa.Column("genre", sa.String(length=20), nullable=False),
+        sa.Column("revenu_mensuel", sa.Integer(), nullable=False),
+        sa.Column("statut_marital", sa.String(length=50), nullable=False),
+        sa.Column("departement", sa.String(length=100), nullable=False),
+        sa.Column("poste", sa.String(length=100), nullable=False),
+
+        sa.Column("nombre_experiences_precedentes", sa.Integer(), nullable=False),
+        sa.Column("nombre_heures_travailless", sa.Integer(), nullable=False),
+        sa.Column("annee_experience_totale", sa.Integer(), nullable=False),
+        sa.Column("annees_dans_l_entreprise", sa.Integer(), nullable=False),
+        sa.Column("annees_dans_le_poste_actuel", sa.Integer(), nullable=False),
+
+        sa.Column("nombre_participation_pee", sa.Integer(), nullable=False),
+        sa.Column("nb_formations_suivies", sa.Integer(), nullable=False),
+        sa.Column("nombre_employee_sous_responsabilite", sa.Integer(), nullable=False),
+
+        sa.Column("code_sondage", sa.Integer(), nullable=False),
+        sa.Column("distance_domicile_travail", sa.Integer(), nullable=False),
+        sa.Column("niveau_education", sa.Integer(), nullable=False),
+        sa.Column("domaine_etude", sa.String(length=100), nullable=False),
+
+        sa.Column("ayant_enfants", sa.String(length=10), nullable=False),
+        sa.Column("frequence_deplacement", sa.String(length=50), nullable=False),
+
+        sa.Column("annees_depuis_la_derniere_promotion", sa.Integer(), nullable=False),
+        sa.Column("annes_sous_responsable_actuel", sa.Integer(), nullable=False),
+
+        sa.Column("satisfaction_employee_environnement", sa.Integer(), nullable=False),
+        sa.Column("note_evaluation_precedente", sa.Integer(), nullable=False),
+        sa.Column("niveau_hierarchique_poste", sa.Integer(), nullable=False),
+        sa.Column("satisfaction_employee_nature_travail", sa.Integer(), nullable=False),
+        sa.Column("satisfaction_employee_equipe", sa.Integer(), nullable=False),
+        sa.Column("satisfaction_employee_equilibre_pro_perso", sa.Integer(), nullable=False),
+
+        sa.Column("eval_number", sa.String(length=50), nullable=False),
+        sa.Column("note_evaluation_actuelle", sa.Integer(), nullable=False),
+        sa.Column("heure_supplementaires", sa.String(length=10), nullable=False),
+        sa.Column("augementation_salaire_precedente", sa.Integer(), nullable=False),
+    )
+
+
+def downgrade() -> None:
+    op.drop_table("ml_inputs")
+

docker-compose.yml

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

poetry.lock

@@ -1,5 +1,24 @@
 # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
+[[package]]
+name = "alembic"
+version = "1.16.5"
+description = "A database migration tool for SQLAlchemy."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "alembic-1.16.5-py3-none-any.whl", hash = "sha256:e845dfe090c5ffa7b92593ae6687c5cb1a101e91fa53868497dbd79847f9dbe3"},
+    {file = "alembic-1.16.5.tar.gz", hash = "sha256:a88bb7f6e513bd4301ecf4c7f2206fe93f9913f9b48dac3b78babde2d6fe765e"},
+]
+
+[package.dependencies]
+Mako = "*"
+SQLAlchemy = ">=1.4.0"
+typing-extensions = ">=4.12"
+
+[package.extras]
+tz = ["tzdata"]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -30,6 +49,69 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 [package.extras]
 trio = ["trio (>=0.26.1)"]
 
+[[package]]
+name = "asyncpg"
+version = "0.30.0"
+description = "An asyncio PostgreSQL driver"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "asyncpg-0.30.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bfb4dd5ae0699bad2b233672c8fc5ccbd9ad24b89afded02341786887e37927e"},
+    {file = "asyncpg-0.30.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc1f62c792752a49f88b7e6f774c26077091b44caceb1983509edc18a2222ec0"},
+    {file = "asyncpg-0.30.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3152fef2e265c9c24eec4ee3d22b4f4d2703d30614b0b6753e9ed4115c8a146f"},
+    {file = "asyncpg-0.30.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c7255812ac85099a0e1ffb81b10dc477b9973345793776b128a23e60148dd1af"},
+    {file = "asyncpg-0.30.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:578445f09f45d1ad7abddbff2a3c7f7c291738fdae0abffbeb737d3fc3ab8b75"},
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+    {file = "asyncpg-0.30.0-cp310-cp310-win32.whl", hash = "sha256:aa403147d3e07a267ada2ae34dfc9324e67ccc4cdca35261c8c22792ba2b10cf"},
+    {file = "asyncpg-0.30.0-cp310-cp310-win_amd64.whl", hash = "sha256:fb622c94db4e13137c4c7f98834185049cc50ee01d8f657ef898b6407c7b9c50"},
+    {file = "asyncpg-0.30.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5e0511ad3dec5f6b4f7a9e063591d407eee66b88c14e2ea636f187da1dcfff6a"},
+    {file = "asyncpg-0.30.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:915aeb9f79316b43c3207363af12d0e6fd10776641a7de8a01212afd95bdf0ed"},
+    {file = "asyncpg-0.30.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1c198a00cce9506fcd0bf219a799f38ac7a237745e1d27f0e1f66d3707c84a5a"},
+    {file = "asyncpg-0.30.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3326e6d7381799e9735ca2ec9fd7be4d5fef5dcbc3cb555d8a463d8460607956"},
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+    {file = "asyncpg-0.30.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:bc6d84136f9c4d24d358f3b02be4b6ba358abd09f80737d1ac7c444f36108454"},
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+    {file = "asyncpg-0.30.0-cp311-cp311-win_amd64.whl", hash = "sha256:3356637f0bd830407b5597317b3cb3571387ae52ddc3bca6233682be88bbbc1f"},
+    {file = "asyncpg-0.30.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:c902a60b52e506d38d7e80e0dd5399f657220f24635fee368117b8b5fce1142e"},
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 [[package]]
 name = "certifi"
 version = "2025.8.3"
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 ]
 
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+    {file = "charset_normalizer-3.4.3-cp39-cp39-win_amd64.whl", hash = "sha256:d22dbedd33326a4a5190dd4fe9e9e693ef12160c77382d9e87919bce54f3d4ca"},
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+    {file = "charset_normalizer-3.4.3.tar.gz", hash = "sha256:6fce4b8500244f6fcb71465d4a4930d132ba9ab8e71a7859e6a5d59851068d14"},
+]
+
 [[package]]
 name = "click"
 version = "8.2.1"
@@ -289,6 +459,17 @@ uvicorn = {version = ">=0.15.0", extras = ["standard"]}
 [package.extras]
 standard = ["uvicorn[standard] (>=0.15.0)"]
 
+[[package]]
+name = "filelock"
+version = "3.19.1"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "filelock-3.19.1-py3-none-any.whl", hash = "sha256:d38e30481def20772f5baf097c122c3babc4fcdb7e14e57049eb9d88c6dc017d"},
+    {file = "filelock-3.19.1.tar.gz", hash = "sha256:66eda1888b0171c998b35be2bcc0f6d75c388a7ce20c3f3f37aa8e96c2dddf58"},
+]
+
 [[package]]
 name = "fonttools"
 version = "4.59.2"
@@ -369,6 +550,45 @@ type1 = ["xattr"]
 unicode = ["unicodedata2 (>=15.1.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
+[[package]]
+name = "fsspec"
+version = "2025.9.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "fsspec-2025.9.0-py3-none-any.whl", hash = "sha256:530dc2a2af60a414a832059574df4a6e10cce927f6f4a78209390fe38955cfb7"},
+    {file = "fsspec-2025.9.0.tar.gz", hash = "sha256:19fd429483d25d28b65ec68f9f4adc16c17ea2c7c7bf54ec61360d478fb19c19"},
+]
+
+[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 = "greenlet"
 version = "3.2.4"
@@ -447,6 +667,26 @@ files = [
     {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]
 
+[[package]]
+name = "hf-xet"
+version = "1.1.9"
+description = "Fast transfer of large files with the Hugging Face Hub."
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "hf_xet-1.1.9.tar.gz", hash = "sha256:c99073ce404462e909f1d5839b2d14a3827b8fe75ed8aed551ba6609c026c803"},
+]
+
+[package.extras]
+tests = ["pytest"]
+
 [[package]]
 name = "httpcore"
 version = "1.0.9"
@@ -547,6 +787,44 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
+[[package]]
+name = "huggingface-hub"
+version = "0.34.4"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+    {file = "huggingface_hub-0.34.4.tar.gz", hash = "sha256:a4228daa6fb001be3f4f4bdaf9a0db00e1739235702848df00885c9b5742c85c"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+hf-xet = {version = ">=1.1.3,<2.0.0", markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""}
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
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+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "libcst (>=1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.9.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+hf-xet = ["hf-xet (>=1.1.2,<2.0.0)"]
+inference = ["aiohttp"]
+mcp = ["aiohttp", "mcp (>=1.8.0)", "typer"]
+oauth = ["authlib (>=1.3.2)", "fastapi", "httpx", "itsdangerous"]
+quality = ["libcst (>=1.4.0)", "mypy (==1.15.0)", "mypy (>=1.14.1,<1.15.0)", "ruff (>=0.9.0)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "authlib (>=1.3.2)", "fastapi", "gradio (>=4.0.0)", "httpx", "itsdangerous", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors[torch]", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -767,6 +1045,25 @@ files = [
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 ]
 
+[[package]]
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+version = "1.3.10"
+description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+MarkupSafe = ">=0.9.2"
+
+[package.extras]
+babel = ["Babel"]
+lingua = ["lingua"]
+testing = ["pytest"]
+
 [[package]]
 name = "markdown-it-py"
 version = "4.0.0"
@@ -1290,6 +1587,83 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["coverage", "pytest", "pytest-benchmark"]
 
+[[package]]
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+version = "2.9.10"
+description = "psycopg2 - Python-PostgreSQL Database Adapter"
+optional = false
+python-versions = ">=3.8"
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+    {file = "psycopg2_binary-2.9.10-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:7f5d859928e635fa3ce3477704acee0f667b3a3d3e4bb109f2b18d4005f38287"},
+    {file = "psycopg2_binary-2.9.10-cp39-cp39-win32.whl", hash = "sha256:3216ccf953b3f267691c90c6fe742e45d890d8272326b4a8b20850a03d05b7b8"},
+    {file = "psycopg2_binary-2.9.10-cp39-cp39-win_amd64.whl", hash = "sha256:30e34c4e97964805f715206c7b789d54a78b70f3ff19fbe590104b71c45600e5"},
+]
+
 [[package]]
 name = "pydantic"
 version = "2.11.7"
@@ -1423,6 +1797,29 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
+[[package]]
+name = "pydantic-settings"
+version = "2.10.1"
+description = "Settings management using Pydantic"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "pydantic_settings-2.10.1-py3-none-any.whl", hash = "sha256:a60952460b99cf661dc25c29c0ef171721f98bfcb52ef8d9ea4c943d7c8cc796"},
+    {file = "pydantic_settings-2.10.1.tar.gz", hash = "sha256:06f0062169818d0f5524420a360d632d5857b83cffd4d42fe29597807a1614ee"},
+]
+
+[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"
@@ -1584,6 +1981,27 @@ files = [
     {file = "pyyaml-6.0.2.tar.gz", hash = "sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e"},
 ]
 
+[[package]]
+name = "requests"
+version = "2.32.5"
+description = "Python HTTP for Humans."
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
+    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+]
+
+[package.dependencies]
+certifi = ">=2017.4.17"
+charset_normalizer = ">=2,<4"
+idna = ">=2.5,<4"
+urllib3 = ">=1.21.1,<3"
+
+[package.extras]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
+use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+
 [[package]]
 name = "rich"
 version = "14.1.0"
@@ -2154,7 +2572,7 @@ files = [
 ]
 
 [package.dependencies]
-greenlet = {version = ">=1", markers = "python_version < \"3.14\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
+greenlet = {version = ">=1", optional = true, markers = "python_version < \"3.14\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\") or extra == \"asyncio\""}
 typing-extensions = ">=4.6.0"
 
 [package.extras]
@@ -2577,4 +2995,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "7ca2b5831a4e584d1f5c1a59aeeaf40b6a9333bbc81ae9e8221549e8878660b3"
+content-hash = "ae6861740d43c577f92198e978bd401f46311b2faddb8841e5d26b8163e1a78c"

pyproject.toml

@@ -17,7 +17,12 @@ scipy = "^1.16.1"
 scikit-learn = "^1.7.2"
 imbalanced-learn = "^0.14.0"
 shap = "^0.48.0"
-sqlalchemy = "^2.0.43"
+sqlalchemy = {extras = ["asyncio"], version = "^2.0.43"}
+huggingface-hub = "^0.34.4"
+alembic = "^1.16.5"
+asyncpg = "^0.30.0"
+psycopg2-binary = "^2.9.10"
+pydantic-settings = "^2.10.1"
 
 
 [tool.poetry.group.dev.dependencies]

space.yaml

@@ -1,8 +0,0 @@
-# space.yaml
-title: "Deploy ML API"
-emoji: "🚀"
-colorFrom: "blue"
-colorTo: "green"
-sdk: "docker"
-pinned: false
-license: "apache-2.0"

src/config/db.py

@@ -0,0 +1,15 @@
+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)

src/controllers/home_controller.py

@@ -0,0 +1,28 @@
+from fastapi import APIRouter, HTTPException
+from sqlalchemy.orm import Session
+from config.db import SessionLocal       
+from models.ml import MLModel           
+
+router = APIRouter() 
+
+@router.get("/", tags=["models"])
+def list_ml_models():
+    try:
+        with SessionLocal() as s:  
+            rows = (
+                s.query(MLModel)
+                 .order_by(MLModel.created_at.desc())
+                 .all()
+            )
+            return [
+                {
+                    "id": str(r.id),
+                    "name": r.name,
+                    "description": r.description,
+                    "created_at": r.created_at.isoformat() if r.created_at else None,
+                    "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 +1,72 @@
+# src/controllers/predict_controller.py
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from typing import Optional, List
+
+from sqlalchemy.orm import Session
+from config.db import SessionLocal
+from models.ml import MLModel 
+
+# Schemas
+from models.ml_inputs import MLInput
+from schemas.ModelFeatures import ModelFeatures
+
+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
+
+router = APIRouter(prefix="/predict", tags=["inference"])
+
+# (optionnel) mapping lisible des classes
+LABELS = {
+    "0": "reste_dans_l_entreprise",
+    "1": "parti_de_l_entreprise",
+}
+
+# --------- Route ----------
+@router.post("/", response_model=PredictResponse)
+def batch_predict(payload: PredictRequest):
+    with SessionLocal() as s:
+        row = (
+            s.query(MLModel)
+             .filter(MLModel.name == payload.model_name)
+             .first()
+        )
+
+        objs = [MLInput(**x.model_dump()) for x in payload.inputs]
+        s.add_all(objs)
+        s.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 p in probas:
+            i = int(p.argmax())
+            key = str(classes[i]) if classes is not None else str(i)
+            label = LABELS.get(key, key)
+            results.append(PredictItemResult(label=label, proba=float(p[i])))
+
+    except Exception as e:
+        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

@@ -1,81 +1,28 @@
 
-from typing import Any, Optional
-from fastapi import FastAPI, HTTPException
-import joblib
-import numpy as np
+from fastapi import FastAPI, Depends, HTTPException
 import pandas as pd
+from contextlib import asynccontextmanager
 
-from shema.ModelFeatures import ModelFeatures
+from config.db import SessionLocal
+from schemas.ModelFeatures import ModelFeatures
+from model_loader import load_model 
+from features import compute_features
 
-_model: Optional[Any] = None
+from sqlalchemy.orm import Session
 
-app = FastAPI()
-
-LABELS = {
-    "0": "reste_dans_l_entreprise",
-    "1": "parti_de_l_entreprise",
-}
-
-
-def load_model():
-    global _model
-    if _model is None:
-        _model = joblib.load('./artifacts/best_model.joblib')
-    return _model
-
-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()
+from controllers.home_controller import router as ml_home_router
+from controllers.predict_controller import router as predict_router
 
-    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
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+        
 
-@app.get("/")
-def read_root():
-    return {"Hello": "World"}
+app = FastAPI()
 
-@app.post("/predict", tags=["inference"])
-def predict(body: ModelFeatures):
-    try:
-        m = load_model()
-        raw = pd.DataFrame([body.model_dump()])
+app.include_router(ml_home_router)
 
-        X = compute_features(raw)  
-        proba = m.predict_proba(X)[0]
-        i = int(proba.argmax())
-        classes = getattr(m, "classes_", None)
-        pred = str(classes[i]) if classes is not None else str(i)
-        label = LABELS.get(pred, str(pred))
-        return {"label": label, "proba": float(proba[i])}
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=str(e))
+app.include_router(predict_router)

src/model_loader.py

@@ -0,0 +1,18 @@
+import os
+from functools import lru_cache
+from typing import Any
+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")      
+
+@lru_cache(maxsize=1)
+def load_model(name) -> Any:
+    local_path = hf_hub_download(
+        repo_id=HF_REPO_ID,
+        filename=f"{name}.joblib",
+        token=HF_TOKEN,
+        local_files_only=False,
+    )
+    return joblib.load(local_path)

src/models/base.py

@@ -0,0 +1,3 @@
+from sqlalchemy.orm import declarative_base
+
+Base = declarative_base()

src/models/ml.py

@@ -0,0 +1,23 @@
+import uuid
+from datetime import datetime
+from sqlalchemy import Column, String, Text, DateTime, Boolean
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.orm import Mapped, mapped_column
+
+
+from .base import Base
+
+
+class MLModel(Base):
+    __tablename__ = "ml_models"
+
+    id: Mapped[uuid.UUID] = mapped_column(
+        UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
+    )
+    name: Mapped[str] = mapped_column(String(100), nullable=False, unique=True)
+    description: Mapped[str | None] = mapped_column(Text, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=datetime.utcnow, nullable=False
+    )
+    is_active: Mapped[bool] = mapped_column(Boolean, default=True, nullable=False)
+

src/models/ml_inputs.py

@@ -0,0 +1,54 @@
+import uuid
+from datetime import datetime, timezone
+from sqlalchemy.orm import Mapped, mapped_column
+from sqlalchemy import String, Integer, Boolean, DateTime
+from sqlalchemy.dialects.postgresql import UUID
+from .base import Base 
+
+class MLInput(Base):
+    __tablename__ = "ml_inputs"
+
+    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True),
+                                                 default=lambda: datetime.now(timezone.utc), nullable=False)
+
+    id_employee: Mapped[int] = mapped_column(Integer, index=True)
+    age: Mapped[int] = mapped_column(Integer)
+
+    genre: Mapped[str] = mapped_column(String(20))
+    revenu_mensuel: Mapped[int] = mapped_column(Integer)
+    statut_marital: Mapped[str] = mapped_column(String(50))
+    departement: Mapped[str] = mapped_column(String(100), index=True)
+    poste: Mapped[str] = mapped_column(String(100))
+
+    nombre_experiences_precedentes: Mapped[int] = mapped_column(Integer)
+    nombre_heures_travailless: Mapped[int] = mapped_column(Integer)
+    annee_experience_totale: Mapped[int] = mapped_column(Integer)
+    annees_dans_l_entreprise: Mapped[int] = mapped_column(Integer)
+    annees_dans_le_poste_actuel: Mapped[int] = mapped_column(Integer)
+
+    nombre_participation_pee: Mapped[int] = mapped_column(Integer)
+    nb_formations_suivies: Mapped[int] = mapped_column(Integer)
+    nombre_employee_sous_responsabilite: Mapped[int] = mapped_column(Integer)
+
+    code_sondage: Mapped[int] = mapped_column(Integer)
+    distance_domicile_travail: Mapped[int] = mapped_column(Integer)
+    niveau_education: Mapped[int] = mapped_column(Integer)
+    domaine_etude: Mapped[str] = mapped_column(String(100))
+
+    ayant_enfants: Mapped[str] = mapped_column(String(10))
+    frequence_deplacement: Mapped[str] = mapped_column(String(50))
+
+    annees_depuis_la_derniere_promotion: Mapped[int] = mapped_column(Integer)
+    annes_sous_responsable_actuel: Mapped[int] = mapped_column(Integer)  
+    satisfaction_employee_environnement: Mapped[int] = mapped_column(Integer)
+    note_evaluation_precedente: Mapped[int] = mapped_column(Integer)
+    niveau_hierarchique_poste: Mapped[int] = mapped_column(Integer)
+    satisfaction_employee_nature_travail: Mapped[int] = mapped_column(Integer)
+    satisfaction_employee_equipe: Mapped[int] = mapped_column(Integer)
+    satisfaction_employee_equilibre_pro_perso: Mapped[int] = mapped_column(Integer)
+
+    eval_number: Mapped[str] = mapped_column(String(50))
+    note_evaluation_actuelle: Mapped[int] = mapped_column(Integer)
+    heure_supplementaires: Mapped[str] = mapped_column(String(10))  
+    augementation_salaire_precedente: Mapped[int] = mapped_column(Integer)  

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 +1,40 @@
+# src/seeds/ml_models_seed.py
+import os
+from datetime import datetime, timezone
+from sqlalchemy import create_engine, text
+from sqlalchemy.orm import Session
+
+# (optionnel) charge .env automatiquement
+try:
+    from dotenv import load_dotenv
+    load_dotenv()
+except Exception:
+    pass
+
+DATABASE_URL = os.environ["DATABASE_URL"]  # ex: postgresql+psycopg2://...
+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": "xgboost_v1", "description": "XGB v1",        "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()