init commit

Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.10
+
+WORKDIR /home/app
+
+RUN apt-get update -y 
+RUN apt-get install nano unzip -y
+RUN apt install curl -y
+
+RUN curl -fsSL https://get.deta.dev/cli.sh | sh
+
+COPY requirements.txt /dependencies/requirements.txt
+RUN pip install -r /dependencies/requirements.txt
+
+COPY . /home/app
+
+CMD gunicorn app:app  --bind 0.0.0.0:$PORT --worker-class uvicorn.workers.UvicornWorker 

README.md

@@ -1,8 +1,8 @@
 ---
-title: Model Serv Api
-emoji: 🌖
-colorFrom: yellow
-colorTo: green
+title: Apimodelibm
+emoji: 📚
+colorFrom: blue
+colorTo: yellow
 sdk: docker
 pinned: false
 ---

app.py

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+import os
+import mlflow
+import pandas as pd
+import uvicorn
+import json
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from pydantic import BaseModel
+from typing import Literal, List, Union
+
+# -----------------------------------------------------------------------------
+# ENV + MLflow setup
+# -----------------------------------------------------------------------------
+# Sur Hugging Face, ces variables sont lues depuis les "Secrets"
+MLFLOW_TRACKING_URI = os.getenv("MLFLOW_TRACKING_URI")
+REGISTERED_MODEL_NAME = os.getenv("MLFLOW_REGISTERED_MODEL_NAME")
+MODEL_STAGE = os.getenv("MLFLOW_MODEL_STAGE")
+MODEL_ALIAS = os.getenv("MLFLOW_MODEL_ALIAS")
+
+# On force l'URI pour mlflow
+if MLFLOW_TRACKING_URI:
+    mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
+
+
+def build_model_uri() -> str:
+    if MODEL_ALIAS:
+        return f"models:/{REGISTERED_MODEL_NAME}@{MODEL_ALIAS}"
+    return f"models:/{REGISTERED_MODEL_NAME}/{MODEL_STAGE or 'Production'}"
+    # models:/ibm_attrition_detector@production
+
+
+MODEL_URI = build_model_uri()
+MODEL = None
+
+# -----------------------------------------------------------------------------
+# FastAPI Setup
+# -----------------------------------------------------------------------------
+app = FastAPI(title="👨‍💼 HR Prediction API")
+
+
+class PredictionFeatures(BaseModel):
+    Age: Union[int, float]
+    BusinessTravel: str
+    DailyRate: Union[int, float]
+    Department: str
+    DistanceFromHome: Union[int, float]
+    Education: Union[int, float]
+    EducationField: str
+    EmployeeCount: Union[int, float]
+    EmployeeNumber: Union[int, float]
+    EnvironmentSatisfaction: Union[int, float]
+    Gender: str
+    HourlyRate: Union[int, float]
+    JobInvolvement: Union[int, float]
+    JobLevel: Union[int, float]
+    JobRole: str
+    JobSatisfaction: Union[int, float]
+    MaritalStatus: str
+    MonthlyIncome: Union[int, float]
+    MonthlyRate: Union[int, float]
+    NumCompaniesWorked: Union[int, float]
+    Over18: str
+    OverTime: str
+    PercentSalaryHike: Union[int, float]
+    PerformanceRating: Union[int, float]
+    RelationshipSatisfaction: Union[int, float]
+    StandardHours: Union[int, float]
+    StockOptionLevel: Union[int, float]
+    TotalWorkingYears: Union[int, float]
+    TrainingTimesLastYear: Union[int, float]
+    WorkLifeBalance: Union[int, float]
+    YearsAtCompany: Union[int, float]
+    YearsInCurrentRole: Union[int, float]
+    YearsSinceLastPromotion: Union[int, float]
+    YearsWithCurrManager: Union[int, float]
+
+
+# -----------------------------------------------------------------------------
+# Startup: CHARGEMENT BLOQUANT (Solution au bug 500)
+# -----------------------------------------------------------------------------
+@app.on_event("startup")
+def load_model_sync():
+    global MODEL
+    print(f"🚀 [INFO] Attempting to load model: {MODEL_URI}")
+    try:
+        # On attend que le chargement soit fini avant de rendre l'API disponible
+        MODEL = mlflow.sklearn.load_model(MODEL_URI)
+        # models:/ibm_attrition_detector@production
+        print("✅ [INFO] Model loaded successfully!")
+    except Exception as e:
+        print(f"❌ [ERROR] Failed to load model: {e}")
+        # En cas d'échec, on laisse MODEL à None pour que /health le signale
+
+
+# -----------------------------------------------------------------------------
+# Endpoints
+# -----------------------------------------------------------------------------
+@app.get("/health")
+def health():
+    return {
+        "status": "ok",
+        "model_uri": MODEL_URI,
+        "model_loaded": MODEL is not None,
+    }
+
+
+@app.post("/predict")
+async def predict(payload: PredictionFeatures):
+    if MODEL is None:
+        raise HTTPException(
+            status_code=503, detail="Model is still loading or failed to load."
+        )
+
+    # Conversion pydantic -> dict -> DataFrame
+    df = pd.DataFrame([payload.dict()])
+    pred = MODEL.predict(df)
+    return {"prediction": int(pred[0])}
+
+
+if __name__ == "__main__":
+    # Port 7860 est le standard pour Hugging Face Spaces
+    uvicorn.run(app, host="0.0.0.0", port=int(os.getenv("PORT", 7860)))

requirements.txt

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+mlflow==2.21.3
+scikit-learn==1.4.2
+requests>=2.31.0,<3
+fastapi 
+uvicorn[standard]
+pydantic>=2.0
+email-validator>=2.0
+typing 
+pandas 
+gunicorn 
+openpyxl 
+boto3
+python-multipart
+dotenv
+python-dotenv