Ajouter openpyxl pour lire les fichiers Excel

api.py

@@ -0,0 +1,76 @@
+import mlflow 
+import uvicorn
+import pandas as pd 
+from pydantic import BaseModel
+from typing import Literal, List, Union
+from fastapi import FastAPI, File, UploadFile
+import joblib
+
+# Log model from mlflow 
+logged_model = 'runs:/.../model'
+
+# Load model as a PyFuncModel.
+loaded_model = mlflow.pyfunc.load_model(logged_model)
+
+tags_metadata = [
+    {
+        "name": "Machine Learning",
+        "description": "Prediction Endpoint."
+    }
+]
+
+app = FastAPI(
+    title="Car price prediction API",
+    openapi_tags=tags_metadata
+)
+
+class PredictionFeatures(BaseModel):
+    model_key: str
+    mileage: int
+    engine_power: int
+    fuel: str
+    car_type: str
+    private_parking_available: bool
+    has_gps: bool
+    has_air_conditioning: bool
+    automatic_car: bool
+    has_getaround_connect: bool
+    has_speed_regulator: bool
+    winter_tires: bool
+
+@app.get("/", tags=["Introduction Endpoints"])
+async def index():
+    """
+    Simply returns a welcome message!
+    """
+    message = "Hello world! This `/` is the most simple and default endpoint. If you want to learn more, check out documentation of the api at `/docs`"
+    return message
+
+
+@app.post("/predict", tags=["Machine Learning"])
+async def predict(predictionFeatures: PredictionFeatures):
+    # Read data 
+    input_data = pd.DataFrame({
+        "model_key": [predictionFeatures.model_key],
+        "mileage": [predictionFeatures.mileage],
+        "engine_power": [predictionFeatures.engine_power],
+        "fuel": [predictionFeatures.fuel],
+        "car_type": [predictionFeatures.car_type],
+        "private_parking_available": [predictionFeatures.private_parking_available],
+        "has_gps": [predictionFeatures.has_gps],
+        "has_air_conditioning": [predictionFeatures.has_air_conditioning],
+        "automatic_car": [predictionFeatures.automatic_car],
+        "has_getaround_connect": [predictionFeatures.has_getaround_connect],
+        "has_speed_regulator": [predictionFeatures.has_speed_regulator],
+        "winter_tires": [predictionFeatures.winter_tires]
+    })
+
+    prediction = loaded_model.predict(input_data)
+
+    # Format response
+    response = {"prediction": prediction.tolist()[0]}
+    return response
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)

app.py

@@ -3,7 +3,7 @@ import pandas as pd
 import plotly.express as px
 
 # Charger les données
-df = pd.read_csv('get_around_delay_analysis/rentals_data-Tableau 1.csv', sep=';')
+df = pd.read_excel('https://full-stack-assets.s3.eu-west-3.amazonaws.com/Deployment/get_around_delay_analysis.xlsx')
 
 # Rename col time_delta_with_previous_rental_in_minutes & delay_at_checkout_in_minutes
 df.rename(columns={'time_delta_with_previous_rental_in_minutes': 'delta'}, inplace=True)

git_push.sh

@@ -3,15 +3,16 @@
 # Vérifiez si un message de commit a été fourni
 if [ -z "$1" ]
 then
-  echo "Veuillez fournir un message de commit."
-  exit 1
+  COMMIT_MESSAGE="Debugging"
+else
+  COMMIT_MESSAGE="$1"
 fi
 
 # Ajouter tous les fichiers modifiés
 git add .
 
-# Commit avec le message fourni
-git commit -m "$1"
+# Commit avec le message fourni ou par défaut
+git commit -m "$COMMIT_MESSAGE"
 
 # Pousser les modifications vers le dépôt distant
 git push origin main

requirements.txt

@@ -1,7 +1,9 @@
 streamlit
-fastapi
-uvicorn
 pandas
 scikit-learn
-joblib
-mlflow
+mlflow
+plotly
+seaborn
+matplotlib
+boto3
+openpyxl

train.py

@@ -75,7 +75,11 @@ if __name__ == "__main__":
         # Print r2 score :
         model.score(X_test, y_test)
 
-        
+        # Enregistrer le run_id dans un fichier
+        run_id = run.info.run_id
+        with open("run_id.txt", "w") as f:
+            f.write(run_id)
+            
     print("...Done!")
     print("Saving model...")
     mlflow.sklearn.log_model(model, "model", signature=infer_signature(X_train, predictions))