Mise à jour des requêtes du web service de tarif

Dockerfile

@@ -18,3 +18,5 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 COPY --chown=user . /app
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+#CMD ["bash","-lc","fastapi run app.py --host 0.0.0.0 --port ${PORT}"]

Dockerfile-local

@@ -0,0 +1,24 @@
+FROM python:3.9
+
+ENV MLFLOW_EXPERIMENT_NAME=$MLFLOW_EXPERIMENT_NAME
+ENV MLFLOW_TRACKING_URI=$MLFLOW_TRACKING_URI
+ENV AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID
+ENV AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY
+ENV BACKEND_STORE_URI=$BACKEND_STORE_URI
+ENV ARTIFACT_ROOT=$ARTIFACT_ROOT
+
+ENV SPACE_URL=$SPACE_URL
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+#CMD ["bash","-lc","fastapi run app.py --host 0.0.0.0 --port ${PORT}"]

README.md

@@ -8,4 +8,10 @@ pinned: false
 license: mit
 short_description: Web service for Getaround app to deliver car rental pricing
 ---
+# Web service for Getaround app
 
+Bienvenue sur l'interface des web fonctions pour déterminer le prix de location de véhicule sur le projet Get Around de Jedha.
+
+La documentation pour l'API se trouve sous le lien ci-dessous :
+
+* [Web service for Getaround api](https://huggingface.co/spaces/pradelf/getaround-api/)

app.py

@@ -65,6 +65,7 @@ app = FastAPI(
 
 
 class RentalFeatures(BaseModel):
+    model_key: str
     model_key: str
     mileage: int
     engine_power: int
@@ -102,22 +103,23 @@ async def predict(predictionFeatures: RentalFeatures):
     Prediction of car daily rental cost for a given properties of a car !
     """
     # Read data
-    pf = [
-        predictionFeatures.model_key or "Peugeot",
-        predictionFeatures.mileage or 0,
-        predictionFeatures.engine_power or 0,
-        predictionFeatures.fuel or "petrol",
-        predictionFeatures.car_type or "sedan",
-        predictionFeatures.private_parking_available or 1,
-        predictionFeatures.has_gps or 0,
-        predictionFeatures.has_air_conditioning or 0,
-        predictionFeatures.automatic_car or 0,
-        predictionFeatures.has_getaround_connect or 0,
-        predictionFeatures.has_speed_regulator or 0,
-        predictionFeatures.winter_tires or 0,
-    ]
-
-    car_caracteristic = pd.DataFrame({"Car": pf})
+    # pf = [
+    #     predictionFeatures.model_key or "Peugeot",   
+    #     predictionFeatures.mileage or 0,
+    #     predictionFeatures.engine_power or 0,
+    #     predictionFeatures.fuel or "petrol",
+    #     predictionFeatures.car_type or "sedan",
+    #     predictionFeatures.private_parking_available or 1,
+    #     predictionFeatures.has_gps or 0,
+    #     predictionFeatures.has_air_conditioning or 0,
+    #     predictionFeatures.automatic_car or 0,
+    #     predictionFeatures.has_getaround_connect or 0,
+    #     predictionFeatures.has_speed_regulator or 0,
+    #     predictionFeatures.winter_tires or 0,
+    # ]
+
+    car_caracteristic = pd.DataFrame([predictionFeatures.model_dump()])
+    #car_caracteristic = pd.DataFrame({"Car": pf})
 
     # Log model from mlflow
     logged_model = "models:/getaround-price-prediction-model/versions/2"  # REPLACE WITH YOUR OWN RUN ID

build.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+set -e
+echo 'Build docker image for Get Around API.'
+source env.sh
+echo $IMAGE_ID
+docker build -f Dockerfile-local -t $IMAGE_ID --no-cache .

env.sh

@@ -0,0 +1,24 @@
+#!/bin/bash
+
+
+export MLFLOW_EXPERIMENT_NAME= 
+
+export MLFLOW_TRACKING_URI=
+
+export BACKEND_STORE_URI=
+
+export SPACE_URL = "https://pradelf-getaround-api.hf.space"
+
+export ARTIFACT_ROOT=
+
+export APP_NAME="getaround-api"
+
+export REPOSITORY_TAG_URI="$APP_NAME"
+
+export TAG="0.0.1"
+
+export TAGNAME="certification"
+
+export IMAGE_APP_ID="$REPOSITORY_TAG_URI:$TAGNAME"
+
+export $(grep -v '^#' .env | xargs -d '\n')

requirements.txt

@@ -1,7 +1,10 @@
-fastapi
+fastapi[standard]
 uvicorn[standard]
-pydantic
-mlflow
-pandas
-
-
+pydantic 
+typing 
+pandas 
+openpyxl 
+mlflow 
+scikit-learn
+python-multipart
+fsspec

run.sh

@@ -0,0 +1,10 @@
+docker run -it \
+-v "$(pwd):/home/app" \
+-p 7860:7860 \
+-e PORT=7860 \
+-e MLFLOW_TRACKING_URI=$MLFLOW_TRACKING_URI \
+-e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
+-e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
+-e BACKEND_STORE_URI=$BACKEND_STORE_URI \
+-e ARTIFACT_ROOT=$ARTIFACT_ROOT \
+api

secrets.sh

@@ -0,0 +1,4 @@
+export MLFLOW_EXPERIMENT_NAME="salary_estimator";
+export MLFLOW_TRACKING_URI="MLFLOW_TRACKING_URI";
+export BACKEND_STORE_URI="BACKEND_STORE_URI";
+export ARTIFACT_ROOT="ARTIFACT_ROOT";

test/test.py

@@ -0,0 +1,10 @@
+import requests
+payload = {
+  "title": "This is my great blog title",
+   "content": "This is the body of my article",
+   "Author": "Jaskier"
+}
+r = requests.post("http://localhost:7860/predict", json={
+  "YearsExperience": 0
+})
+print(r.content)