ok

Dockerfile

@@ -47,4 +47,6 @@ ENV ARTIFACT_STORE_URI=$ARTIFACT_STORE_URI
 CMD mlflow server -p $PORT \
     --host 0.0.0.0 \
     --backend-store-uri $BACKEND_STORE_URI \
-    --default-artifact-root $ARTIFACT_STORE_URI
+    --default-artifact-root $ARTIFACT_STORE_URI \
+    --allowed-hosts $ML_SPACE \
+    --cors-allowed-origins $ML_SPACE

S3_user_accessKeys.csv

@@ -1,4 +0,0 @@
-Access key ID,Secret access key
-AKIA3RYUSMRNBV2EG4B6,ttC9RHfi3q2RJlA6zkZnOXCh90tICVVVLfuWUe11
-BACKEND_STORE_URI : psql 'postgresql://neondb_owner:npg_rmiFXnLj0A5C@ep-old-shadow-abmcscsp-pooler.eu-west-2.aws.neon.tech/neondb?sslmode=require&channel_binding=require'
-ARTIFACT_STORE_URI : s3://mlflow-bucket-36-yeah/mlfow/

app.py

@@ -1,7 +1,45 @@
-from fastapi import FastAPI
+import mlflow
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+import os
 
-app = FastAPI()
+# Load Iris dataset
+iris = load_iris()
 
-@app.get("/")
-def greet_json():
-    return {"Hello": "World!"}
+# Split dataset into X features and Target variable
+X = pd.DataFrame(data = iris["data"], columns= iris["feature_names"])
+y = pd.Series(data = iris["target"], name="target")
+
+# Split our training set and our test set 
+X_train, X_test, y_train, y_test = train_test_split(X, y)
+
+# Set your variables for your environment
+EXPERIMENT_NAME="iris-classification"
+
+# Set tracking URI to your Hugging Face application
+mlflow.set_tracking_uri("https://alvlt-test.hf.space")
+
+# Set experiment's info 
+mlflow.set_experiment(EXPERIMENT_NAME)
+
+# Get our experiment info
+experiment = mlflow.get_experiment_by_name(EXPERIMENT_NAME)
+
+# Call mlflow autolog
+mlflow.sklearn.autolog()
+
+# Instanciate and fit the model 
+lr = LogisticRegression()
+lr.fit(X_train.values, y_train.values)
+
+# Store metrics 
+predicted_qualities = lr.predict(X_test.values)
+accuracy = lr.score(X_test.values, y_test.values)
+
+# mlflow.sklearn.log_model(lr,"iris-log-reg")
+
+# Print results 
+print("LogisticRegression model")
+print("Accuracy: {}".format(accuracy))