Modification du dockerfile et train.py

Dockerfile

@@ -14,13 +14,8 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 COPY --chown=user . /app
 
-ENV AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID
-ENV AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY
-ENV BACKEND_STORE_URI=$BACKEND_STORE_URI
-ENV ARTIFACT_STORE_URI=$ARTIFACT_STORE_URI
-
 CMD mlflow server -p $PORT \
     --host 0.0.0.0 \
     --allowed-hosts atomik31-mlflow.hf.space \
-    #--backend-store-uri $BACKEND_STORE_URI \
-    #--default-artifact-root $ARTIFACT_STORE_URI
+    --backend-store-uri $BACKEND_STORE_URI \
+    --default-artifact-root $ARTIFACT_STORE_URI

secrets.sh

@@ -1,5 +0,0 @@
-ARTIFACT_STORE_URI=s3://mlflow-server-dsfs38/
-AWS_ACCESS_KEY_ID=AKIA5TMZ4ZTAUA3NEDT2
-AWS_SECRET_ACCESS_KEY=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
-BACKEND_STORE_URI=postgresql://neondb_owner:npg_F9QsrpxhyXz1@ep-small-math-ag82ufcb-pooler.c-2.eu-central-1.aws.neon.tech/neondb?sslmode=require&channel_binding=require
-PORT=7860

train.py

@@ -1,17 +1,45 @@
-import random
-names = ["Jean", "Alice", "Bob", "Chloé", "David", "Marion", "Emma", "Farid", "Gina", "Hugo", "John", "Eva"]
-
-def people_groups(names, n, p):
-    random.shuffle(names)
-    
-    groups = []  # ✅ Renommé pour éviter la collision
-    index = 0
-    
-    for i in range(n):
-        group = names[index:index + p]
-        groups.append(group)  # ✅ Maintenant c'est clair
-        index += p
-    
-    return groups
-
-print(people_groups(names, n=4, p=3))
+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
+
+# Load Iris dataset
+iris = load_iris()
+
+# 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="my-first-mlflow-experiment-iris"
+
+# Set tracking URI to your Hugging Face application
+mlflow.set_tracking_uri("https://atomik31-mlflow.hf.space")
+
+# Set experiment's info
+mlflow.set_experiment(EXPERIMENT_NAME)
+
+# Get our experiment info
+experiment = mlflow.get_experiment_by_name(EXPERIMENT_NAME)
+
+# automatically log model info
+mlflow.sklearn.autolog()
+
+with mlflow.start_run(experiment_id = experiment.experiment_id):
+
+    # 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)
+
+    # Print results
+    print("LogisticRegression model")
+    print("Accuracy: {}".format(accuracy))