Use file parameter

Jenkinsfile-predict-s444356

@@ -4,6 +4,9 @@ pipeline {
             image 's444409-create-dataset'
         }
     }
+    parameters {
+        file 'input_example.json'
+    }
     
     stages {
         stage('Get dataset from artifact') {

predict_s444356.py

@@ -6,7 +6,7 @@ logged_model = 'mlruns/1/296d6f314bb2451885fb7ae58988301e/artifacts/model'
 loaded_model = mlflow.pyfunc.load_model(logged_model)
 
 
-with open(f'{logged_model}/input_example.json') as f:
+with open('input_example.json') as f:
     data = json.load(f)
     input_example = np.array([data['inputs'][0]], dtype=np.float64).reshape(-1, 2)