update dockerfile, add predict.py, jenkinsfile_predict

Dockerfile

@@ -37,6 +37,7 @@ COPY Biblioteka_DL/dllib.py .
 COPY Biblioteka_DL/dllib-sacred.py .
 COPY Biblioteka_DL/evaluate.py .
 COPY Biblioteka_DL/dllib-mlflow.py .
+COPY predict.py .
 COPY Biblioteka_DL/all_games.csv .
 
 RUN ./download.sh $CUTOFF

Jenkinsfile_predict

@@ -0,0 +1,16 @@
+pipeline {
+    agent {
+        dockerfile {
+			 additionalBuildArgs "--build-arg KAGGLE_USERNAME=${params.KAGGLE_USERNAME} --build-arg KAGGLE_KEY=${params.KAGGLE_KEY} --build-arg CUTOFF=${params.CUTOFF} -t maciejczajka"
+		}
+    }
+    
+    stages {
+        stage('Script') {
+            steps {
+                copyArtifacts filter: '*', projectName: 's444409-training/master', selector: lastSuccessful()
+		sh "python predict.py"
+            }
+        }
+    }
+}

predict.py

@@ -0,0 +1,16 @@
+import json
+import mlflow
+import pandas as pd
+
+
+#logged_model = 'mlruns/1/d5b6f9c1784a4d2dbb8592cd4ad364d7/artifacts/model'
+logged_model = 'mlruns/1/fa3e620f03e64d888c364827907fb6f5/artifacts/s444409'
+loaded_model = mlflow.pyfunc.load_model(logged_model)
+
+
+with open(f'{logged_model}/input_example.json') as f:
+    data = json.load(f)
+    input_example = pd.DataFrame(data['inputs'][0])
+
+
+loaded_model.predict(input_example)