Replace dict() and json() with model_dump() and model_dump_json(), re…

…spectively to avoid deprecation warnings.

tests/interface/test_rest.py

@@ -65,12 +65,12 @@ def documents(train_data):
 
 
 def test_return_http_200_for_predict(client, documents):
-    resp = client.post(PREDICT_ENDPOINT, json=documents.dict())
+    resp = client.post(PREDICT_ENDPOINT, json=documents.model_dump())
     assert resp.status_code == status.HTTP_200_OK
 
 
 def test_return_scores_for_predict(client, documents):
-    resp = client.post(PREDICT_ENDPOINT, json=documents.dict())
+    resp = client.post(PREDICT_ENDPOINT, json=documents.model_dump())
 
     expected_scores = QualityEstimation(
         scores=[
@@ -79,7 +79,7 @@ def test_return_scores_for_predict(client, documents):
             )
         ]
     )
-    assert resp.json() == json.loads(expected_scores.json())
+    assert resp.json() == json.loads(expected_scores.model_dump_json())
 
 
 def test_return_http_200_for_up(client):

tests/test_integration.py

@@ -92,13 +92,13 @@ def test_rest(train_data_file, model_path):
                     scores=[0.5, 1],
                 )
             ]
-        ).dict(),
+        ).model_dump(),
     )
 
     assert res.status_code == status.HTTP_200_OK
     # We can make following assumption due to the construction of train data
     assert res.json() == json.loads(
-        QualityEstimation(scores=[QualityScores(name=Metric.RECALL, scores=[1])]).json()
+        QualityEstimation(scores=[QualityScores(name=Metric.RECALL, scores=[1])]).model_dump_json()
     )