Pydantic Fix: Change deprecated function

uptrain/framework/base.py

@@ -141,7 +141,7 @@ def serialize(self, fpath: str | None = None):
         if fpath is None:
             fpath = os.path.join(self.logs_folder, "settings.json")
         with open(fpath, "w") as f:
-            jsondump(self.dict(), f)
+            jsondump(self.model_dump(), f)
 
     @classmethod
     def deserialize(cls, fpath: str):

uptrain/framework/evalllm.py

@@ -246,7 +246,7 @@ def evaluate(
             )
 
             if isinstance(m, ParametricEval):
-                dictm = m.dict()
+                dictm = m.model_dump()
                 dictm.update({"scenario_description": this_scenario_description})
                 ser_checks.append({"check_name": m.__class__.__name__, **dictm})
             elif isinstance(m, Evals):
@@ -321,7 +321,7 @@ def evaluate(
                     "data": results,
                     "checks": checks,
                     "metadata": metadata,
-                    "schema_dict": schema.dict(),
+                    "schema_dict": schema.model_dump(),
                     "project": project_name,
                 },
             )
@@ -345,8 +345,8 @@ def evaluate_on_server(self, data, ser_checks, schema):
                         data=data[i : i + BATCH_SIZE],
                         checks=ser_checks,
                         metadata={
-                            "schema": schema.dict(),
-                            "uptrain_settings": self.settings.dict(),
+                            "schema": schema.model_dump(),
+                            "uptrain_settings": self.settings.model_dump(),
                         },
                     )
                     break

uptrain/framework/remote.py

@@ -174,7 +174,7 @@ def add_checkset(self, name: str, checkset: CheckSet, settings: Settings):
         url = f"{self.base_url}/checkset"
         response = self.client.post(
             url,
-            json={"name": name, "config": checkset.dict(), "settings": settings.dict()},
+            json={"name": name, "config": checkset.dict(), "settings": settings.model_dump()},
         )
         return raise_or_return(response)
 
@@ -210,7 +210,7 @@ def add_experiment(
             json={
                 "name": name,
                 "config": modified_checkset.dict(),
-                "settings": settings.dict(),
+                "settings": settings.model_dump(),
             },
         )
         return raise_or_return(response)
@@ -422,10 +422,10 @@ def evaluate(
         results = []
 
         if params is not None:
-            params["uptrain_settings"] = self.settings.dict()
+            params["uptrain_settings"] = self.settings.model_dump()
         else:
             params = {}
-            params["uptrain_settings"] = self.settings.dict()
+            params["uptrain_settings"] = self.settings.model_dump()
 
         NUM_TRIES = 3
         for i in range(0, len(full_dataset), 100):
@@ -528,9 +528,9 @@ def perform_root_cause_analysis(
                             "rca_templates": ser_templates,
                             "metadata": {
                                 "project": project_name,
-                                "schema": schema.dict(),
+                                "schema": schema.model_dump(),
                                 **metadata,
-                                "uptrain_settings": self.settings.dict(),
+                                "uptrain_settings": self.settings.model_dump(),
                             },
                         },
                     )
@@ -645,7 +645,7 @@ def log_and_evaluate(
                 req_attrs.update([schema.question])
 
             if isinstance(m, ParametricEval):
-                dictm = m.dict()
+                dictm = m.model_dump()
                 dictm.update({"scenario_description": scenario_description})
                 ser_checks.append({"check_name": m.__class__.__name__, **dictm})
             elif isinstance(m, Evals):
@@ -676,9 +676,9 @@ def log_and_evaluate(
                             "checks": ser_checks,
                             "metadata": {
                                 "project": project_name,
-                                "schema": schema.dict(),
+                                "schema": schema.model_dump(),
                                 **metadata,
-                                "uptrain_settings": self.settings.dict(),
+                                "uptrain_settings": self.settings.model_dump(),
                             },
                         },
                     )

uptrain/operators/drift.py

@@ -143,9 +143,9 @@ def _check_params(cls, values):
 
     def setup(self):
         if self.algorithm == "DDM":
-            self._algo_obj = drift.binary.DDM(**self.params.dict())  # type: ignore
+            self._algo_obj = drift.binary.DDM(**self.params.model_dump())  # type: ignore
         elif self.algorithm == "ADWIN":
-            self._algo_obj = drift.ADWIN(**self.params.dict())  # type: ignore
+            self._algo_obj = drift.ADWIN(**self.params.model_dump())  # type: ignore
         self._counter = 0
         self._avg_accuracy = 0.0
         self._cuml_accuracy = 0.0

uptrain/utilities/__init__.py

@@ -54,7 +54,7 @@ def to_py_types(obj: t.Any) -> t.Any:
                 "params": obj.dict(include=set(obj.__fields__)),
             }
     elif isinstance(obj, BaseModel):
-        return obj.dict()
+        return obj.model_dump()
 
     # for numpy types
     if isinstance(obj, np.integer):