Send password in request body

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "tabpfn-client"
-version = "0.0.20"
+version = "0.0.21"
 requires-python = ">=3.10"
 dependencies = [
     "httpx>=0.24.1",

tabpfn_client/client.py

@@ -313,7 +313,7 @@ def register(
 
         response = self.httpx_client.post(
             self.server_endpoints.register.path,
-            params={
+            json={
                 "email": email,
                 "password": password,
                 "password_confirm": password_confirm,

tabpfn_client/estimator.py

@@ -181,6 +181,7 @@ def __init__(
         self.remove_outliers = remove_outliers
         self.add_fingerprint_features = add_fingerprint_features
         self.subsample_samples = subsample_samples
+        self.last_train_set_uid = None
 
     def _validate_targets_and_classes(self, y) -> np.ndarray:
         from sklearn.utils import column_or_1d
@@ -206,7 +207,7 @@ def fit(self, X, y):
                 ), "Only 'latest_tabpfn_hosted' model is supported at the moment for init(use_server=True)"
             except AssertionError as e:
                 print(e)
-            config.g_tabpfn_config.inference_handler.fit(X, y)
+            self.last_train_set_uid = config.g_tabpfn_config.inference_handler.fit(X, y)
             self.fitted_ = True
         else:
             raise NotImplementedError(
@@ -223,7 +224,10 @@ def predict(self, X):
     def predict_proba(self, X):
         check_is_fitted(self)
         return config.g_tabpfn_config.inference_handler.predict(
-            X, task="classification", config=self.get_params()
+            X,
+            task="classification",
+            train_set_uid=self.last_train_set_uid,
+            config=self.get_params(),
         )["probas"]
 
 
@@ -323,6 +327,7 @@ def __init__(
         self.cancel_nan_borders = cancel_nan_borders
         self.super_bar_dist_averaging = super_bar_dist_averaging
         self.subsample_samples = subsample_samples
+        self.last_train_set_uid = None
 
     def fit(self, X, y):
         # assert init() is called
@@ -335,7 +340,7 @@ def fit(self, X, y):
                 ), "Only 'latest_tabpfn_hosted' model is supported at the moment for init(use_server=True)"
             except AssertionError as e:
                 print(e)
-            config.g_tabpfn_config.inference_handler.fit(X, y)
+            self.last_train_set_uid = config.g_tabpfn_config.inference_handler.fit(X, y)
             self.fitted_ = True
         else:
             raise NotImplementedError(
@@ -357,5 +362,8 @@ def predict(self, X):
     def predict_full(self, X):
         check_is_fitted(self)
         return config.g_tabpfn_config.inference_handler.predict(
-            X, task="regression", config=self.get_params()
+            X,
+            task="regression",
+            train_set_uid=self.last_train_set_uid,
+            config=self.get_params(),
         )

tabpfn_client/service_wrapper.py

@@ -178,20 +178,25 @@ class InferenceClient(ServiceClientWrapper):
 
     def __init__(self, service_client=ServiceClient()):
         super().__init__(service_client)
-        self.last_train_set_uid = None
 
-    def fit(self, X, y) -> None:
+    def fit(self, X, y) -> str:
         if not self.service_client.is_initialized:
             raise RuntimeError(
                 "Dear TabPFN User, please initialize the client first by verifying your E-mail address sent to your registered E-mail account."
                 "Please Note: The email verification token expires in 30 minutes."
             )
 
-        self.last_train_set_uid = self.service_client.upload_train_set(X, y)
+        return self.service_client.upload_train_set(X, y)
 
-    def predict(self, X, task: Literal["classification", "regression"], config=None):
+    def predict(
+        self,
+        X,
+        task: Literal["classification", "regression"],
+        train_set_uid: str,
+        config=None,
+    ):
         return self.service_client.predict(
-            train_set_uid=self.last_train_set_uid,
+            train_set_uid=train_set_uid,
             x_test=X,
             tabpfn_config=config,
             task=task,