Push changes ahead of the release (#175)

* Push changes ahead of the release

* Fix unit test

Author: safaricd

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "tabpfn-client"
-version = "0.2.5"
+version = "0.2.7"
 requires-python = ">=3.9"
 dynamic = ["dependencies", "optional-dependencies"]
 

tabpfn_client/server_config.yaml

@@ -5,7 +5,7 @@
 
 # production
 protocol: "https"
-host: "tabpfn-server-wjedmz7r5a-ez.a.run.app"
+host: "api.priorlabs.ai"
 port: "443"
 gui_url: "https://ux.priorlabs.ai"
 endpoints:

tabpfn_client/tests/integration/test_server_config.py

@@ -13,7 +13,7 @@ def setUp(self):
             self.config = yaml.safe_load(f)
 
     def test_host_configuration(self):
-        expected_host = "tabpfn-server-wjedmz7r5a-ez.a.run.app"
+        expected_host = "api.priorlabs.ai"
         self.assertEqual(
             self.config["host"], expected_host, f"Host should be {expected_host}"
         )

tabpfn_client/tests/quick_test_v1.py

@@ -0,0 +1,112 @@
+import os
+import json
+import pandas as pd
+import requests
+from typing import Optional
+
+from tabpfn_client.tabpfn_common_utils.utils import get_example_dataset
+
+TARGET_NAME = "target"
+
+
+def call_fit(train_path: str, target_name: str = TARGET_NAME, api_key: Optional[str] = None) -> str:
+    """
+    Call the /v1/fit endpoint to train a model.
+    
+    Args:
+        train_path: Path to the training CSV file
+        target_name: Name of the target column (default: "target")
+        api_key: API key for authentication (if None, reads from PRIORLABS_API_KEY env var)
+    
+    Returns:
+        model_id: The model ID returned from the fit endpoint
+    """
+    headers = {"Authorization": f"Bearer {api_key}"}
+    
+    payload = {
+        "task": "classification",
+        "schema": {
+            "target": target_name,
+            "description": "Iris dataset (quick test)",
+        },
+    }
+    files = {
+        "data": (None, json.dumps(payload), "application/json"),
+        "dataset_file": (train_path, open(train_path, "rb")),
+    }
+    response = requests.post(
+        "http://localhost:8000/v1/fit",
+        headers=headers,
+        files=files,
+    )
+    if response.status_code != 200:
+        raise RuntimeError(f"[FIT] HTTP {response.status_code}: {response.text}")
+    res_j = response.json()
+    model_id = res_j.get("model_id")
+    if not model_id:
+        raise RuntimeError(f"[FIT] No model_id in response: {res_j}")
+    print(f"✅ Model trained: {model_id}")
+    return model_id
+
+
+def call_predict(test_path: str, model_id: str, api_key: Optional[str] = None) -> None:
+    """
+    Call the /v1/predict endpoint to get predictions.
+    
+    Args:
+        test_path: Path to the test CSV file
+        model_id: The model ID from the fit call
+        api_key: API key for authentication (if None, reads from PRIORLABS_API_KEY env var)
+    """
+    headers = {"Authorization": f"Bearer {api_key}"}
+    
+    payload = {
+        "task": "classification",
+        "model_id": model_id,
+    }
+    files = {
+        "data": (None, json.dumps(payload), "application/json"),
+        "file": (test_path, open(test_path, "rb")),
+    }
+    response = requests.post(
+        "http://localhost:8000/v1/predict",
+        headers=headers,
+        files=files,
+    )
+    if response.status_code != 200:
+        raise RuntimeError(f"[PREDICT] HTTP {response.status_code}: {response.text}")
+    print("✅ Predictions:")
+    print(json.dumps(response.json(), indent=2))
+
+
+def main() -> None:
+    """Main function to generate dataset and test both endpoints."""
+    # === Generate/train/test data as in quick_test.py ===
+    x_train, x_test, y_train, y_test = get_example_dataset("iris")
+
+    train_df = x_train.copy()
+    train_df[TARGET_NAME] = y_train.values
+
+    test_df = x_test.copy()
+    test_df[TARGET_NAME] = y_test.values
+
+    train_path = "train.csv"
+    test_path = "test.csv"
+
+    train_df.to_csv(train_path, index=False)
+    test_df.to_csv(test_path, index=False)
+
+    # Get the API key
+    api_key = os.getenv("PRIORLABS_API_KEY")
+
+    #Test /v1/fit
+    print("--- Testing /v1/fit ---")
+    model_id = call_fit(train_path, api_key=api_key)
+
+    # Test /v1/predict
+    print("\n--- Testing /v1/predict ---")
+    call_predict(test_path, model_id, api_key=api_key)
+
+
+if __name__ == "__main__":
+    main()