updated gemini code

llmebench/models/Gemini.py

@@ -6,6 +6,7 @@
 
 import vertexai
 import vertexai.preview.generative_models as generative_models
+from google.oauth2 import service_account
 from vertexai.generative_models import FinishReason, GenerativeModel, Part
 
 from llmebench.models.model_base import ModelBase
@@ -53,50 +54,75 @@ class GeminiModel(ModelBase):
     def __init__(
         self,
         project_id=None,
-        api_key=None,
         model_name=None,
+        location=None,
+        credentials_path=None,  # path to JSON file
+        credentials_info=None,  # dict or JSON string
         timeout=20,
         temperature=0,
+        tolerance=1e-7,
         top_p=0.95,
         max_tokens=2000,
         **kwargs,
     ):
-        # API parameters
-        # self.api_url = api_url or os.getenv("AZURE_DEPLOYMENT_API_URL")
-        self.api_key = api_key or os.getenv("GOOGLE_API_KEY")
         self.project_id = project_id or os.getenv("GOOGLE_PROJECT_ID")
         self.model_name = model_name or os.getenv("MODEL")
-        if self.api_key is None:
+        self.location = location or os.getenv("VERTEX_LOCATION") or "us-central1"
+        self.credentials = None
+
+        # 1. Prefer explicit credentials_info (dict or JSON string)
+        if credentials_info:
+            if isinstance(credentials_info, str):
+                credentials_info = json.loads(credentials_info)
+            self.credentials = service_account.Credentials.from_service_account_info(
+                credentials_info
+            )
+        # 2. Else, load from path (arg or env)
+        elif credentials_path or os.getenv("GOOGLE_APPLICATION_CREDENTIALS"):
+            path = credentials_path or os.getenv("GOOGLE_APPLICATION_CREDENTIALS")
+            with open(path, "r") as f:
+                info = json.load(f)
+            self.credentials = service_account.Credentials.from_service_account_info(
+                info
+            )
+        elif os.getenv("GOOGLE_APPLICATION_CREDENTIALS") is not None:
+            os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = os.getenv(
+                "GOOGLE_APPLICATION_CREDENTIALS"
+            )
+        # 3. Else, None: will fall back to ADC (Application Default Credentials)
+
+        if not self.project_id:
             raise Exception(
-                "API Key must be provided as model config or environment variable (`GOOGLE_API_KEY`)"
+                "PROJECT_ID must be set (argument or `GOOGLE_PROJECT_ID` in .env)"
             )
-        if self.project_id is None:
+        if not self.model_name:
+            raise Exception("MODEL must be set (argument or `MODEL` in .env)")
+        if not self.location:
             raise Exception(
-                "PROJECT_ID must be provided as model config or environment variable (`GOOGLE_PROJECT_ID`)"
+                "LOCATION must be set (argument or `VERTEX_LOCATION` in .env)"
             )
-        self.api_timeout = timeout
+
+        vertexai.init(
+            project=self.project_id,
+            location=self.location,
+            credentials=self.credentials,
+        )
+
+        self.tolerance = tolerance
+        self.temperature = max(temperature, tolerance)
+        self.top_p = top_p
+        self.max_tokens = max_tokens
+
         self.safety_settings = {
             generative_models.HarmCategory.HARM_CATEGORY_HATE_SPEECH: generative_models.HarmBlockThreshold.BLOCK_ONLY_HIGH,
             generative_models.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: generative_models.HarmBlockThreshold.BLOCK_ONLY_HIGH,
             generative_models.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: generative_models.HarmBlockThreshold.BLOCK_ONLY_HIGH,
             generative_models.HarmCategory.HARM_CATEGORY_HARASSMENT: generative_models.HarmBlockThreshold.BLOCK_ONLY_HIGH,
         }
-        # Parameters
-        tolerance = 1e-7
-        self.temperature = temperature
-        if self.temperature < tolerance:
-            # Currently, the model inference fails if temperature
-            # is exactly 0, so we nudge it slightly to work around
-            # the issue
-            self.temperature += tolerance
-        self.top_p = top_p
-        self.max_tokens = max_tokens
 
         super(GeminiModel, self).__init__(
             retry_exceptions=(TimeoutError, GeminiFailure), **kwargs
         )
-        vertexai.init(project=self.project_id, location="us-central1")
-        # self.client = GenerativeModel(self.model_name)
 
     def summarize_response(self, response):
         """Returns the "outputs" key's value, if available"""
@@ -127,20 +153,6 @@ def prompt(self, processed_input):
             This method raises this exception if the server responded with a non-ok
             response
         """
-        # headers = {
-        #     "Content-Type": "application/json",
-        #     "Authorization": "Bearer " + self.api_key,
-        # }
-        # body = {
-        #     "input_data": {
-        #         "input_string": processed_input,
-        #         "parameters": {
-        #             "max_tokens": self.max_tokens,
-        #             "temperature": self.temperature,
-        #             "top_p": self.top_p,
-        #         },
-        #     }
-        # }
         generation_config = {
             "max_output_tokens": self.max_tokens,
             "temperature": self.temperature,

tests/models/test_Gemini.py

@@ -53,18 +53,20 @@ class TestGeminiDepModelConfig(unittest.TestCase):
     def test_gemini_deployed_model_config(self):
         "Test if model config parameters passed as arguments are used"
         model = GeminiModel(
-            project_id="test_project_id", api_key="secret-key", model_name="gemini-test"
+            project_id="test_project_id",
+            model_name="gemini-test",
+            location="us-central1",
         )
 
         self.assertEqual(model.project_id, "test_project_id")
-        self.assertEqual(model.api_key, "secret-key")
+        self.assertEqual(model.location, "us-central1")
         self.assertEqual(model.model_name, "gemini-test")
 
     @patch.dict(
         "os.environ",
         {
             "GOOGLE_PROJECT_ID": "test_project_id",
-            "GOOGLE_API_KEY": "secret-key",
+            "LOCATION": "us-central1",
             "MODEL": "gemini-test",
         },
     )
@@ -73,23 +75,25 @@ def test_gemini_deployed_model_config_env_var(self):
         model = GeminiModel()
 
         self.assertEqual(model.project_id, "test_project_id")
-        self.assertEqual(model.api_key, "secret-key")
+        self.assertEqual(model.location, "us-central1")
         self.assertEqual(model.model_name, "gemini-test")
 
     @patch.dict(
         "os.environ",
         {
             "GOOGLE_PROJECT_ID": "test_project_id",
-            "GOOGLE_API_KEY": "secret-env-key",
+            "LOCATION": "us-central1",
             "MODEL": "gemini-test",
         },
     )
     def test_gemini_deployed_model_config_priority(self):
         "Test if model config parameters passed directly get priority"
         model = GeminiModel(
-            project_id="test_project_id", api_key="secret-key", model_name="gemini_test"
+            project_id="test_project_id",
+            model_name="gemini_test",
+            location="us-central1",
         )
 
         self.assertEqual(model.project_id, "test_project_id")
-        self.assertEqual(model.api_key, "secret-key")
+        self.assertEqual(model.location, "us-central1")
         self.assertEqual(model.model_name, "gemini_test")