gemini works as expected

simplemind/providers/gemini.py

@@ -4,7 +4,6 @@
 from functools import cached_property
 from typing import TYPE_CHECKING, Type, TypeVar
 
-import google.generativeai as genai
 import instructor
 from pydantic import BaseModel
 
@@ -30,12 +29,21 @@ class Gemini(BaseProvider):
         self.api_key = api_key or settings.get_api_key(PROVIDER_NAME)
         self.model_name = DEFAULT_MODEL
 
+    def set_model(self, model_name: str):
+        self.model_name = model_name
+
     @cached_property
-    def client(self, model_name: str = DEFAULT_MODEL):
+    def client(self):
         """The raw Gemini client."""
         if not self.api_key:
             raise ValueError("Gemini API key is required")
-        self.model_name = model_name
+        try:
+            import google.generativeai as genai
+        except ImportError as exc:
+            raise ImportError(
+                "Please install the `google-generativeai` package: `pip install google-generativeai`"
+            ) from exc
+        genai.configure(api_key=self.api_key)
         return genai.GenerativeModel(model_name=self.model_name)
 
     @cached_property
@@ -73,8 +81,7 @@ class Gemini(BaseProvider):
     @logger
     def structured_response(self, prompt: str, response_model: Type[T], **kwargs) -> T:
         """Send a structured response to the Gemini API."""
-        llm_model = kwargs.pop("llm_model", self.model_name)
-
+        kwargs.pop("llm_model")
         try:
             response = self.structured_client.chat.completions.create(
                 messages=[{"role": "user", "content": prompt}],
@@ -86,13 +93,12 @@ class Gemini(BaseProvider):
             raise RuntimeError(
                 f"Failed to send structured response to Gemini API: {e}"
             ) from e
-        return response
+        return response_model.model_validate(response)
 
     @logger
     def generate_text(self, prompt: str, **kwargs) -> str:
         """Generate text using the Gemini API."""
-        llm_model = kwargs.pop("llm_model", self.model_name)
-
+        kwargs.pop("llm_model")
         try:
             response = self.client.generate_content(prompt, **kwargs)
         except Exception as e: