Use global region for gemini 2.5 pro

cogs/ai_code_agent_cog.py

@@ -35,6 +35,7 @@ except ImportError:
         # Allow cog to load but genai_client will be None
 
 from tavily import TavilyClient
+from gurt.api import get_genai_client_for_model
 
 # Define standard safety settings using google.generativeai types
 # Set all thresholds to OFF as requested for internal tools
@@ -2432,7 +2433,8 @@ class AICodeAgentCog(commands.Cog):
                     # for i, item in enumerate(vertex_contents):
                     #     print(f"  History {i} Role: {item.role}, Parts: {item.parts}")
 
-                    response = await self.genai_client.aio.models.generate_content(
+                    client = get_genai_client_for_model(self._ai_model)
+                    response = await client.aio.models.generate_content(
                         model=f"publishers/google/models/{self._ai_model}",
                         contents=vertex_contents,
                         config=generation_config,  # Corrected parameter name

cogs/aimod_cog.py

@@ -30,6 +30,7 @@ from gurt.config import (
     PROJECT_ID,
     LOCATION,
 )  # Assuming gurt.config exists and has these
+from gurt.api import get_genai_client_for_model
 
 from . import aimod_config as aimod_config_module
 from .aimod_config import (
@@ -1727,7 +1728,8 @@ CRITICAL: Do NOT output anything other than the required JSON response.
                 # response_mime_type="application/json", # Consider if model supports this for forcing JSON
             )
 
-            response = await self.genai_client.aio.models.generate_content(
+            client = get_genai_client_for_model(model_id_to_use)
+            response = await client.aio.models.generate_content(
                 model=model_path,  # Correctly formatted model path
                 contents=request_contents,  # User's message with context and images
                 config=final_generation_config,  # Pass the config with system_instruction
@@ -2412,7 +2414,8 @@ CRITICAL: Do NOT output anything other than the required JSON response.
         )
 
         try:
-            response = await self.genai_client.aio.models.generate_content(
+            client = get_genai_client_for_model(APPEAL_AI_MODEL)
+            response = await client.aio.models.generate_content(
                 model=f"publishers/google/models/{APPEAL_AI_MODEL}",
                 contents=[
                     types.Content(role="user", parts=[types.Part(text=user_prompt)])

cogs/neru_teto_cog.py

@@ -27,6 +27,7 @@ from google.genai import types
 from google.api_core import exceptions as google_exceptions
 
 from gurt.config import PROJECT_ID, LOCATION
+from gurt.api import get_genai_client_for_model
 
 STANDARD_SAFETY_SETTINGS = [
     types.SafetySetting(
@@ -128,7 +129,8 @@ class DmbotTetoCog(commands.Cog):
         )
 
         try:
-            response = await self.genai_client.aio.models.generate_content(
+            client = get_genai_client_for_model(self._ai_model)
+            response = await client.aio.models.generate_content(
                 model=f"publishers/google/models/{self._ai_model}",
                 contents=contents,
                 config=generation_config,

cogs/rule34_cog.py

@@ -12,6 +12,7 @@ from google.api_core import exceptions as google_exceptions
 
 # Import project configuration for Vertex AI
 from gurt.config import PROJECT_ID, LOCATION
+from gurt.api import get_genai_client_for_model
 
 from .gelbooru_watcher_base_cog import GelbooruWatcherBaseCog
 
@@ -171,7 +172,8 @@ class Rule34Cog(GelbooruWatcherBaseCog):  # Removed name="Rule34"
             log.debug(
                 f"Rule34Cog: Sending to Vertex AI for tag transformation. Model: {self.tag_transformer_model}, Input: '{user_tags}'"
             )
-            response = await self.genai_client.aio.models.generate_content(
+            client = get_genai_client_for_model(self.tag_transformer_model)
+            response = await client.aio.models.generate_content(
                 model=f"publishers/google/models/{self.tag_transformer_model}",
                 contents=contents_for_api,
                 config=generation_config,

cogs/teto_cog.py

@@ -27,6 +27,7 @@ from google.api_core import exceptions as google_exceptions
 
 # Import project configuration for Vertex AI
 from gurt.config import PROJECT_ID, LOCATION
+from gurt.api import get_genai_client_for_model
 
 # Define standard safety settings using google.generativeai types
 # Set all thresholds to OFF as requested
@@ -527,7 +528,8 @@ class TetoCog(commands.Cog):
                 print(
                     f"[TETO DEBUG] Sending to Vertex AI. Model: {self._ai_model}, Tool Config: {vertex_tools is not None}"
                 )
-                response = await self.genai_client.aio.models.generate_content(
+                client = get_genai_client_for_model(self._ai_model)
+                response = await client.aio.models.generate_content(
                     model=f"publishers/google/models/{self._ai_model}",  # Use simpler model path
                     contents=final_contents_for_api,
                     config=generation_config_with_system,  # Pass the updated config

gurt/api.py

@@ -183,6 +183,7 @@ from .config import (
     PISTON_API_KEY,
     BASELINE_PERSONALITY,
     TENOR_API_KEY,  # Import other needed configs
+    PRO_MODEL_LOCATION,
 )
 from .prompt import build_dynamic_system_prompt
 from .context import (
@@ -385,24 +386,41 @@ def _format_embeds_for_prompt(embed_content: List[Dict[str, Any]]) -> Optional[s
     return "\n".join(formatted_strings) if formatted_strings else None
 
 
-# --- Initialize Google Generative AI Client for Vertex AI ---
+# --- Initialize Google Generative AI Clients for Vertex AI ---
-# No explicit genai.configure(api_key=...) needed when using Vertex AI backend
+# Separate clients for different regions so models can use their preferred location
 try:
-    genai_client = genai.Client(
+    genai_client_us_central1 = genai.Client(
         vertexai=True,
         project=PROJECT_ID,
-        location=LOCATION,
+        location="us-central1",
     )
+    genai_client_global = genai.Client(
+        vertexai=True,
+        project=PROJECT_ID,
+        location=PRO_MODEL_LOCATION,
+    )
+    # Default client remains us-central1 for backward compatibility
+    genai_client = genai_client_us_central1
 
-    print(
+    print("Google GenAI Clients initialized for us-central1 and global regions.")
-        f"Google GenAI Client initialized for Vertex AI project '{PROJECT_ID}' in location '{LOCATION}'."
-    )
 except NameError:
     genai_client = None
+    genai_client_us_central1 = None
+    genai_client_global = None
     print("Google GenAI SDK (genai) not imported, skipping client initialization.")
 except Exception as e:
     genai_client = None
-    print(f"Error initializing Google GenAI Client for Vertex AI: {e}")
+    genai_client_us_central1 = None
+    genai_client_global = None
+    print(f"Error initializing Google GenAI Clients for Vertex AI: {e}")
+
+
+def get_genai_client_for_model(model_name: str):
+    """Return the appropriate genai client based on the model name."""
+    if "gemini-2.5-pro-preview-06-05" in model_name:
+        return genai_client_global
+    return genai_client_us_central1
+
 
 # --- Constants ---
 # Define standard safety settings using google.generativeai types
@@ -452,8 +470,9 @@ async def call_google_genai_api_with_retry(
     Raises:
         Exception: If the API call fails after all retry attempts or encounters a non-retryable error.
     """
-    if not genai_client:
+    client = get_genai_client_for_model(model_name)
-        raise Exception("Google GenAI Client (genai_client) is not initialized.")
+    if not client:
+        raise Exception("Google GenAI Client is not initialized.")
 
     last_exception = None
     start_time = time.monotonic()
@@ -478,7 +497,7 @@ async def call_google_genai_api_with_retry(
 
             # Use the non-streaming async call - config now contains all settings
             # The 'model' parameter here should be the actual model name string
-            response = await genai_client.aio.models.generate_content(
+            response = await client.aio.models.generate_content(
                 model=model_name,  # Use the model_name string directly
                 contents=contents,
                 config=generation_config,  # Pass the combined config object
@@ -1060,8 +1079,8 @@ async def get_ai_response(
         - "error": An error message string if a critical error occurred, otherwise None.
         - "fallback_initial": Optional minimal response if initial parsing failed critically (less likely with controlled generation).
     """
-    if not PROJECT_ID or not LOCATION or not genai_client:  # Check genai_client too
+    if not PROJECT_ID:
-        error_msg = "Google Cloud Project ID/Location not configured or GenAI Client failed to initialize."
+        error_msg = "Google Cloud Project ID not configured."
         print(f"Error in get_ai_response: {error_msg}")
         return {"final_response": None, "error": error_msg}
 
@@ -2707,7 +2726,8 @@ async def generate_image_description(
     Returns:
         The AI-generated description string, or None if an error occurs.
     """
-    if not genai_client:
+    client = get_genai_client_for_model(EMOJI_STICKER_DESCRIPTION_MODEL)
+    if not client:
         print(
             "Error in generate_image_description: Google GenAI Client not initialized."
         )

gurt/config.py

@@ -11,6 +11,8 @@ load_dotenv()
 # --- API and Keys ---
 PROJECT_ID = os.getenv("GCP_PROJECT_ID", "1079377687568")
 LOCATION = os.getenv("GCP_LOCATION", "us-central1")
+# Specific region for Gemini 2.5 Pro model
+PRO_MODEL_LOCATION = "global"
 TAVILY_API_KEY = os.getenv("TAVILY_API_KEY", "")
 TENOR_API_KEY = os.getenv("TENOR_API_KEY", "")  # Added Tenor API Key
 PISTON_API_URL = os.getenv("PISTON_API_URL")  # For run_python_code tool

gurt/tools.py

@@ -41,6 +41,7 @@ from .config import (
     TAVILY_DEFAULT_MAX_RESULTS,
     TAVILY_DISABLE_ADVANCED,
 )
+from gurt.api import get_genai_client_for_model
 
 # Assume these helpers will be moved or are accessible via cog
 # We might need to pass 'cog' to these tool functions if they rely on cog state heavily
@@ -4544,15 +4545,10 @@ async def send_tenor_gif(
             for gif_data in gif_parts:
                 ai_content.append(gif_data["part"])
 
-            # Initialize AI client if needed
-            if not hasattr(cog, "genai_client") or not cog.genai_client:
-                cog.genai_client = genai.Client(
-                    vertexai=True, project=PROJECT_ID, location=LOCATION
-                )
-
             # Generate AI selection
             try:
-                response = await cog.genai_client.aio.models.generate_content(
+                client = get_genai_client_for_model(DEFAULT_MODEL)
+                response = await client.aio.models.generate_content(
                     model=DEFAULT_MODEL,
                     contents=[types.Content(role="user", parts=ai_content)],
                     config=types.GenerateContentConfig(