update gemini to poe

anycoder_app/config.py

@@ -48,9 +48,9 @@ _fastrtc_docs_last_fetched: Optional[datetime] = None
 # Available Models Configuration
 AVAILABLE_MODELS = [
     {
-        "name": "Gemini 3 Pro Preview",
-        "id": "gemini-3-pro-preview",
-        "description": "Google Gemini 3 Pro Preview with deep thinking, Google Search integration, and advanced reasoning"
+        "name": "Gemini 3.0 Pro",
+        "id": "gemini-3.0-pro",
+        "description": "Google Gemini 3.0 Pro via Poe with advanced reasoning"
     },
     {
         "name": "DeepSeek V3.2-Exp",

anycoder_app/deploy.py

@@ -178,7 +178,7 @@ def _generation_code_impl(query: Optional[str], _setting: Dict[str, str], _histo
     if has_existing_content and query.strip():
         # Skip search/replace for models that use native clients (non-OpenAI-compatible)
         # These models need the full generation flow to work properly
-        native_client_models = ["gemini-3-pro-preview"]
+        native_client_models = []  # All models now use OpenAI-compatible APIs
         
         if _current_model['id'] not in native_client_models:
             try:
@@ -463,42 +463,8 @@ Generate the exact search/replace blocks needed to make these changes."""
     
     messages.append({'role': 'user', 'content': enhanced_query})
     try:
-        # Handle Gemini 3 Pro Preview with native SDK
-        if _current_model["id"] == "gemini-3-pro-preview":
-            # Convert messages to Gemini format
-            from google.genai import types
-            contents = []
-            for msg in messages:
-                if msg['role'] != 'system':  # Gemini doesn't use system role the same way
-                    contents.append(
-                        types.Content(
-                            role="user" if msg['role'] == 'user' else "model",
-                            parts=[types.Part.from_text(text=msg['content'])]
-                        )
-                    )
-            
-            # Add system prompt as first user message if exists
-            if messages and messages[0]['role'] == 'system':
-                system_content = messages[0]['content']
-                contents.insert(0, types.Content(
-                    role="user",
-                    parts=[types.Part.from_text(text=f"System instructions: {system_content}")]
-                ))
-            
-            tools = [types.Tool(googleSearch=types.GoogleSearch())]
-            generate_content_config = types.GenerateContentConfig(
-                thinkingConfig=types.ThinkingConfig(thinkingLevel="HIGH"),
-                tools=tools,
-                max_output_tokens=16384
-            )
-            
-            completion = client.models.generate_content_stream(
-                model="gemini-3-pro-preview",
-                contents=contents,
-                config=generate_content_config,
-            )
         # Handle Mistral API method difference
-        elif _current_model["id"] in ("codestral-2508", "mistral-medium-2508"):
+        if _current_model["id"] in ("codestral-2508", "mistral-medium-2508"):
             completion = client.chat.stream(
                 model=get_real_model_id(_current_model["id"]),
                 messages=messages,
@@ -556,11 +522,7 @@ Generate the exact search/replace blocks needed to make these changes."""
         for chunk in completion:
             # Handle different response formats for Mistral vs others
             chunk_content = None
-            if _current_model["id"] == "gemini-3-pro-preview":
-                # Gemini native SDK format: chunk.text
-                if hasattr(chunk, 'text') and chunk.text:
-                    chunk_content = chunk.text
-            elif _current_model["id"] in ("codestral-2508", "mistral-medium-2508"):
+            if _current_model["id"] in ("codestral-2508", "mistral-medium-2508"):
                 # Mistral format: chunk.data.choices[0].delta.content
                 if (
                     hasattr(chunk, "data") and chunk.data and
@@ -2338,7 +2300,7 @@ def _fetch_inference_provider_code(model_id: str) -> Optional[str]:
     """
     # Skip non-HuggingFace models (external APIs)
     non_hf_models = [
-        "gemini-3-pro-preview", "gemini-2.5-flash", "gemini-2.5-pro",
+        "gemini-3.0-pro", "gemini-2.5-flash", "gemini-2.5-pro",
         "gemini-flash-latest", "gemini-flash-lite-latest",
         "gpt-5", "gpt-5.1", "gpt-5.1-instant", "gpt-5.1-codex", "gpt-5.1-codex-mini",
         "grok-4", "Grok-Code-Fast-1",
@@ -2442,7 +2404,7 @@ def import_model_from_hf(model_id: str, prefer_local: bool = False) -> Tuple[str
     
     # Skip non-HuggingFace models (external APIs) - these are not importable
     non_hf_models = [
-        "gemini-3-pro-preview", "gemini-2.5-flash", "gemini-2.5-pro",
+        "gemini-3.0-pro", "gemini-2.5-flash", "gemini-2.5-pro",
         "gemini-flash-latest", "gemini-flash-lite-latest",
         "gpt-5", "gpt-5.1", "gpt-5.1-instant", "gpt-5.1-codex", "gpt-5.1-codex-mini",
         "grok-4", "Grok-Code-Fast-1",

anycoder_app/models.py

@@ -22,11 +22,11 @@ Messages = List[Dict[str, str]]
 
 def get_inference_client(model_id, provider="auto"):
     """Return an InferenceClient with provider based on model_id and user selection."""
-    if model_id == "gemini-3-pro-preview":
-        # Use native Google GenAI client for Gemini 3 Pro Preview with v1alpha API
-        return genai.Client(
-            api_key=os.getenv("GEMINI_API_KEY"),
-            http_options={'api_version': 'v1alpha'}
+    if model_id == "gemini-3.0-pro":
+        # Use Poe (OpenAI-compatible) client for Gemini 3.0 Pro
+        return OpenAI(
+            api_key=os.getenv("POE_API_KEY"),
+            base_url="https://api.poe.com/v1"
         )
     elif model_id == "qwen3-30b-a3b-instruct-2507":
         # Use DashScope OpenAI client

backend_api.py

@@ -62,7 +62,7 @@ print("[Startup] System prompts initialization complete")
 
 # Define models and languages here to avoid importing Gradio UI
 AVAILABLE_MODELS = [
-    {"name": "Gemini 3 Pro Preview", "id": "gemini-3-pro-preview", "description": "Google Gemini 3 Pro Preview with deep thinking, Google Search integration, and advanced reasoning"},
+    {"name": "Gemini 3.0 Pro", "id": "gemini-3.0-pro", "description": "Google Gemini 3.0 Pro via Poe with advanced reasoning"},
     {"name": "Sherlock Dash Alpha", "id": "openrouter/sherlock-dash-alpha", "description": "Sherlock Dash Alpha model via OpenRouter"},
     {"name": "MiniMax M2", "id": "MiniMaxAI/MiniMax-M2", "description": "MiniMax M2 model via HuggingFace InferenceClient with Novita provider"},
     {"name": "DeepSeek V3.2-Exp", "id": "deepseek-ai/DeepSeek-V3.2-Exp", "description": "DeepSeek V3.2 Experimental via HuggingFace"},
@@ -112,7 +112,7 @@ user_sessions = {}
 class CodeGenerationRequest(BaseModel):
     query: str
     language: str = "html"
-    model_id: str = "gemini-3-pro-preview"
+    model_id: str = "gemini-3.0-pro"
     provider: str = "auto"
     history: List[List[str]] = []
     agent_mode: bool = False
@@ -389,19 +389,8 @@ async def generate_code(
             
             # Stream the response
             try:
-                # Handle Gemini 3 Pro Preview with native SDK
-                if selected_model_id == "gemini-3-pro-preview":
-                    print("[Generate] Using Gemini 3 native SDK")
-                    contents, config = create_gemini3_messages(messages)
-                    
-                    stream = client.models.generate_content_stream(
-                        model="gemini-3-pro-preview",
-                        contents=contents,
-                        config=config,
-                    )
-                
                 # Handle Mistral models with different API
-                elif is_mistral_model(selected_model_id):
+                if is_mistral_model(selected_model_id):
                     print("[Generate] Using Mistral SDK")
                     stream = client.chat.stream(
                         model=actual_model_id,
@@ -426,11 +415,7 @@ async def generate_code(
                     # Handle different response formats
                     chunk_content = None
                     
-                    if selected_model_id == "gemini-3-pro-preview":
-                        # Gemini native SDK format: chunk.text
-                        if hasattr(chunk, 'text') and chunk.text:
-                            chunk_content = chunk.text
-                    elif is_mistral_model(selected_model_id):
+                    if is_mistral_model(selected_model_id):
                         # Mistral format: chunk.data.choices[0].delta.content
                         if (hasattr(chunk, "data") and chunk.data and
                             hasattr(chunk.data, "choices") and chunk.data.choices and 

backend_models.py

@@ -8,32 +8,25 @@ from typing import Optional
 from openai import OpenAI
 from mistralai import Mistral
 
-# Import genai for Gemini 3
+# Import genai for Gemini (legacy - no longer used with Poe API)
 try:
     from google import genai
     from google.genai import types
     GEMINI_AVAILABLE = True
 except ImportError:
     GEMINI_AVAILABLE = False
-    print("WARNING: google-genai not available, Gemini 3 will not work")
 
 def get_inference_client(model_id: str, provider: str = "auto"):
     """
     Return an appropriate client based on model_id.
     
-    For Gemini 3: Returns genai.Client (native Google SDK)
-    For others: Returns OpenAI-compatible client or raises error
+    Returns OpenAI-compatible client for all models or raises error if not configured.
     """
-    if model_id == "gemini-3-pro-preview":
-        if not GEMINI_AVAILABLE:
-            raise ImportError("google-genai package required for Gemini 3. Install with: pip install google-genai")
-        # Use native Google GenAI client for Gemini 3 Pro Preview with v1alpha API
-        api_key = os.getenv("GEMINI_API_KEY")
-        if not api_key:
-            raise ValueError("GEMINI_API_KEY environment variable required for Gemini 3")
-        return genai.Client(
-            api_key=api_key,
-            http_options={'api_version': 'v1alpha'}
+    if model_id == "gemini-3.0-pro":
+        # Use Poe (OpenAI-compatible) client for Gemini 3.0 Pro
+        return OpenAI(
+            api_key=os.getenv("POE_API_KEY"),
+            base_url="https://api.poe.com/v1"
         )
     
     elif model_id == "qwen3-30b-a3b-instruct-2507":
@@ -328,7 +321,7 @@ def create_gemini3_messages(messages: list) -> tuple:
 
 def is_native_sdk_model(model_id: str) -> bool:
     """Check if model uses native SDK (not OpenAI-compatible)"""
-    return model_id in ["gemini-3-pro-preview"]
+    return False  # All models now use OpenAI-compatible APIs
 
 
 def is_mistral_model(model_id: str) -> bool:

frontend/src/app/page.tsx

@@ -14,7 +14,7 @@ export default function Home() {
   const [messages, setMessages] = useState<Message[]>([]);
   const [generatedCode, setGeneratedCode] = useState('');
   const [selectedLanguage, setSelectedLanguage] = useState<Language>('html');
-  const [selectedModel, setSelectedModel] = useState('gemini-3-pro-preview');
+  const [selectedModel, setSelectedModel] = useState('gemini-3.0-pro');
   const [isGenerating, setIsGenerating] = useState(false);
   const [isAuthenticated, setIsAuthenticated] = useState(false);