gemini_node_vertex.py

import io
import json
import wave
import torch
import numpy as np
from PIL import Image
from google import genai
from google.genai import types
from google.oauth2 import service_account

class GeminiChatVertexNode:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "prompt": ("STRING", {"multiline": True}),
                "project_id": ("STRING", {"multiline": False, "default": ""}),
                "location": ([
                    "global", "us-central1", "us-east1", "us-east4", "us-east5", "us-south1",
                    "us-west1", "us-west2", "us-west3", "us-west4",
                    "northamerica-northeast1", "northamerica-northeast2",
                    "southamerica-east1", "southamerica-west1", "africa-south1",
                    "europe-west1", "europe-north1", "europe-west2", "europe-west3",
                    "europe-west4", "europe-west6", "europe-west8", "europe-west9",
                    "europe-west12", "europe-southwest1", "europe-central2",
                    "asia-east1", "asia-east2", "asia-northeast1", "asia-northeast2",
                    "asia-northeast3", "asia-south1", "asia-south2", "asia-southeast1",
                    "asia-southeast2", "australia-southeast1", "australia-southeast2",
                    "me-central1", "me-central2", "me-west1"
                ], {"default": "global"}),
                "service_account": ("STRING", {"multiline": True, "default": ""}),
                "model": ([
                    "gemini-3.6-flash", 
                    "gemini-3.5-flash", 
                    "gemini-3.5-flash-lite", 
                    "gemini-3.1-pro-preview", 
                    "gemini-3.1-flash-lite", 
                    "gemini-3-flash-preview", 
                    "gemini-2.5-flash", 
                    "gemini-2.5-pro", 
                    "gemini-2.5-flash-lite", 
                    "gemini-flash-latest", 
                    "gemini-flash-lite-latest"
                ], {"default": "gemini-3.5-flash"}),
                "temperature": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 2.0, "step": 0.1}),
                "top_p": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.01}),
                "thinking": ("BOOLEAN", {"default": True}),
                "google_search": ("BOOLEAN", {"default": False}),
                "url_context": ("BOOLEAN", {"default": False}),
                "seed": ("INT", {"default": 69, "min": -1, "max": 2147483646, "step": 1}),
            },
            "optional": {
                "system_instruction": ("STRING", {"multiline": True, "default": ""}),
                "thinking_budget": ("INT", {"default": -1, "min": -1, "max": 24576, "step": 1}),
                "image": ("IMAGE",),
                "audio": ("AUDIO",),
            }
        }
    
    RETURN_TYPES = ("STRING",)
    RETURN_NAMES = ("response",)
    FUNCTION = "generate"
    CATEGORY = "text/generation"
    
    def setup_client(self, service_account_json, project_id, location):
        if not service_account_json.strip():
            raise ValueError("Service account JSON content is required.")
        if not project_id.strip():
            raise ValueError("Project ID is required.")
        
        try:
            sa_info = json.loads(service_account_json)
        except json.JSONDecodeError as e:
            raise ValueError(f"Invalid JSON content: {str(e)}")

        credentials = service_account.Credentials.from_service_account_info(
            sa_info,
            scopes=["https://www.googleapis.com/auth/cloud-platform"]
        )

        return genai.Client(
            vertexai=True,
            project=project_id.strip(),
            location=location.strip(),
            credentials=credentials,
            http_options=types.HttpOptions(
                retry_options=types.HttpRetryOptions(attempts=10, jitter=10)
            )
        )

    def generate(self, prompt, project_id, location, service_account, model, temperature, top_p,
                 thinking, google_search, url_context, seed,
                 system_instruction=None, thinking_budget=-1, image=None, audio=None):

        client = self.setup_client(service_account, project_id, location)
        parts = [types.Part.from_text(text=prompt)]

        if image is not None:
            for i in range(image.shape[0]):
                arr = (image[i].cpu().numpy() * 255).astype(np.uint8)
                buf = io.BytesIO()
                Image.fromarray(arr).save(buf, format="PNG")
                parts.append(types.Part.from_bytes(mime_type="image/png", data=buf.getvalue()))

        if audio is not None:
            wf = audio.get("waveform") if isinstance(audio, dict) else audio[0]
            sr = audio.get("sample_rate", 44100) if isinstance(audio, dict) else audio[1]
            
            wf = wf.cpu().numpy() if isinstance(wf, torch.Tensor) else wf
            if wf.ndim > 1: wf = wf.mean(axis=0) if wf.shape[0] > 1 else wf.squeeze()
            wf_int16 = (np.clip(wf, -1, 1) * 32767).astype(np.int16)
            
            buf = io.BytesIO()
            with wave.open(buf, 'wb') as w:
                w.setnchannels(1); w.setsampwidth(2); w.setframerate(sr)
                w.writeframes(wf_int16.tobytes())
            parts.append(types.Part.from_bytes(mime_type="audio/wav", data=buf.getvalue()))

        model_lower = model.lower()
        t_config = None

        # Check if the chosen model is a Pro model (requires thinking configurations to run properly)
        is_pro = "pro" in model_lower

        if is_pro or thinking:
            final_budget = thinking_budget if thinking else 0
            if is_pro and final_budget == 0:
                print("Pro models cannot have thinking turned off - defaulting thinking budget to -1")
                final_budget = -1
            
            t_config = types.ThinkingConfig(thinking_budget=final_budget)

        tools = []
        if google_search: tools.append(types.Tool(googleSearch=types.GoogleSearch()))
        if url_context: tools.append(types.Tool(url_context=types.UrlContext()))

        config = types.GenerateContentConfig(
            temperature=temperature,
            top_p=top_p,
            seed=seed,
            system_instruction=system_instruction.strip() if system_instruction else None,
            thinking_config=t_config,
            tools=tools if tools else None
        )
        
        response = client.models.generate_content(
            model=model,
            contents=[types.Content(role="user", parts=parts)],
            config=config
        )
        
        return (response.text,)

NODE_CLASS_MAPPINGS = {"GeminiChatVertexNode": GeminiChatVertexNode}
NODE_DISPLAY_NAME_MAPPINGS = {"GeminiChatVertexNode": "Gemini Chat (Vertex AI)"}