import os import json import uuid import torch import numpy as np from PIL import Image import io import folder_paths from google import genai from google.genai import types import time import traceback import threading import queue import sys import importlib import subprocess import random def check_and_install_dependencies(): required_packages = { 'requests': 'requests', 'pysocks': 'PySocks', } for module_name, package_name in required_packages.items(): try: importlib.import_module(module_name) except ImportError: print(f"[WARNING] {package_name} not found, trying to install...") try: subprocess.check_call([sys.executable, "-m", "pip", "install", package_name]) print(f"[INFO] Successfully installed {package_name}") except Exception as e: print(f"[ERROR] Failed to install {package_name}: {str(e)}") try: check_and_install_dependencies() except Exception as e: print(f"[WARNING] Error checking dependencies: {str(e)}") class SSL_GeminiAPIKeyConfig: @classmethod def INPUT_TYPES(cls): return { "required": { "api_key": ("STRING", {"multiline": False}), } } RETURN_TYPES = ("GEMINI_CONFIG",) RETURN_NAMES = ("config",) FUNCTION = "configure" CATEGORY = "💠SSL/API/Gemini" def configure(self, api_key): config = {"api_key": api_key} return (config,) class SSL_GeminiTextPrompt: @classmethod def INPUT_TYPES(cls): return { "required": { "config": ("GEMINI_CONFIG",), "prompt": ("STRING", {"multiline": True}), "system_instruction": ("STRING", {"default": "You are a helpful AI assistant.", "multiline": True}), "model": (["gemini-1.0-pro", "gemini-exp-1206", "gemini-1.5-flash", "gemini-1.5-pro", "gemini-2.0-flash", "gemini-2.0-flash-lite-001", "gemini-2.0-flash-exp", "gemini-2.0-pro", "gemini-2.0-pro-exp-02-05"], {"default": "gemini-2.0-flash"}), "temperature": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "top_p": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.01}), "top_k": ("INT", {"default": 40, "min": 1, "max": 100, "step": 1}), "max_output_tokens": ("INT", {"default": 8192, "min": 1, "max": 8192, "step": 1}), "include_images": (["True", "False"], {"default": "True"}), }, "optional": { "input_image": ("IMAGE",), "input_image_2": ("IMAGE",), # Add second image input "use_proxy": (["False", "True"], {"default": "False"}), "proxy_host": ("STRING", {"default": "127.0.0.1"}), "proxy_port": ("INT", {"default": 7890, "min": 1, "max": 65535}), "use_seed": (["True", "False"], {"default": "True"}), "seed": ("INT", {"default": 0, "min": 0, "max": 2147483647}), } } RETURN_TYPES = ("STRING", "IMAGE", "INT") RETURN_NAMES = ("text", "image", "seed") FUNCTION = "generate" CATEGORY = "💠SSL/API/Gemini" def save_binary_file(self, data, mime_type): ext = ".bin" if mime_type == "image/png": ext = ".png" elif mime_type == "image/jpeg": ext = ".jpg" output_dir = folder_paths.get_output_directory() gemini_dir = os.path.join(output_dir, "gemini_outputs") os.makedirs(gemini_dir, exist_ok=True) file_name = os.path.join(gemini_dir, f"gemini_output_{uuid.uuid4()}{ext}") with open(file_name, "wb") as f: f.write(data) return file_name def generate_empty_image(self, width=64, height=64): empty_image = np.ones((height, width, 3), dtype=np.float32) * 0.2 tensor = torch.from_numpy(empty_image).unsqueeze(0) return tensor def generate(self, config, prompt, system_instruction, model, temperature, top_p, top_k, max_output_tokens, include_images, input_image=None, input_image_2=None, use_proxy="False", proxy_host="127.0.0.1", proxy_port=7890, use_seed="True", seed=0): original_http_proxy = os.environ.get('HTTP_PROXY') original_https_proxy = os.environ.get('HTTPS_PROXY') original_http_proxy_lower = os.environ.get('http_proxy') original_https_proxy_lower = os.environ.get('https_proxy') print(f"[INFO] Starting generation, model: {model}, temperature: {temperature}") # Improved random seed handling logic if use_seed == "True": # If seed is 0, generate a random seed if seed == 0: # Use current timestamp and random number combination to generate seed, ensuring it's different each run current_time = int(time.time() * 1000) random_component = random.randint(0, 1000000) actual_seed = (current_time + random_component) % 2147483647 print(f"[INFO] Generated random seed: {actual_seed}") else: # Use the specified seed value actual_seed = seed print(f"[INFO] Using specified seed: {actual_seed}") # Set random seed random.seed(actual_seed) np.random.seed(actual_seed) torch.manual_seed(actual_seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(actual_seed) else: actual_seed = None print("[INFO] Seed not used") try: if use_proxy == "True": if not proxy_host.startswith(('http://', 'https://')): proxy_url = f"http://{proxy_host}:{proxy_port}" else: proxy_url = f"{proxy_host}:{proxy_port}" os.environ['HTTP_PROXY'] = proxy_url os.environ['HTTPS_PROXY'] = proxy_url os.environ['http_proxy'] = proxy_url os.environ['https_proxy'] = proxy_url os.environ['REQUESTS_CA_BUNDLE'] = '' print(f"[INFO] Proxy enabled: {proxy_url}") print(f"[INFO] Initializing Gemini client") try: client_options = {} if use_proxy == "True": try: import google.api_core.http_client import google.auth.transport.requests try: import requests from requests.adapters import HTTPAdapter class ProxyAdapter(HTTPAdapter): def __init__(self, proxy_url, **kwargs): self.proxy_url = proxy_url super().__init__(**kwargs) def add_headers(self, request, **kwargs): super().add_headers(request, **kwargs) session = requests.Session() proxies = { "http": proxy_url, "https": proxy_url } session.proxies.update(proxies) adapter = ProxyAdapter(proxy_url, max_retries=3) session.mount('http://', adapter) session.mount('https://', adapter) session.verify = False http_client = google.api_core.http_client.RequestsHttpClient(session=session) client_options["http_client"] = http_client print(f"[INFO] Used requests library to set proxy for HTTP client") except Exception as proxy_error: print(f"[WARNING] Failed to set HTTP client proxy: {str(proxy_error)}") except ImportError as e: print(f"[WARNING] Failed to import Google API HTTP client library: {str(e)}") client = genai.Client(api_key=config["api_key"], **client_options) print(f"[INFO] Gemini client initialized successfully") except Exception as e: print(f"[ERROR] Gemini client initialization failed: {str(e)}") return (f"Gemini client initialization failed: {str(e)}", self.generate_empty_image(), actual_seed if actual_seed is not None else 0) try: import socket test_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) test_socket.settimeout(5) test_host = "generativelanguage.googleapis.com" if use_proxy == "True": try: import socks test_socket = socks.socksocket() test_socket.set_proxy(socks.HTTP, proxy_host, proxy_port) test_socket.settimeout(5) except ImportError: print(f"[WARNING] PySocks library not installed, cannot test connection via proxy") test_socket.connect((test_host, 443)) test_socket.close() network_ok = True except Exception as e: network_ok = False print(f"[WARNING] Network connection test failed: {str(e)}") if use_proxy == "True": try: import subprocess cmd = f"curl -x {proxy_url} -s -o /dev/null -w '%{{http_code}}' https://{test_host}" try: result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=10) if result.returncode == 0 and result.stdout.strip() in ['200', '301', '302']: network_ok = True else: print(f"[WARNING] curl proxy test failed, status code: {result.stdout.strip() if result.stdout else 'N/A'}") except Exception as curl_error: print(f"[WARNING] curl test failed: {str(curl_error)}") except ImportError: pass contents = [] # 处理输入图像 images_to_process = [] if input_image is not None: images_to_process.append(input_image) if input_image_2 is not None: images_to_process.append(input_image_2) # 如果有图像需要处理 if images_to_process: try: img_parts = [] for img in images_to_process: img_array = img[0].cpu().numpy() img_array = (img_array * 255).astype(np.uint8) pil_img = Image.fromarray(img_array) img_byte_arr = io.BytesIO() pil_img.save(img_byte_arr, format='PNG') img_bytes = img_byte_arr.getvalue() img_part = {"inline_data": {"mime_type": "image/png", "data": img_bytes}} img_parts.append(img_part) # Add all image parts and text parts contents = img_parts + [{"text": prompt}] except Exception as e: print(f"[ERROR] Error processing input image: {str(e)}") return (f"Error processing input image: {str(e)}", self.generate_empty_image(), actual_seed if actual_seed is not None else 0) else: contents = prompt response_modalities = ["text"] if include_images == "True": response_modalities.append("image") generate_content_config = types.GenerateContentConfig( temperature=temperature, top_p=top_p, top_k=top_k, max_output_tokens=max_output_tokens, safety_settings=[ types.SafetySetting( category="HARM_CATEGORY_HARASSMENT", threshold="BLOCK_NONE", # Block none ), types.SafetySetting( category="HARM_CATEGORY_HATE_SPEECH", threshold="BLOCK_NONE", # Block none ), types.SafetySetting( category="HARM_CATEGORY_SEXUALLY_EXPLICIT", threshold="BLOCK_NONE", # Block none ), types.SafetySetting( category="HARM_CATEGORY_DANGEROUS_CONTENT", threshold="BLOCK_NONE", # Block none ), ], response_modalities=response_modalities, system_instruction=[ types.Part.from_text(text=system_instruction), ], ) if use_seed == "True" and actual_seed is not None: try: generate_content_config.seed = actual_seed except Exception as seed_error: print(f"[WARNING] Failed to set seed for API request: {str(seed_error)}") # text_output = "" # Will be initialized in api_call and retrieved from queue # image_tensor = None # Will be initialized in api_call and retrieved from queue try: print(f"[INFO] Sending API request to Gemini") start_time = time.time() timeout = 30 # Timeout for queue.get() def api_call(): last_exception = None max_retries = 3 for attempt in range(max_retries): current_text_output = "" current_image_tensor = None # Initialized for each attempt try: print(f"[INFO] API call attempt {attempt + 1}/{max_retries}") api_response = client.models.generate_content( model=model, contents=contents, config=generate_content_config, ) print(f"[INFO] Received API response for attempt {attempt + 1}") if hasattr(api_response, 'candidates') and api_response.candidates: for part in api_response.candidates[0].content.parts: if hasattr(part, 'text') and part.text is not None: current_text_output += part.text elif hasattr(part, 'inline_data') and part.inline_data is not None: try: inline_data = part.inline_data mime_type = inline_data.mime_type data = inline_data.data image_path = self.save_binary_file(data, mime_type) img = Image.open(image_path) if img.mode != 'RGB': img = img.convert('RGB') img_array = np.array(img).astype(np.float32) / 255.0 current_image_tensor = torch.from_numpy(img_array).unsqueeze(0) except Exception as img_e: print(f"[ERROR] Image processing error during attempt {attempt + 1}: {str(img_e)}") current_text_output += f"\nImage processing error: {str(img_e)}" if current_image_tensor is None: current_image_tensor = self.generate_empty_image() if current_image_tensor is None: # Ensure image_tensor is always set current_image_tensor = self.generate_empty_image() result_queue.put(("success", (current_text_output, current_image_tensor))) print(f"[INFO] Attempt {attempt + 1}/{max_retries} successful.") return # Success, exit the function except Exception as e: last_exception = e print(f"[ERROR] Attempt {attempt + 1}/{max_retries} failed: {str(e)}") if attempt < max_retries - 1: wait_time = 2**attempt # Exponential backoff: 1s, 2s print(f"[INFO] Waiting {wait_time}s before next attempt...") time.sleep(wait_time) # On the last attempt, the loop will end, and we'll put the error in the queue outside the loop. # If all attempts failed print(f"[ERROR] All {max_retries} API call and processing attempts failed. Last error: {str(last_exception)}") result_queue.put(("error", last_exception)) result_queue = queue.Queue() api_thread = threading.Thread(target=api_call) api_thread.daemon = True api_thread.start() text_output = "" image_tensor = self.generate_empty_image() # Default to empty image try: status, result = result_queue.get(timeout=timeout) elapsed_time = time.time() - start_time if status == "success": text_output, image_tensor = result # result is now a tuple (text_output, image_tensor) print(f"[INFO] API request and processing successfully completed in {elapsed_time:.2f} seconds") else: # status == "error" error_exception = result print(f"[ERROR] Error in API request/processing thread after {elapsed_time:.2f} seconds: {str(error_exception)}") text_output = f"API call/processing error: {str(error_exception)}" # image_tensor is already self.generate_empty_image() error_str = str(error_exception).lower() if any(term in error_str for term in ["timeout", "connection", "network", "socket", "连接", "网络"]): if not network_ok and use_proxy != "True": text_output += " Network connection test failed, consider enabling proxy." # return (f"API request failed: {str(error_exception)}. Network connection test failed, consider enabling proxy.", self.generate_empty_image(), actual_seed if actual_seed is not None else 0) # raise result # No need to raise here, already handled by setting text_output except queue.Empty: elapsed_time = time.time() - start_time print(f"[ERROR] API request/processing timed out in main thread, waited: {elapsed_time:.2f} seconds") timeout_msg = f"Gemini API request/processing timed out, waited {timeout} seconds." if not network_ok: if use_proxy == "True": timeout_msg += f" Network connection test failed, the current proxy ({proxy_host}:{proxy_port}) may be invalid, please check proxy settings." else: timeout_msg += " Network connection test failed, consider enabling proxy." else: timeout_msg += " Network connection test successful, but API request/processing still timed out. This could be due to a busy server or a large request." text_output = timeout_msg # image_tensor is already self.generate_empty_image() # return (timeout_msg, self.generate_empty_image(), actual_seed if actual_seed is not None else 0) # print(f"[INFO] Received API response") # This line is now part of api_call or success message # The following block is now inside api_call # if hasattr(response, 'candidates') and response.candidates: # for part in response.candidates[0].content.parts: # if hasattr(part, 'text') and part.text is not None: # text_output += part.text # # elif hasattr(part, 'inline_data') and part.inline_data is not None: # try: # inline_data = part.inline_data # mime_type = inline_data.mime_type # data = inline_data.data # # image_path = self.save_binary_file(data, mime_type) # # img = Image.open(image_path) # # if img.mode != 'RGB': # img = img.convert('RGB') # # img_array = np.array(img).astype(np.float32) / 255.0 # image_tensor = torch.from_numpy(img_array).unsqueeze(0) # except Exception as e: # print(f"[ERROR] Image processing error: {str(e)}") # text_output += f"\nImage processing error: {str(e)}" except Exception as e: # Catch-all for unforeseen errors in the main try block print(f"[ERROR] Unhandled error in generate method: {str(e)}") text_output = f"Unhandled error: {str(e)}" if image_tensor is None: # Ensure image_tensor is set image_tensor = self.generate_empty_image() # if image_tensor is None: # This should be handled by initialization or in api_call # image_tensor = self.generate_empty_image() # if use_seed == "True" and actual_seed is not None: # seed_info = f"\n\n[Seed information: {actual_seed}]" # text_output += seed_info return (text_output, image_tensor, actual_seed if actual_seed is not None else 0) finally: if original_http_proxy: os.environ['HTTP_PROXY'] = original_http_proxy else: if 'HTTP_PROXY' in os.environ: os.environ.pop('HTTP_PROXY') if original_https_proxy: os.environ['HTTPS_PROXY'] = original_https_proxy else: if 'HTTPS_PROXY' in os.environ: os.environ.pop('HTTPS_PROXY') if original_http_proxy_lower: os.environ['http_proxy'] = original_http_proxy_lower else: if 'http_proxy' in os.environ: os.environ.pop('http_proxy') if original_https_proxy_lower: os.environ['https_proxy'] = original_https_proxy_lower else: if 'https_proxy' in os.environ: os.environ.pop('https_proxy') if 'REQUESTS_CA_BUNDLE' in os.environ: os.environ.pop('REQUESTS_CA_BUNDLE') try: import requests if hasattr(requests, 'Session'): clean_session = requests.Session() if hasattr(requests, 'session'): requests.session = lambda: clean_session except: pass NODE_CLASS_MAPPINGS = { "SSL_GeminiAPIKeyConfig": SSL_GeminiAPIKeyConfig, "SSL_GeminiTextPrompt": SSL_GeminiTextPrompt, } NODE_DISPLAY_NAME_MAPPINGS = { "SSL_GeminiAPIKeyConfig": "Configure Gemini API Key", "SSL_GeminiTextPrompt": "Expanded Gemini Text/Image", }