diff --git a/prompter.py b/prompter.py index db6ef42..9a11fda 100644 --- a/prompter.py +++ b/prompter.py @@ -6,6 +6,17 @@ import re from PIL import Image import numpy as np import base64 +from ComfyUI_RN_External_Interface.utils import generate_request_id, log_prepare, log_complete, log_error, ProgressBar + +ENV_KEYS_API_KEY = ["COMFYUI_RN_API_KEY", "COMFLY_API_KEY", "RUNNODE_API_KEY", "RN_API_KEY", "LLM_API_KEY", "OPENAI_API_KEY", "DEEPSEEK_API_KEY"] +ENV_KEYS_BASE_URL = ["COMFYUI_RN_BASE_URL", "COMFLY_BASE_URL", "RUNNODE_BASE_URL", "RN_BASE_URL", "LLM_API_BASEURL", "OPENAI_BASE_URL", "OPENAI_API_BASE_URL", "DEEPSEEK_API_BASE_URL"] + +def get_first_env(names): + for n in names: + v = os.environ.get(n) + if v is not None and str(v).strip() != "": + return v + return None def _normalize_baseurl(u): if not u: @@ -182,8 +193,10 @@ def get_model_config(model_name): config_path = path break - with open(config_path, 'r', encoding='utf-8') as f: - config = json.load(f) + config = {} + if config_path and os.path.exists(config_path): + with open(config_path, 'r', encoding='utf-8') as f: + config = json.load(f) # 模型名称映射字典（前端显示名称 -> 配置中的模型名称） mapping = { @@ -463,19 +476,31 @@ class RN_Translator(): return f"翻译错误：{str(e)}" def translate_text(self, prompt, advanced_options, seed=0, apiBaseUrl="default", apiKey="default", model="default"): - + request_id = generate_request_id("translate", "rn_prompter") + log_prepare("文本翻译", request_id, "RunNode/Prompter-", "Prompter") + rn_pbar = ProgressBar(request_id, "Prompter", streaming=True, task_type="文本翻译", source="RunNode/Prompter-") cleaned = re.sub(r'[\x00\x01-\x08\x0b\x0c\x0e-\x1f\x7f]', '', prompt or "") if not cleaned.strip(): + rn_pbar.error("错误：请输入要翻译的文本") return ("错误：请输入要翻译的文本",) src_label, dst_label = self._detect_direction(cleaned, advanced_options) chunks = self._split_text_into_chunks(cleaned, max_chunk_size=400) if len(chunks) == 1: res = self._translate_chunk(chunks[0], src_label, dst_label, temperature=None, apiBaseUrl=apiBaseUrl, apiKey=apiKey, model=model) + if isinstance(res, str) and (res.startswith("错误：") or res.startswith("翻译错误")): + rn_pbar.error(res) + else: + rn_pbar.done(char_count=len(res or "")) return (res,) translated = [] for c in chunks: translated.append(self._translate_chunk(c, src_label, dst_label, temperature=None, apiBaseUrl=apiBaseUrl, apiKey=apiKey, model=model)) - return (' '.join(translated),) + joined = ' '.join(translated) + if joined.startswith("错误：") or joined.startswith("翻译错误"): + rn_pbar.error(joined) + else: + rn_pbar.done(char_count=len(joined)) + return (joined,) @classmethod def IS_CHANGED(cls, **kwargs): @@ -608,19 +633,31 @@ class RN_Prompt_Translator(): return f"翻译错误：{str(e)}" def translate_text(self, prompt, advanced_options, seed=0, apiBaseUrl="default", apiKey="default", model="default"): - + request_id = generate_request_id("prompt_translate", "rn_prompter") + log_prepare("提示词翻译", request_id, "RunNode/Prompter-", "Prompter") + rn_pbar = ProgressBar(request_id, "Prompter", streaming=True, task_type="提示词翻译", source="RunNode/Prompter-") cleaned = re.sub(r'[\x00\x01-\x08\x0b\x0c\x0e-\x1f\x7f]', '', prompt or "") if not cleaned.strip(): + rn_pbar.error("错误：请输入要翻译的文本") return ("错误：请输入要翻译的文本",) src_label, dst_label = self._detect_direction(cleaned, advanced_options) chunks = self._split_text_into_chunks(cleaned, max_chunk_size=400) if len(chunks) == 1: res = self._translate_chunk(chunks[0], src_label, dst_label, temperature=None, apiBaseUrl=apiBaseUrl, apiKey=apiKey, model=model) + if isinstance(res, str) and (res.startswith("错误：") or res.startswith("翻译错误")): + rn_pbar.error(res) + else: + rn_pbar.done(char_count=len(res or "")) return (res,) translated = [] for c in chunks: translated.append(self._translate_chunk(c, src_label, dst_label, temperature=None, apiBaseUrl=apiBaseUrl, apiKey=apiKey, model=model)) - return (' '.join(translated),) + joined = ' '.join(translated) + if joined.startswith("错误：") or joined.startswith("翻译错误"): + rn_pbar.error(joined) + else: + rn_pbar.done(char_count=len(joined)) + return (joined,) @classmethod def IS_CHANGED(cls, **kwargs): @@ -743,17 +780,30 @@ class RN_Midjourney_Prompter(): return f"Error: {str(e)}" def generate_mj_prompt(self, prompt, seed=0, temperature=0.3, apiBaseUrl="default", apiKey="default", model="default"): + request_id = generate_request_id("mj_prompt", "rn_prompter") + log_prepare("MJ 提示词", request_id, "RunNode/Prompter-", "Prompter") + rn_pbar = ProgressBar(request_id, "Prompter", streaming=True, task_type="MJ提示词", source="RunNode/Prompter-") cleaned = re.sub(r'[\x00\x01-\x08\x0b\x0c\x0e-\x1f\x7f]', '', prompt or "") if not cleaned.strip(): + rn_pbar.error("错误：请输入要生成的文本") return ("错误：请输入要生成的文本",) chunks = self._split_text_into_chunks(cleaned, max_chunk_size=400) if len(chunks) == 1: res = self._generate_chunk_mj(chunks[0], temperature=temperature, apiBaseUrl=apiBaseUrl, apiKey=apiKey, model=model) + if isinstance(res, str) and (res.startswith("错误：") or res.startswith("Error")): + rn_pbar.error(res) + else: + rn_pbar.done(char_count=len(res or "")) return (res,) generated = [] for c in chunks: generated.append(self._generate_chunk_mj(c, temperature=temperature, apiBaseUrl=apiBaseUrl, apiKey=apiKey, model=model)) - return (' '.join(generated),) + joined = ' '.join(generated) + if joined.startswith("错误：") or joined.startswith("Error"): + rn_pbar.error(joined) + else: + rn_pbar.done(char_count=len(joined)) + return (joined,) @classmethod def IS_CHANGED(cls, **kwargs): @@ -790,6 +840,9 @@ class RN_LLMAPI_Node(): def rn_run_llmapi(self, api_baseurl, api_key, model, role, prompt, temperature, seed, max_video_frames=5, ref_image=None, video=None): + request_id = generate_request_id("llmapi", "rn_prompter") + log_prepare("通用LLM调用", request_id, "RunNode/Prompter-", "Prompter") + rn_pbar = ProgressBar(request_id, "Prompter", streaming=True, task_type="LLM调用", source="RunNode/Prompter-") cfg = get_config() vllm_cfg = get_vllm_config() has_visual = (ref_image is not None) or (video is not None) @@ -817,10 +870,7 @@ class RN_LLMAPI_Node(): # 处理视频输入 if video is not None: try: - # 提取视频帧 video_frames = extract_video_frames(video, max_frames=max_video_frames) - - # 为每个帧添加图像内容 for i, frame_base64 in enumerate(video_frames): user_content.append({ "type": "image_url", @@ -829,15 +879,11 @@ class RN_LLMAPI_Node(): "detail": "medium" } }) - - # 如果需要，可以在prompt中说明这些是视频帧 if len(video_frames) > 1: prompt_addition = f"\nNote: I've provided {len(video_frames)} keyframes from the video. " user_content[0]["text"] = prompt + prompt_addition - except Exception as e: - print(f"视频处理出错: {str(e)}") - # 可以选择返回错误信息或继续处理 + rn_pbar.error(f"视频处理出错: {str(e)}") # 构建消息 messages.append({ @@ -852,15 +898,16 @@ class RN_LLMAPI_Node(): temperature=temperature, seed=seed ) - if completion is not None and hasattr(completion, 'choices'): response = completion.choices[0].message.content else: response = 'Error: No response from API' - except Exception as e: response = f"API调用出错: {str(e)}" - + if isinstance(response, str) and (response.startswith("Error") or response.startswith("API调用出错")): + rn_pbar.error(response) + else: + rn_pbar.done(char_count=len(response or "")) return (response,) @@ -908,12 +955,20 @@ class RN_LLMAPI_Pro_Node(): CATEGORY = "RunNode/rn_prompter" def rn_run_llmapi_pro(self, model, role, prompt, temperature, seed, api_baseurl='', api_key='', ref_image=None): + request_id = generate_request_id("llmapi_pro", "rn_prompter") + log_prepare("专业LLM调用", request_id, "RunNode/Prompter-", "Prompter") + rn_pbar = ProgressBar(request_id, "Prompter", streaming=True, task_type="LLM调用", source="RunNode/Prompter-") + + # 获取环境变量 + env_base_url = get_first_env(ENV_KEYS_BASE_URL) + env_api_key = get_first_env(ENV_KEYS_API_KEY) + # 获取模型对应的配置 model_config = get_model_config(model) - # 优先使用传入的api_baseurl和api_key，如果没有则使用配置文件中的 - used_api_baseurl = api_baseurl or model_config.get('base_url', '') - used_api_key = api_key or model_config.get('api_key', '') + # 优先级: 输入参数 > 环境变量 > 模型特定配置 > 全局配置 + used_api_baseurl = api_baseurl or env_base_url or model_config.get('base_url', '') + used_api_key = api_key or env_api_key or model_config.get('api_key', '') used_model = model_config.get('model', '') # 使用配置中的模型名称 # 如果配置中没有模型名称，使用映射后的名称 @@ -950,6 +1005,7 @@ class RN_LLMAPI_Pro_Node(): if not used_api_baseurl: return ("ERROR: No API base URL configured. Please check your configuration.",) + used_api_baseurl = _normalize_baseurl(used_api_baseurl) print(f"Using base_url: {used_api_baseurl}") print(f"Using model: {used_model}") @@ -987,14 +1043,63 @@ class RN_LLMAPI_Pro_Node(): messages=messages, temperature=temperature ) - - if completion is not None and hasattr(completion, 'choices'): + if completion is not None and hasattr(completion, 'choices') and len(completion.choices) > 0: prompt = completion.choices[0].message.content else: - prompt = 'Error: No response from API' - + print(f"API Response invalid: {completion}") + prompt = 'Error: No response from API (Empty choices)' except Exception as e: - print(f"API call error: {e}") - prompt = f'Error: {str(e)}' + error_msg = str(e) + print(f"API call error: {error_msg}") + # 诊断：如果是 JSON 解析错误，尝试获取原始响应内容帮助调试 + if "Expecting value" in error_msg or "JSON" in error_msg: + try: + import urllib.request + import urllib.error + + # 尝试构建完整 URL 进行测试 + test_url = used_api_baseurl + if not test_url.endswith('/'): + test_url += '/' + test_url += 'chat/completions' + + print(f"Diagnosing API connection to: {test_url}") + + # 简单的测试请求头 + headers = { + "Authorization": f"Bearer {used_api_key}", + "Content-Type": "application/json" + } + + # 最小化的测试 Payload + payload = json.dumps({ + "model": used_model, + "messages": [{"role": "user", "content": "hi"}], + "max_tokens": 1 + }).encode('utf-8') + + req = urllib.request.Request(test_url, data=payload, headers=headers, method='POST') + + try: + with urllib.request.urlopen(req, timeout=10) as response: + print(f"Diagnostic Response Code: {response.getcode()}") + body = response.read().decode('utf-8', errors='ignore') + print(f"Diagnostic Response Body (first 500 chars): {body[:500]}") + except urllib.error.HTTPError as he: + print(f"Diagnostic HTTP Error: {he.code}") + error_body = he.read().decode('utf-8', errors='ignore') + print(f"Diagnostic Error Body: {error_body[:500]}") + error_msg += f" (HTTP {he.code}: {error_body[:200]})" + except Exception as de: + print(f"Diagnostic check failed: {de}") + + except Exception as diag_e: + print(f"Failed to run diagnostics: {diag_e}") + + prompt = f'Error: {error_msg}' + if isinstance(prompt, str) and prompt.startswith('Error'): + rn_pbar.error(prompt) + else: + rn_pbar.done(char_count=len(prompt or "")) return (prompt,)