Open_Duck_Mini_Interact/ark_api_module.py

from openai import OpenAI
import time
import sys
import queue  # 新增：用于缓存实时文本片段
import threading  # 新增：用于并行处理语音播放
# 原代码7. 火山方舟API调用完整逻辑
class ArkAPIController:
    def __init__(self, ark_api_key, ark_model_id, tts_controller, feedback_text):
        # 接收调度脚本传入的TTS实例和反馈文本，保持原逻辑
        self.ARK_API_KEY = ark_api_key
        self.ARK_MODEL_ID = ark_model_id
        self.tts_controller = tts_controller
        self.FEEDBACK_TEXT = feedback_text
        self.chat_context = []  # 聊天上下文由模块内部维护（与原逻辑一致）
        self.MAX_CONTEXT_LEN = 10

         # 新增：实时语音播放队列与线程
        self.speech_queue = queue.Queue()  # 缓存待播放的文本片段
        self.speech_thread = threading.Thread(target=self._process_speech_queue, daemon=True)
        self.speech_thread.start()  # 启动语音播放线程

     # 新增：处理语音队列的函数（循环从队列取片段并播放）
    def _process_speech_queue(self):
        """持续从队列中获取文本片段并调用TTS播放"""
        while True:
            text = self.speech_queue.get()  # 阻塞等待队列消息
            if text is None:  # 退出信号
                break
            self.tts_controller.speak(text)  # 播放片段
            self.speech_queue.task_done()  # 标记任务完成
    def call_ark_api(self, content_type: str, content: dict):
        # 播放操作反馈（同步执行）
        self.tts_controller.speak(self.FEEDBACK_TEXT[content_type])

        client = OpenAI(
            base_url="https://ark.cn-beijing.volces.com/api/v3",
            api_key=self.ARK_API_KEY
        )

        try:
            messages = []
            if content_type == "chat":
                messages.extend(self.chat_context[-self.MAX_CONTEXT_LEN*2:])
                messages.append({"role": "user", "content": [{"type": "text", "text": content["prompt"]}]})
            elif content_type == "image_recog":
                messages.append({
                    "role": "user",
                    "content": [
                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{content['image_base64']}"}},
                        {"type": "text", "text": content["prompt"]}
                    ]
                })

            response = client.chat.completions.create(
                model=self.ARK_MODEL_ID,
                messages=messages,
                max_tokens=300,
                temperature=0.7 if content_type == "chat" else 0.3,
                stream=True
            )

            full_response = ""
            current_speech_chunk = ""  # 缓存当前待播放的片段
            print("\n" + "="*50)
            print("🤖 回应：", end="", flush=True)
            
            for chunk in response:
                if chunk.choices and chunk.choices[0].delta.content:
                    char = chunk.choices[0].delta.content
                    full_response += char
                    current_speech_chunk += char  # 累加片段
                    print(char, end="", flush=True)
                    time.sleep(0.05)

                    # 关键逻辑：当片段包含标点或达到一定长度时，推送到语音队列
                    if any(punct in current_speech_chunk for punct in [".", "。", "!", "！", "?", "？", ",", "，", ";", "；"]):
                        self.speech_queue.put(current_speech_chunk)  # 推送片段到队列
                        current_speech_chunk = ""  # 重置片段缓存

            # 处理最后剩余的片段（如果有）
            if current_speech_chunk:
                self.speech_queue.put(current_speech_chunk)
            
            print("\n" + "="*50 + "\n")

            # 等待所有语音片段播放完成
            self.speech_queue.join()

            # 维护聊天上下文（原有逻辑）
            if content_type == "chat" and full_response.strip():
                self.chat_context.append({"role": "user", "content": [{"type": "text", "text": content["prompt"]}]})
                self.chat_context.append({"role": "assistant", "content": [{"type": "text", "text": full_response}]})

            return full_response
        except Exception as e:
            error_msg = f"❌ API调用失败：{str(e)}"
            print(f"\n" + "="*50)
            print(error_msg)
            print("="*50 + "\n")
            self.tts_controller.speak(self.FEEDBACK_TEXT["api_error"])
            return error_msg