forked from quant-speed-AI/Scoring-System
421 lines
18 KiB
Python
421 lines
18 KiB
Python
import json
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import logging
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import time
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import uuid
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import oss2
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from aliyunsdkcore.client import AcsClient
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from aliyunsdkcore.acs_exception.exceptions import ClientException, ServerException
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# 尝试导入最新的 API 版本，如果有问题可能需要调整
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try:
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from aliyunsdktingwu.request.v20230930 import CreateTaskRequest, GetTaskInfoRequest
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except ImportError:
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# Fallback or error handling if version differs
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pass
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from django.conf import settings
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logger = logging.getLogger(__name__)
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from .models import TranscriptionTask, AIEvaluation, AIEvaluationTemplate
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class AliyunTingwuService:
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def __init__(self):
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self.access_key_id = settings.ALIYUN_ACCESS_KEY_ID
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self.access_key_secret = settings.ALIYUN_ACCESS_KEY_SECRET
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self.oss_bucket_name = settings.ALIYUN_OSS_BUCKET_NAME
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self.oss_endpoint = settings.ALIYUN_OSS_ENDPOINT
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self.tingwu_app_key = settings.ALIYUN_TINGWU_APP_KEY
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self.region_id = "cn-shanghai" # 听悟服务区域，根据文档应与OSS区域一致，或者使用 'cn-beijing'
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# 初始化 OSS Bucket
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if self.access_key_id and self.access_key_secret and self.oss_endpoint:
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auth = oss2.Auth(self.access_key_id, self.access_key_secret)
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self.bucket = oss2.Bucket(auth, self.oss_endpoint, self.oss_bucket_name)
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else:
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self.bucket = None
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logger.warning("Aliyun OSS configuration missing.")
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# 初始化听悟 Client
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if self.access_key_id and self.access_key_secret:
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self.client = AcsClient(
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self.access_key_id,
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self.access_key_secret,
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self.region_id
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)
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# 显式添加听悟服务的 Endpoint 映射，解决 EndpointResolvingError
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# 听悟 API 的服务接入点通常是 tingwu.cn-beijing.aliyuncs.com
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# 但新版听悟 API (tingwu.aliyuncs.com) 可能不同，需根据实际情况添加
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# 这里添加一个通用的 Endpoint 映射
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try:
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# 尝试为 tingwu 产品设置 Endpoint
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# 注意：听悟服务主要部署在北京，Endpoint 通常为 tingwu.cn-beijing.aliyuncs.com
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# 如果您的服务在上海，也可能需要连接到北京的接入点
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self.client.add_endpoint(self.region_id, "tingwu", "tingwu.cn-beijing.aliyuncs.com")
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except Exception as e:
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logger.warning(f"Failed to add endpoint: {e}")
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else:
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self.client = None
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logger.warning("Aliyun AccessKey configuration missing.")
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def upload_to_oss(self, file_obj, file_name, day=7):
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"""
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上传文件到 OSS 并返回带签名的 URL
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默认生成有效期为 7 天 (3600 * 24 * day) 的签名URL，方便评委在一段时间内都能播放。
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"""
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if not self.bucket:
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raise Exception("OSS Client not initialized")
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try:
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# 上传文件
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# file_obj 应该是打开的文件对象或字节流
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self.bucket.put_object(file_name, file_obj)
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# 生成签名 URL，有效期 7 天 (3600 * 24 * 7 = 604800 秒)
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url = self.bucket.sign_url('GET', file_name, 3600 * 24 * day)
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return url
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except Exception as e:
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logger.error(f"OSS Upload failed: {e}")
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raise e
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def create_transcription_task(self, file_url, language="cn"):
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"""
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创建听悟转写任务
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"""
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if not self.client:
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raise Exception("Tingwu Client not initialized")
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request = CreateTaskRequest.CreateTaskRequest()
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# 针对阿里云 SDK 不同版本的兼容性处理
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# "type" 参数是听悟 API (ROA 风格) 的必填项，用于指定任务类型
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# 根据官方文档，离线任务的 type 通常就是 'offline'
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request.add_query_param('type', 'offline')
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# 构造请求体 (Body)
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# 根据听悟 API 文档，AppKey, Input, Parameters 应位于 JSON Body 中
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# 而不是 Query Parameter
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body = {
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"AppKey": self.tingwu_app_key,
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"Input": {
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"FileUrl": file_url,
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"SourceLanguage": language,
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"TaskKey": str(uuid.uuid4())
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},
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"Parameters": {
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"Transcoding": {
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"TargetAudioFormat": "mp3"
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},
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"Transcription": {
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"DiarizationEnabled": True,
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"ChannelId": 0
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},
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"TranscriptionEnabled": True,
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"AutoChaptersEnabled": True,
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"SummarizationEnabled": True,
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"Summarization": {
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"Types": ["Paragraph", "Conversational", "QuestionsAnswering", "MindMap"]
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}
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}
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}
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# 设置 Body 内容
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request.set_content(json.dumps(body))
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request.add_header('Content-Type', 'application/json')
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# 强制设置 Endpoint，避免 SDK.EndpointResolvingError
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# 听悟目前主要服务点在北京
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request.set_endpoint("tingwu.cn-beijing.aliyuncs.com")
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# 显式设置 Method 为 PUT
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request.set_method('PUT')
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try:
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response = self.client.do_action_with_exception(request)
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return json.loads(response)
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except (ClientException, ServerException) as e:
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logger.error(f"Tingwu CreateTask failed: {e}")
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raise e
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def get_task_info(self, task_id):
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"""
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查询任务状态和结果
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"""
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if not self.client:
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raise Exception("Tingwu Client not initialized")
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request = GetTaskInfoRequest.GetTaskInfoRequest()
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request.set_TaskId(task_id)
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try:
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response = self.client.do_action_with_exception(request)
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return json.loads(response)
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except (ClientException, ServerException) as e:
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logger.error(f"Tingwu GetTaskInfo failed: {e}")
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raise e
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def parse_and_update_task(self, task, result):
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"""
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解析听悟结果并更新任务
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:param task: TranscriptionTask 实例
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:param result: get_task_info 返回的完整 JSON (或 Data 部分)
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"""
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# 记录之前的状态，用于判断是否是首次完成
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previous_status = task.status
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# 1. 提取 Data 对象
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if isinstance(result, dict):
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data_obj = result.get('Data', result)
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else:
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data_obj = result
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if not isinstance(data_obj, dict):
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logger.error(f"Unexpected data format: {type(data_obj)}")
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return
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# 2. 更新状态
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task_status = data_obj.get('TaskStatus') or data_obj.get('Status')
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if task_status in ['COMPLETE', 'COMPLETED', 'SUCCEEDED']:
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task.status = 'SUCCEEDED' # 使用字符串引用，避免导入模型循环引用
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elif task_status == 'FAILED':
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task.status = 'FAILED'
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task.error_message = data_obj.get('TaskStatusText', data_obj.get('Message', 'Unknown error'))
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task.save()
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return
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else:
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# 仍在处理中，不更新内容
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return
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# 3. 解析结果
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task_result = data_obj.get('Result', {})
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# 兼容处理：如果 Result 为空，或者不存在，尝试直接使用 data_obj 作为结果源
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# 某些情况下，Summarization/AutoChapters 可能直接位于 Data 层级
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if not task_result:
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task_result = data_obj
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# 辅助函数：从源字典或其 Result 子字典中获取字段
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def get_data_field(source, key):
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# 1. 尝试直接从 task_result 获取 (如果 task_result 就是 Data 本身，这里也会生效)
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if isinstance(source, dict) and key in source:
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return source[key]
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# 2. 如果 source 是 Data，尝试从 source['Result'] 获取
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if isinstance(source, dict) and 'Result' in source and isinstance(source['Result'], dict):
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if key in source['Result']:
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return source['Result'][key]
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return None
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# --- A. 处理逐字稿 (Transcription) ---
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transcription_data = get_data_field(task_result, 'Transcription') or get_data_field(data_obj, 'Transcription') or {}
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# 处理 URL 下载
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if isinstance(transcription_data, str) and transcription_data.startswith('http'):
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try:
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import requests
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t_resp = requests.get(transcription_data)
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if t_resp.status_code == 200:
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transcription_data = t_resp.json()
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except Exception as e:
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logger.error(f"Download transcription failed: {e}")
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transcription_data = {}
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elif isinstance(transcription_data, dict) and 'TranscriptionUrl' in transcription_data:
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try:
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import requests
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t_resp = requests.get(transcription_data['TranscriptionUrl'])
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if t_resp.status_code == 200:
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transcription_data = t_resp.json()
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except Exception as e:
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logger.error(f"Download transcription url failed: {e}")
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# 保存原始数据
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task.transcription_data = transcription_data
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# 提取文本
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# 结构: {"Transcription": {"Paragraphs": [{"Words": [{"Text": "..."}]}]}}
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# 或直接 {"Paragraphs": ...}
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content_source = transcription_data
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if 'Transcription' in content_source and isinstance(content_source['Transcription'], dict):
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content_source = content_source['Transcription']
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paragraphs = content_source.get('Paragraphs', [])
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full_text_lines = []
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if paragraphs and isinstance(paragraphs, list):
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for p in paragraphs:
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# 尝试从 Words 中提取
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words = p.get('Words', [])
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if words:
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line_text = "".join([str(w.get('Text', '')) for w in words])
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full_text_lines.append(line_text)
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# 兼容旧结构或直接 Text
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elif 'Text' in p:
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full_text_lines.append(p['Text'])
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if full_text_lines:
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task.transcription = "\n".join(full_text_lines)
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# --- B. 处理 AI 总结 (Summarization) ---
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summarization = get_data_field(task_result, 'Summarization') or get_data_field(data_obj, 'Summarization') or {}
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# 处理 URL 下载
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if isinstance(summarization, str) and summarization.startswith('http'):
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try:
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import requests
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s_resp = requests.get(summarization)
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if s_resp.status_code == 200:
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summarization = s_resp.json()
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except Exception as e:
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logger.error(f"Download summarization failed: {e}")
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summarization = {}
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# 保存原始数据
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task.summary_data = summarization
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# 提取文本 (MindMapSummary)
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# 结构: {"MindMapSummary": [{"Title": "...", "Topic": [...]}]}
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# 移除了原先的 summary_text 拼接逻辑
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# --- C. 处理章节 (AutoChapters) ---
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auto_chapters = get_data_field(task_result, 'AutoChapters') or get_data_field(data_obj, 'AutoChapters') or []
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# 处理 URL 下载
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if isinstance(auto_chapters, str) and auto_chapters.startswith('http'):
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try:
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import requests
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ac_resp = requests.get(auto_chapters)
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if ac_resp.status_code == 200:
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auto_chapters = ac_resp.json()
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except Exception as e:
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logger.error(f"Download auto chapters failed: {e}")
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auto_chapters = []
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# 保存原始数据
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task.auto_chapters_data = auto_chapters
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# 保存任务，确保原始数据已写入数据库
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task.save()
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# 调用大模型生成总结 (如果 summary_data 或 auto_chapters_data 存在)
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if task.summary_data or task.auto_chapters_data:
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try:
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# 设置占位状态
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task.summary = "AI总结生成当中..."
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task.save(update_fields=['summary'])
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# 异步执行总结
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import threading
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from .bailian_service import BailianService
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def async_summarize_in_service(task_id):
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try:
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# 重新获取 task 以避免线程安全问题
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from .models import TranscriptionTask
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t = TranscriptionTask.objects.get(id=task_id)
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bailian_service = BailianService()
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bailian_service.summarize_task(t)
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except Exception as e:
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logger.error(f"Async summary generation failed in service: {e}")
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threading.Thread(target=async_summarize_in_service, args=(task.id,)).start()
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logger.info(f"Triggered async summary generation for task {task.id}")
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except Exception as e:
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logger.error(f"Failed to trigger AI summarization: {e}")
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# 4. 自动触发 AI 评估 (如果任务首次成功且有启用的模板)
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if previous_status != 'SUCCEEDED' and task.status == 'SUCCEEDED' and task.transcription:
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# 同样改为异步触发，传递 task.id 以避免线程中的对象状态问题
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import threading
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threading.Thread(target=self.trigger_ai_evaluations, args=(task.id,)).start()
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def trigger_ai_evaluations(self, task_id):
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"""
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根据启用的模板自动触发 AI 评估
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逻辑：
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1. 如果模板关联了评分维度(s score_dimension)，只对关联了相同维度的比赛进行评估
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2. 如果模板未关联评分维度：
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- 如果是默认模板(is_default=True)，评价所有比赛
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- 否则不进行自动评价
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"""
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try:
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# 在线程中重新获取 task 对象，并预加载 project，避免懒加载导致的线程数据库连接问题
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from .models import TranscriptionTask
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task = TranscriptionTask.objects.select_related('project', 'project__competition').get(id=task_id)
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except Exception as e:
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# 兼容处理：如果 task_id 其实是 task 对象（虽然我们上面改了，但防止其他地方调用传错）
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if hasattr(task_id, 'id'):
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try:
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from .models import TranscriptionTask
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task = TranscriptionTask.objects.select_related('project', 'project__competition').get(id=task_id.id)
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except:
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task = task_id
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else:
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logger.error(f"Failed to retrieve task {task_id}: {e}")
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return
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active_templates = AIEvaluationTemplate.objects.filter(is_active=True)
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if not active_templates.exists():
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logger.info("No active AI evaluation templates found.")
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return
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from .bailian_service import BailianService
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service = BailianService()
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for template in active_templates:
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# 检查是否已经存在相同的评估，避免重复创建
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if AIEvaluation.objects.filter(task=task, template=template).exists():
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logger.info(f"Evaluation for task {task.id} and template {template.name} already exists.")
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continue
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# 获取任务关联的比赛
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task_competition = None
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if task.project and task.project.competition:
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task_competition = task.project.competition
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# 判断是否应该对此任务进行评估
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should_evaluate = False
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if template.score_dimension:
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# 模板关联了评分维度，只对关联了相同维度的比赛进行评估
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if task_competition:
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# 获取该比赛下所有关联了相同评分维度的比赛ID列表
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from competition.models import ScoreDimension
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related_competition_ids = ScoreDimension.objects.filter(
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id=template.score_dimension.id
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).values_list('competition_id', flat=True)
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if task_competition.id in related_competition_ids:
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should_evaluate = True
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logger.info(f"Template '{template.name}' is linked to score_dimension, task's competition matches.")
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else:
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logger.info(f"Template '{template.name}' is linked to score_dimension, but task's competition does not match. Skipping.")
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else:
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logger.info(f"Task {task.id} has no associated competition. Skipping template '{template.name}'.")
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else:
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# 模板未关联评分维度，只有默认模板才评价所有比赛
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if template.is_default:
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should_evaluate = True
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logger.info(f"Template '{template.name}' is default template, evaluating all competitions.")
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else:
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logger.info(f"Template '{template.name}' is not linked to score_dimension and is not default. Skipping.")
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if not should_evaluate:
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continue
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# 创建评估记录
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evaluation = AIEvaluation.objects.create(
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task=task,
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template=template,
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model_selection=template.model_selection,
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prompt=template.prompt,
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status=AIEvaluation.Status.PENDING
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)
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# 触发评估
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try:
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service.evaluate_task(evaluation)
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logger.info(f"Triggered evaluation {evaluation.id} for template {template.name}")
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except Exception as e:
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logger.error(f"Failed to trigger evaluation {evaluation.id}: {e}")
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