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- import hashlib
- import traceback
- import uuid
- from concurrent.futures import ThreadPoolExecutor
- from datetime import date
- from datetime import timedelta
- from io import BytesIO
- from typing import Optional, List
- from urllib.parse import quote
- import os
- import sys
- import pandas as pd
- import uvicorn
- import yaml
- from fastapi import FastAPI
- from fastapi.middleware.cors import CORSMiddleware
- from fastapi.responses import StreamingResponse
- from loguru import logger
- from pydantic import BaseModel, Field
- curr_path = os.path.abspath(os.path.dirname(__file__))
- project_root_path = curr_path[:curr_path.find("video_to_word") + len("video_to_word")]
- sys.path.append(project_root_path)
- from asr_client import send_asr_request, send_task_request
- from common_func import get_db_engine
- from config.url import toutiao_static_video_url
- from database import insert, update, query, Task
- from time_task.get_material_and_script_by_query_word import get_material_and_script
- logger.remove() # 删去 import logger之后自动产生的handler,不删除的话会出现重复输出的现象
- logger.add("/data/pythonProject/video_to_word/logs/main_server.{time:YYYY-MM-DD}.log",
- rotation="00:00",
- format="{time:YYYY-MM-DD HH:mm:ss,SSS} [{process}] [{thread}] {level} {file} {line} - {message}",
- level="INFO")
- with open('/data/pythonProject/video_to_word/config/config.yaml', mode='r', encoding='utf-8') as f:
- config = yaml.load(f.read(), Loader=yaml.FullLoader)
- source_name_map = config['source_name_map']
- # 数据库连接引擎,依据开发、测试环境/生产环境 进行切换
- mac = uuid.UUID(int=uuid.getnode()).hex[-12:]
- if mac in ['5254003fa716', '52540003f5dd']:
- ai_word_engine = get_db_engine(config['ai_word_dev_db'])
- else:
- ai_word_engine = get_db_engine(config['ai_word_product_db'])
- threadPool = ThreadPoolExecutor(max_workers=4)
- app = FastAPI()
- origins = [
- "http://192.168.1.34",
- "http://192.168.1.34:8000",
- "http://192.168.1.105",
- "http://192.168.1.105:3000",
- "http://111.206.86.186",
- "http://111.206.86.186:3000",
- "http://adsp.tjyourong.com.cn",
- "http://adsp.tjyourong.com.cn:3000",
- "http://adsp.c-top.com.cn",
- "http://adsp.c-top.com.cn:3000"
- ]
- app.add_middleware(
- CORSMiddleware,
- allow_origins=origins,
- allow_credentials=True,
- allow_methods=["*"],
- allow_headers=["*"],
- )
- class QueryItem():
- signature: Optional[str] = None
- url: Optional[str] = None
- @app.get('/', tags=['back-end task'])
- def index():
- return {'message': '你已经正确创建 FastApi 服务!'}
- @app.post('/asr/task/submit', tags=['back-end task'])
- def task_submit(signature: str, url: str):
- json = send_asr_request(url)
- task = Task(signature=signature, task_id=json.Data.TaskId, task_result=json.to_json_string(), task_status=1)
- insert(task)
- return {'code': 0, 'taskId': json.Data.TaskId}
- @app.post('/asr/task/result', tags=['back-end task'])
- def task_submit(task_id: int):
- json = send_task_request(task_id)
- task = query(None, None, task_id)[0]
- task.task_status = json.Data.Status
- task.task_result = json.to_json_string()
- try:
- if json.Data.Status == 2:
- task.word_text = json.Data.ResultDetail[0].FinalSentence
- task.word_split = json.Data.ResultDetail[0].SliceSentence
- task.word_text_md5 = hashlib.md5(task.word_text.encode('utf-8')).hexdigest()
- except:
- # 提取原始文本内容和分词内容发生异常,把 task_status 置为 -1
- task.task_status = -1
- update(task)
- return {'code': 0, 'status': json.Data.StatusStr}
- @app.post('/asr/task/list', tags=['back-end task'])
- def task_submit(task_status: int):
- task = query(None, task_status, None)
- return {'code': 0, 'data': task}
- class BaseResponse(BaseModel):
- message: str = Field(..., description='消息')
- success: bool = Field(..., description='true or false')
- code: int = Field(..., description='')
- class TaskDetail(BaseModel):
- source_name: str = Field('内部创意', description='数据来源名称')
- query_word: str = Field('红包', description='关键词')
- stat_date: str = Field('2021-11-11', description='日期')
- script_num: int = Field(180, description='脚本数量')
- task_status: str = Field('执行成功', description='状态')
- number: int = Field(0, description='序号')
- class ConfigDetail(BaseModel):
- config_id: str = Field(..., description="脚本配置id")
- query_word_lst: List[str] = Field(..., description="关键词")
- create_time: str = Field(..., description="创建时间")
- operator: str = Field(..., description="创建人")
- number: int = Field(..., description="序号")
- class TaskResponse(BaseResponse):
- total_num: int = Field(0, description="总个数")
- page_num: int = Field(1, description="第几页")
- page_size: int = Field(10, description="每页个数")
- config_id: str = Field('', description="脚本配置id")
- result: List[TaskDetail] = Field(..., description="结果详情")
- class ConfigResponse(BaseResponse):
- total_num: int = Field(0, description="总个数")
- page_num: int = Field(1, description="第几页")
- page_size: int = Field(10, description="每页个数")
- result: List[ConfigDetail] = Field(..., description="结果详情")
- class QueryWordItem(BaseModel):
- query_word: str = Field("红包", description="查询词", min_length=1)
- stat_date: str = Field("2021-11-16", description="日期", min_length=10, max_length=10)
- source_code: int = Field(2, description="数据来源编码{1:'内部创意', 2:'巨量创意', 3:'开眼快创'}")
- class ScriptConfigLst(BaseModel):
- start_date: Optional[date] = Field(date.today() + timedelta(days=-6), description="开始日期-用于查询")
- end_date: Optional[date] = Field(date.today(), description="结束日期-用于查询")
- search_word: Optional[str] = Field('', description="关键词-用于查询")
- page_num: int = Field(1, description="第几页")
- page_size: int = Field(10, description="每页的大小")
- class QueryWordTaskInfoLst(BaseModel):
- start_date: Optional[date] = Field(date.today() + timedelta(days=-30), description="开始日期-用于查询")
- end_date: Optional[date] = Field(date.today(), description="结束日期-用于查询")
- search_word: Optional[str] = Field('', description="关键词-用于查询")
- page_num: int = Field(1, description="第几页")
- page_size: int = Field(10, description="每页的大小")
- config_id: Optional[str] = Field('', description="脚本配置id")
- source_code: Optional[List[int]] = Field([0], description="数据来源编码{1:'内部创意', 2:'巨量创意', 3:'开眼快创'}")
- class AddScriptConfig(BaseModel):
- query_word_lst: List[str] = Field(..., description="关键词组")
- operator: str = Field(..., description="操作者")
- class Config:
- schema_extra = {
- "example": {
- "query_word_lst": ["红包", "淘特"],
- "operator": "龙猫"
- }
- }
- @logger.catch
- @app.post('/export_script_file/', tags=['front-end interactive'],
- description="导出文件",
- summary='导出文件'
- )
- def export_script_file(item: List[QueryWordItem]):
- try:
- video_df = pd.DataFrame()
- # 1 从数据库获取视频数据
- # 如果同一个素材有多个查询词,则合并打上这多个查询词
- for obj in item:
- query_word = obj.query_word
- stat_date = obj.stat_date
- source_code = obj.source_code
- sql = f"select signature, video_url, query_word, stat_date, {source_code} source_code from {source_name_map[source_code]['table']} " \
- f"where query_word = '{query_word}' " \
- f"and stat_date = '{stat_date}'"
- df = pd.read_sql(sql, ai_word_engine)
- video_df = video_df.append(df)
- # 按 'signature' + 'query_word' + 'stat_date' 进行去重
- video_df.drop_duplicates(['signature', 'query_word', 'stat_date', 'source_code'], keep='last', inplace=True)
- video_query_word_df = video_df.groupby('signature').apply(lambda x: pd.Series({'query_word_lst': x['query_word'].unique(),
- 'video_url': x['video_url'].values[0],
- 'source_code': x['source_code'].values[0]}))
- video_query_word_df.reset_index(inplace=True, drop=False)
- # 如果来源==2 (头条巨量引擎),把视频链接替换为永久链接
- video_query_word_df['video_url'] = video_query_word_df.apply(
- lambda row: toutiao_static_video_url + row['signature'] if row.get('source_code') == 2 else row['video_url'], axis=1)
- # 2 根据第一步的视频数据获取脚本
- if not video_query_word_df.empty:
- signature_lst = list(video_query_word_df.signature.values) if len(video_query_word_df.signature.values) > 1 \
- else list(video_query_word_df.signature.values) * 2
- sql = f"select signature, word_text from tb_asr_result where signature in {tuple(signature_lst)}" \
- f"and word_text is not null"
- script_df = pd.read_sql(sql, ai_word_engine)
- out_df = video_query_word_df.merge(script_df, on='signature', how='inner')
- # 3 返回流数据
- if not out_df.empty:
- bio = BytesIO()
- writer = pd.ExcelWriter(bio, engine='xlsxwriter')
- out_df[['signature', 'query_word_lst', 'word_text', 'video_url']].to_excel(writer, index=False, encoding='utf8mb4')
- writer.save()
- bio.seek(0)
- # 组装header
- now_date = date.today().strftime('%Y-%m-%d')
- headers = {"content-type": "application/vnd.ms-excel",
- "content-disposition": f"attachment;filename={quote('优质素材脚本_')}{now_date}.xlsx"
- }
- logger.info(f"request body: {item}, message: 数据导出成功")
- return StreamingResponse(bio, media_type='xlsx', headers=headers)
- else:
- logger.info(f"request body: {item}, message: 没有获取到对应的数据")
- return None
- except:
- logger.error(f"request body: {item}, message: {traceback.format_exc()}")
- return None
- @logger.catch
- @app.post('/get_script_config_lst/', tags=['front-end interactive'], response_model=ConfigResponse,
- description="脚本配置列表",
- summary='脚本配置列表'
- )
- def get_script_config_lst(item: ScriptConfigLst):
- try:
- end_date = item.end_date + timedelta(days=1)
- sql = f"select * from ctop_ai_script_query_word_config " \
- f"where start_time >= '{item.start_date}' " \
- f"and start_time < '{end_date}' " \
- f"and ('{item.search_word}' = '' or query_word like '%%{item.search_word}%%')"
- org_df = pd.read_sql(sql, ai_word_engine)
- g_df = org_df.groupby('config_id').apply(lambda x: pd.Series({'query_word_lst': list(x['query_word'].unique()),
- 'operator': x['operator'].min(),
- 'create_time': str(x['start_time'].min())}))
- g_df.reset_index(drop=False, inplace=True)
- g_df.sort_values(by='create_time', ascending=False, inplace=True)
- g_df['number'] = list(range(1, len(g_df) + 1))
- total_num = g_df.shape[0]
- detail = g_df.iloc[(item.page_num - 1) * item.page_size: item.page_num * item.page_size].to_dict('records')
- response = {'code': 0,
- "message": "查询成功",
- "success": True,
- "result": detail,
- "total_num": total_num,
- "page_num": item.page_num,
- "page_size": item.page_size}
- logger.info(f"request body: {item}, response body: {response}")
- return response
- except:
- response = {"code": -1,
- "message": traceback.format_exc(),
- "success": False,
- "result": None}
- logger.error(f"request body: {item}, response body: {response}")
- return response
- @logger.catch
- @app.post('/get_query_word_task_info_lst/', tags=['front-end interactive'], response_model=TaskResponse,
- description="脚本数据导出列表",
- summary='脚本数据导出列表'
- )
- def get_query_word_task_info_lst(item: QueryWordTaskInfoLst):
- try:
- end_date = item.end_date + timedelta(days=1)
- source_code_lst = item.source_code * 2 if len(item.source_code) == 1 else item.source_code
- if item.config_id != '':
- sql = f"select distinct(query_word) query_word from ctop_ai_script_query_word_config where config_id = {item.config_id}"
- query_word_lst = list(pd.read_sql(sql, ai_word_engine).query_word.values)
- if len(query_word_lst) > 0:
- query_word_lst = query_word_lst * 2 if len(query_word_lst) == 1 else query_word_lst
- sql = f"select * from ctop_ai_query_word_task_record where query_word in {tuple(query_word_lst)}" \
- f"and stat_date >= '{item.start_date}' and stat_date < '{end_date}' " \
- f"and ('{item.source_code}' = '[0]' or source_code in {tuple(source_code_lst)}) " \
- f"and ('{item.search_word}' = '' or query_word = '{item.search_word}')"
- df = pd.read_sql(sql, ai_word_engine)
- else:
- sql = f"select * from ctop_ai_query_word_task_record where " \
- f"stat_date >= '{item.start_date}' and stat_date < '{end_date}' " \
- f"and ('{item.source_code}' = '[0]' or source_code in {tuple(source_code_lst)}) " \
- f"and ('{item.search_word}' = '' or query_word = '{item.search_word}')"
- df = pd.read_sql(sql, ai_word_engine)
- df['source_name'] = df['source_code'].apply(lambda x: source_name_map[x]['name'])
- df = df[['source_name', 'query_word', 'stat_date', 'script_num', 'task_status']]
- df.sort_values(['stat_date', 'source_name', 'query_word'], ascending=False, inplace=True)
- df['number'] = list(range(1, len(df) + 1))
- total_num = df.shape[0]
- detail = df.iloc[(item.page_num - 1) * item.page_size: item.page_num * item.page_size].to_dict('records')
- response = {'code': 0,
- "message": "查询成功",
- "success": True,
- "result": detail,
- "total_num": total_num,
- "page_num": item.page_num,
- "page_size": item.page_size,
- "config_id": item.config_id}
- logger.info(f"request body: {item}, response body: {response}")
- return response
- except:
- response = {"code": -1,
- "message": traceback.format_exc(),
- "success": False,
- "result": None}
- logger.error(f"request body: {item}, response body: {response}")
- return response
- @logger.catch
- @app.post('/add_script_config/',
- tags=['front-end interactive'],
- description="新增脚本配置",
- summary='新增脚本配置',
- response_model=BaseResponse)
- def add_script_config(item: AddScriptConfig):
- try:
- # 按查询词拆分配置记录
- config_id = str(uuid.uuid4())
- config_df = pd.DataFrame(data=item.query_word_lst, columns=['query_word'])
- config_df['config_id'] = config_id
- config_df['operator'] = item.operator
- config_df['operate_type'] = 1
- # 新增配置记录插入到 ctop_ai_script_query_word_config
- config_df.to_sql(name="ctop_ai_script_query_word_config",
- con=ai_word_engine,
- if_exists='append',
- index=False)
- logger.info(f"request body: {item}, code:0, message: add_script_config success")
- return {"code": 0,
- "message": "add success",
- "success": True}
- except:
- logger.error(f"request body: {item}, code:-1, message: add_script_config fail {traceback.format_exc()}")
- return {"code": -1,
- "message": traceback.format_exc(),
- "success": False}
- @logger.catch
- @app.post('/get_material_and_script_time_task/',
- response_model=BaseResponse,
- tags=['back-end task'],
- description="获取素材和脚本任务",
- summary='获取素材和脚本任务')
- def get_material_and_script_time_task():
- try:
- get_material_and_script()
- logger.info(f"{date.today().strftime('%Y-%m-%d')}, 获取素材和脚本任务执行完成.")
- return {"code": 0,
- "success": True,
- "message": f"{date.today().strftime('%Y-%m-%d')},获取素材和脚本任务执行完成."}
- except:
- logger.error(f"{date.today().strftime('%Y-%m-%d')}, 获取素材和脚本任务执行发生异常. {traceback.format_exc()}")
- return {"code": -1,
- "success": False,
- "message": f"{date.today().strftime('%Y-%m-%d')},获取素材和脚本任务执行发生异常 .{traceback.format_exc()}"}
- if __name__ == '__main__':
- uvicorn.run(app='main:app', host="0.0.0.0", port=31013, reload=True, debug=True)
- # gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker #线上启动命令
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