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启动

uvicorn main:app --reload

1. 一个基于 FastAPI 的翻译服务 API，主要功能包括提交翻译请求、查询翻译状态和结果

工作流程

1. 用户通过 POST /translate 提交翻译请求
2. 服务保存请求到数据库并启动后台任务
3. 后台任务处理完成后更新数据库
4. 用户可以通过 GET /translate/{id} 查询状态和结果

main.py

from fastapi import FastAPI, BackgroundTasks, HTTPException, Request, Depends
# Depends 的作用，依赖注入：FastAPI 的核心特性之一，允许你声明某个参数（如 db）需要由其他函数（如 get_db）提供。
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware
from fastapi.templating import Jinja2Templates
from sqlalchemy.orm import Session # 表示数据库会话对象，通过这个会话，可以执行数据库操作（如查询、插入、更新等）。from schemas import TranslationRequestSchema
from typing import List
from utils import translate_text, process_translations# db related
from database import engine, SessionLocal, get_db
import models 
from models import TranslationRequest, TranslationResult, IndividualTranslations# 创建数据库中所有定义的表结构
models.Base.metadata.create_all(engine)app = FastAPI()# Add CORS middleware
app.add_middleware(CORSMiddleware,allow_origins=["*"],  # Allows all originsallow_credentials=True,allow_methods=["*"],  # Allows all methodsallow_headers=["*"],  # Allows all headers
)# 指定模板文件存放的目录（通常是项目下的 `templates` 文件夹）
templates = Jinja2Templates(directory="templates")# 返回一个 HTML 页面 (index.html)
@app.get("/index", response_class=HTMLResponse)
def index(request: Request):return templates.TemplateResponse("index.html", # 模板文件名（位于 `templates/` 下）{"request": request} # 传递给模板的上下文数据)
# FastAPI 的 TemplateResponse 要求上下文必须包含 request 对象，用于生成 URL# 接收 POST 请求
@app.post("/translate")
async def translate(request: TranslationRequestSchema, background_tasks: BackgroundTasks, db: Session = Depends(get_db)):print(request.text)print(request.languages)# 创建并保存翻译请求到数据库request_data = models.TranslationRequest(text=request.text,languages=request.languages)db.add(request_data)db.commit()db.refresh(request_data)# print(f"Translation request submitted. ID: {request_data.id}")  # 打印 ID 以确认是否正确生成# 添加后台任务处理翻译background_tasks.add_task(process_translations, request_data.id, request.text, request.languages)# print("request_data: ", request_data)return {"payload": request_data}# 根据 request_id 查询翻译请求
@app.get("/translate/{request_id}")
async def get_translation_status(request_id: int, request: Request, db: Session = Depends(get_db)):# 根据 request_id 从 TranslationRequest 表中获取一条记录request_obj = db.query(TranslationRequest).filter(TranslationRequest.id == request_id).first() # 执行查询并返回第一条结果if not request_obj:raise HTTPException(status_code=404, detail="Request not found")if request_obj.status == "in progress":return {"status": request_obj.status}translations = db.query(TranslationResult).filter(TranslationResult.request_id == request_id).all()return templates.TemplateResponse("results.html", {"request": request, "translations": translations})

2. 验证从前端（如 Web 或移动端）传入的数据是否符合预期格式

schemas.py

# Making sure the data coming from the frontend is valid
from pydantic import BaseModelclass TranslationRequestSchema(BaseModel):text: strlanguages: strclass Config:schema_extra = {"example": {"text": "Hello, world!","languages": "english, german, russian"}}# schema_extra：为 FastAPI 的自动文档（如 Swagger UI）提供示例数据。# 效果：在 API 文档中会显示这个示例，帮助开发者理解如何构造请求体。

3. 配置数据库连接

database.py

import os
from dotenv import load_dotenv
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
# sessionmaker：生成数据库会话（Session）的工厂。load_dotenv()SQLALCHEMY_DATABASE_URL = os.getenv("DATABASE_URL")
# DATABASE_URL：数据库连接字符串，格式通常为： "数据库类型://用户名:密码@主机:端口/数据库名"engine = create_engine(SQLALCHEMY_DATABASE_URL, echo=True)
# create_engine：创建核心接口，用于管理数据库连接池。
# echo=True：调试模式下打印所有执行的 SQL 语句（生产环境应设为 False）。SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
# autocommit=False：禁用自动提交（需显式调用 commit()）。
# autoflush=False：禁用自动刷新（避免意外刷新未完成的操作）。
# bind=engine：绑定到之前创建的引擎。Base = declarative_base()# 作用：为每个请求生成独立的数据库会话，并在请求结束后自动关闭。
def get_db():db = SessionLocal()try:yield db # yield db：将会话提供给路由函数使用finally:db.close()

4. 定义三个SQLAlchemy数据库模型，并创建了对应的数据库表结构。这些模型用于管理一个翻译系统的数据存储。

models.py

# 定义三个数据库模型，并创建了对应的数据库表from sqlalchemy import Column, Integer, String, DateTime, ForeignKey, JSON, create_engine
from sqlalchemy.ext.declarative import declarative_base
from datetime import datetime
import os
from dotenv import load_dotenvload_dotenv()SQLALCHEMY_DATABASE_URL = os.getenv("DATABASE_URL")Base = declarative_base()# 记录用户提交的翻译请求，包括原文、目标语言和状态
class TranslationRequest(Base):__tablename__ = "translation_requests"id = Column(Integer, primary_key=True, index=True)text = Column(String, nullable=False)languages = Column(String, nullable=False)status = Column(String, default="in progress", nullable=False)created_at = Column(DateTime, default=datetime.utcnow)updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)# 存储每个请求的翻译结果（按语言拆分）
# 一个TranslationRequest可以对应多个TranslationResult
class TranslationResult(Base):__tablename__ = "translation_results"id = Column(Integer, primary_key=True, index=True)# request_id: 外键，关联到TranslationRequest表的idrequest_id = Column(Integer, ForeignKey("translation_requests.id"), nullable=False)language = Column(String, nullable=False)translated_text = Column(String, nullable=False)created_at = Column(DateTime, default=datetime.utcnow)class IndividualTranslations(Base):__tablename__ = "individual_translations"id = Column(Integer, primary_key=True, index=True)request_id = Column(Integer, ForeignKey("translation_requests.id"), nullable=False)translated_text = Column(String, nullable=False)created_at = Column(DateTime, default=datetime.utcnow)# to ensure tables are created in the database
engine = create_engine(SQLALCHEMY_DATABASE_URL)
Base.metadata.create_all(engine) # 在数据库中生成所有定义的表

5. 使用Google的Gemini AI模型进行文本翻译，并将结果存储到数据库中

utils.py

import google.generativeai as genai
from sqlalchemy.orm import Session
from models import TranslationRequest, TranslationResult, IndividualTranslations
from datetime import datetime
from database import get_db
from typing import Listimport os
from dotenv import load_dotenvload_dotenv()GEMINI_API_KEY = os.getenv("GOOGLE_API_KEY")
async def translate_text(text: str, language: str) -> str:# 设置 API Keygenai.configure(api_key=GEMINI_API_KEY)model = genai.GenerativeModel("gemini-2.0-flash")  # 选择 Gemini-Pro 模型# 发送请求并获取响应response = model.generate_content(f"Translate the following text to {language}: {text}. Only provide the translated text without any additional explanations or formatting")return response.text.strip()
async def process_translations(request_id: int, text: str, languages: List[str]):db = next(get_db()) # 获取数据库会话try:language_list = languages.split(", ")for language in language_list:translated_text = await translate_text(text, language)# 将结果保存到两个表中translation_result = TranslationResult(request_id=request_id, language=language, translated_text=translated_text)individual_translation = IndividualTranslations(request_id=request_id, translated_text=translated_text)db.add(translation_result)db.add(individual_translation)db.commit()# 查询数据库，获取与 request_id 对应的翻译请求记录。request = db.query(TranslationRequest).filter(TranslationRequest.id == request_id).first()request.status = "completed"request.updated_at = datetime.utcnow()db.add(request)db.commit()finally:db.close()

6. 实现一个多语言翻译服务的用户界面

index.html

<!DOCTYPE html>
<html lang="en">
<head><meta charset="UTF-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Translation Service</title><link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet"><style>body, html {height: 100%;margin: 0;background-color: white;display: flex;flex-direction: column;}.content {flex: 1;}.navbar {background-color: #34568B;}.btn-primary {background-color: #88B04B;border-color: #88B04B;}.form-label {color: #34568B;}.logo {max-width: 30%;height: auto;padding: 20px;}.footer {color: black;text-align: center;padding: 10px 0;width: 100%;}.form-container {display: flex;justify-content: center;align-items: center;flex-direction: column;height: 100%;padding: 20px;}.form-box {width: 100%;max-width: 600px;margin: 10px 0;}.btn-container {text-align: center;margin-top: 20px;}</style>
</head>
<body><nav class="navbar navbar-expand-lg navbar-dark"><div class="container"><a class="navbar-brand mx-auto" href="#">Multilingual Translation Service</a><div class="navbar-text text-light">Powered by Google LLM Gemini 2.0</div></div></nav><div class="content"><div class="container form-container"><!-- 翻译文本输入框 --><div class="form-box"><label for="text" class="form-label">Text to Translate</label><textarea id="text" class="form-control" rows="10" placeholder="Enter text here..."></textarea></div><!-- 语言选择输入框 --><div class="form-box"><label for="languages" class="form-label">Languages</label><input id="languages" class="form-control" type="text" placeholder="e.g., english, german, russian"><small class="form-text text-muted">Write the languages you want to translate your text to, separated by commas.</small></div><!-- 提交按钮 --><div class="btn-container"><button class="btn btn-primary" onclick="translateText()">Translate</button></div></div></div><script>async function translateText() {// Extract the text and languages from the formvar text_for_translation = document.getElementById('text').value;var languages_chosen = document.getElementById('languages').value;// Prepare the payloadvar payload = {text: text_for_translation,languages: languages_chosen};try {// Execute the POST request to the translation endpointconst response = await fetch('http://localhost:8000/translate', {method: 'POST',headers: {'Content-Type': 'application/json'},body: JSON.stringify(payload)});// Check if the response is OKif (!response.ok) {throw new Error(`HTTP error! Status: ${response.status}`);}// Parse and handle the responseconst result = await response.json();// alert('The result is: ' + result);alert('Translation request submitted. ID: ' + result.payload.id);window.location.href = '/translate/' + result.payload.id;} catch (error) {console.error('Error:', error);alert('Failed to submit translation request.');}}</script><script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script>
</body>
</html>

7. 展示翻译结果

results.html

<!DOCTYPE html>
<html lang="en">
<head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><title>Translation Results</title><link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet">
</head>
<body><!-- 深蓝色导航栏 --><nav class="navbar navbar-expand-lg navbar-dark" style="background-color: #34568B;"><div class="container"><a class="navbar-brand mx-auto" href="#">Multilingual Translation Results</a><div class="navbar-text text-light">Powered by Google LLM Gemini 2.0</div></div></nav><div class="container mt-4"><h1>Translation Results</h1><div><!-- 循环遍历传入的translations列表 -->{% for translation in translations %}<!-- 每个翻译结果用卡片(card)展示 --><div class="card mb-3"><!-- 卡片头部(card-header)显示目标语言 --><div class="card-header">Language: {{ translation.language }}</div><!-- 卡片正文(card-body)显示翻译内容 --><div class="card-body"><p class="card-text">{{ translation.translated_text }}</p></div></div>{% endfor %}</div></div><script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script>
</body>
</html>

8. 数据库配置

DATABASE_URL = 'postgresql://postgres:password@localhost:5432/translationservice'
# DATABASE_URL：数据库连接字符串，格式通常为： "数据库类型://用户名:密码@主机:端口/数据库名"