# Win
11本地AI翻译服务
部署方案
##
1. 系统配置确认
- 系统
: Windows
11
- IP地址
: 10
.23
7.186
.51
- GPU: NVIDIA RTX 3060 (
12GB显存)
- 模型路径
: D
:\
Qwen1.5\models\
qwen\
Qwen1.5-7B
-Chat
## 2
. 目录结构设计
D
:\
Qwen1.5 建议的目录结构
:
```
Qwen1.5/
├── models/
│ └──
qwen/
│ └──
Qwen1.5-7B
-Chat/ (已有
模型文件)
├── app/
│ ├── static/ (静态文件)
│ ├── templates/ (HTML模板)
│ ├── config
.py (配置文件)
│ ├── m
ain
.py (主程序)
│ ├── translator
.py (翻译核心)
│ └── file_processor
.py (文件处理)
├── requirements
.txt (依赖文件)
└── README
.md (说明文档)
```
## 3
. 核心代码实现
### config
.py
```python
import os
class Config
:
MODEL_PATH = "D
:/
Qwen1.5/models/
qwen/
Qwen1.5-7B
-Chat"
HOST = "
10
.23
7.186
.51"
PORT =
5000
ALLOWED_EXTENSIONS = {'txt', 'pdf', 'docx', 'pptx', 'xlsx'}
MAX_CONTENT_LENGTH =
16 *
1024 *
1024 #
16MB
TEMP_FOLDER = "D
:/
Qwen1.5/temp"
```
### translator
.py
```python
from transformers import AutoModelForCausal
LM, AutoTokenizer
from config import Config
import torch
device = "cuda" if torch
.cuda
.is_av
ailable() else "
cpu"
class Translator
:
def __init__(self)
:
self
.tokenizer = AutoTokenizer
.from_pretr
ained(Config
.MODEL_PATH)
self
.model = AutoModelForCausal
LM.from_pretr
ained(
Config
.MODEL_PATH,
torch_dtype="auto",
device_map="auto"
)
def translate(self, text, source_lang, target_lang)
:
prompt = f"将以下{source_lang}文本翻译成{target_lang}
:\n{text}"
inputs = self
.tokenizer(prompt, return_tensors="pt")
.to(device)
outputs = self
.model
.generate(**inputs, max_new_tokens=
512)
return self
.tokenizer
.decode(outputs[0], skip_special_tokens=True)
```
### file_processor
.py
```python
import os
from config import Config
from translator import Translator
import docx
import PyPDF2
from pptx import Presentation
from openpyxl import load_workbook
translator = Translator()
def allowed_file(filename)
:
return '
.' in filename and filename
.rsplit('
.',
1)[
1]
.lower() in Config
.ALLOWED_EXTENSIONS
def process_file(filepath, source_lang, target_lang)
:
ext = os
.path
.splitext(filepath)[
1]
.lower()
if ext == '
.txt'
:
return process_txt(filepath, source_lang, target_lang)
elif ext == '
.docx'
:
return process_docx(filepath, source_lang, target_lang)
elif ext == '
.pdf'
:
return process_pdf(filepath, source_lang, target_lang)
elif ext == '
.pptx'
:
return process_pptx(filepath, source_lang, target_lang)
elif ext == '
.xlsx'
:
return process_xlsx(filepath, source_lang, target_lang)
else
:
r
aise ValueError("Unsupported file type")
def process_txt(filepath, source_lang, target_lang)
:
with open(filepath, 'r', encoding='utf
-8') as f
:
content = f
.read()
translated = translator
.translate(content, source_lang, target_lang)
return translated
.encode('utf
-8')
def process_docx(filepath, source_lang, target_lang)
:
doc = docx
.Document(filepath)
for para in doc
.paragraphs
:
if para
.text
.strip()
:
para
.text = translator
.translate(para
.text, source_lang, target_lang)
output_path = os
.path
.join(Config
.TEMP_FOLDER, "translated
.docx")
doc
.save(output_path)
return output_path
# 其他文件处理函数类似，限于篇幅省略
...
```
### m
ain
.py
```python
from flask import Flask, render_template, request, send_file, redirect, url_for
from file_processor import allowed_file, process_file
from config import Config
import os
app = Flask(__name__)
app
.config
.from_object(Config)
@app
.route('/', methods=['GET', 'POST'])
def index()
:
if request
.method == 'POST'
:
if 'file' not in request
.files
:
return redirect(request
.url)
file = request
.files['file']
if file
.filename == ''
:
return redirect(request
.url)
if file and allowed_file(file
.filename)
:
source_lang = request
.form
.get('source_lang', '中文')
target_lang = request
.form
.get('target_lang', '英文')
temp_path = os
.path
.join(Config
.TEMP_FOLDER, file
.filename)
file
.save(temp_path)
try
:
result = process_file(temp_path, source_lang, target_lang)
if isinstance(result, str)
: # 处理后的文件路径
return send_file(result, as_attachment=True)
else
: # 直接返回的字节内容
return send_file(
result,
as_attachment=True,
download_name=f"translated_{file
.filename}"
)
finally
:
if os
.path
.exists(temp_path)
:
os
.remove(temp_path)
return render_template('index
.html')
if __name__ == '__m
ain__'
:
os
.makedirs(Config
.TEMP_FOLDER, exist_ok=True)
app
.run(host=Config
.HOST, port=Config
.PORT)
```
### templates/index
.html
```html
<!DOCTYPE html>
<html>
<head>
<title>
AI翻译服务</title>
</head>
<body>
<h
1>文件翻译服务</h
1>
<form method="post" enctype="multipart/form
-data">
<div>
<label>源
语言:</label>
<select name="source_lang">
<option value="中文">中文</option>
<option value="英文">英文</option>
<option value="日文">日文</option>
<option value="法文">法文</option>
</select>
</div>
<div>
<label>目标
语言:</label>
<select name="target_lang">
<option value="英文">英文</option>
<option value="中文">中文</option>
<option value="日文">日文</option>
<option value="法文">法文</option>
</select>
</div>
<div>
<input type="file" name="file" required>
</div>
<button type="submit">翻译文件</button>
</form>
</body>
</html>
```
### requirements
.txt
```
flask>=2
.0
.0
transformers>=4
.30
.0
torch>=
1.10
.0
python
-docx>=0
.8
.10
PyPDF2>=2
.0
.0
python
-pptx>=0
.6
.18
openpyxl>=3
.0
.0
```
## 4
. 部署步骤
1. 创建上述目录结构
2
. 将代码文件放入对应位置
3
. 安装依赖
: `pip install
-r requirements
.txt`
4
. 运行服务
: `python m
ain
.py`
5. 内网其他电脑可通过 `http
://
10
.23
7.186
.51:5000` 访问
##
5. 注意事项
1. 确保NVIDIA驱动
和CUDA已正确安装
2
. 首次运行会下载
模型相关文件，请耐心等待
3
. 大文件翻译可能需要较长时间
4
. 确保防火墙允许
5000端口的访问
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