【problem analysis】Error: “app_name” is not translated in af

本文详细介绍了在使用AndroidTools进行应用签名时遇到本地化问题的解决方法,包括修改本地化设置和调整Lint错误提示。通过将关键字符串设置为无需翻译或调整错误提示严重级别,开发者可以避免在应用发布前遇到不必要的障碍。


为了生成应用签名,使用Android Tools>Export Signed Application Package

提示了错误:

"app_name" is not translated in af, am, ar, be, bg, ca, cs, da, de, el, en-rGB, en-rIN, es, es-rUS, et, et-rEE, fa, fi, fr, fr-rCA, hi, hr, hu, hy-rAM, in, it, iw, ja, ka-rGE, km-rKH, ko, lo-rLA, lt, lv, mn-rMN, ms, ms-rMY, nb, nl, pl, pt, pt-rBR, pt-rPT, ro, ru, sk, sl, sr, sv, sw, th, tl, tr, uk, vi, zh-rCN, zh-rHK, zh-rTW, zu

原因:

资源文件本地化问题

解决一:不需要本地化 设置为 不需要翻译 translatable="false"

<?xml version="1.0" encoding="utf-8"?>
<resources>

    <string name="app_name" translatable="false">考神</string>
    <string name="hello_world" translatable="false">Hello world!</string>

</resources>

解决二:设置lint的错误提示

In your ADT go to window->Preferences->Android->Lint Error Checking

Find there Missing Translation and change its Severity to Warning.

"Window" > "Preferences" > "Android" > "Lint Error Checking"

You should be able to disable

"Run full error check when exporting app and abort if fatal errors are found".



import os import datetime from fastapi import FastAPI, UploadFile, File, Request from fastapi.responses import HTMLResponse from fastapi.staticfiles import StaticFiles from fastapi.templating import Jinja2Templates from transformers import AutoModelForCausalLM, AutoTokenizer from utils.web_searcher import search_with_bocha from utils.file_processor import process_uploaded_file import torch # 初始化FastAPI app = FastAPI() app.mount("/static", StaticFiles(directory="static"), name="static") templates = Jinja2Templates(directory="templates") # 加载模型 model_path = "D:/Qwen2.5/models/qwen/Qwen1.5-7B-Chat" device = "cuda" if torch.cuda.is_available() else "cpu" # 量化配置以节省显存 load_kwargs = { "torch_dtype": torch.float16, "device_map": "auto" } tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path, **load_kwargs,load_in_8bit=True) def get_current_time(): """获取当前本地时间""" return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") def generate_response(prompt, search_results=None): """生成AI响应""" current_time = get_current_time() # 构建系统提示 system_prompt = f"""你是一个智能助手,当前时间是{current_time}。 你具备翻译功能,当用户要求翻译时,你需要提供高质量的翻译结果。 """ if search_results: system_prompt += f"\n以下是网络搜索结果:\n{search_results}\n请基于这些信息回答。" messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt} ] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" ).to(device) outputs = model.generate( inputs, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.9 ) response = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) return response @app.get("/", response_class=HTMLResponse) async def chat_interface(request: Request): return templates.TemplateResponse("chat.html", {"request": request}) @app.post("/chat") async def chat_endpoint(request: Request): form_data = await request.form() user_input = form_data.get("message", "") use_search = form_data.get("use_search", False) search_results = None if use_search: search_results = search_with_bocha(user_input) response = generate_response(user_input, search_results) return {"response": response} @app.post("/upload") async def upload_file(file: UploadFile = File(...)): file_path = f"data/uploads/{file.filename}" with open(file_path, "wb") as f: f.write(await file.read()) translated_content = process_uploaded_file(file_path) return {"filename": file.filename, "translated_content": translated_content} if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000)
10-11
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