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Dockerfile

@@ -1,35 +1,14 @@
-
-FROM ubuntu:20.04
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN apt update && apt install -y \
-    python3 python3-pip unzip curl git build-essential cmake libboost-all-dev \
-    && rm -rf /var/lib/apt/lists/*
-
-RUN pip3 install --no-cache-dir fastapi uvicorn[standard] gradio requests langcodes
+FROM python:3.9
 
 RUN useradd -m -u 1000 user
 USER user
+ENV PATH="/home/user/.local/bin:$PATH"
 
-# 下载多个模型
-RUN mkdir -p /home/user/models && \
-    curl -L "https://github.com/xxnuo/MTranServer/releases/download/models/enzh.zip" -o /home/user/models/enzh.zip && \
-    curl -L "https://github.com/xxnuo/MTranServer/releases/download/models/zhen.zip" -o /home/user/models/zhen.zip && \
-    curl -L "https://github.com/xxnuo/MTranServer/releases/download/models/enja.zip" -o /home/user/models/enja.zip && \
-    unzip /home/user/models/enzh.zip -d /home/user/models/enzh && \
-    unzip /home/user/models/zhen.zip -d /home/user/models/zhen && \
-    unzip /home/user/models/enja.zip -d /home/user/models/enja && \
-    rm /home/user/models/*.zip
+WORKDIR /home/user/app
 
-COPY --chown=user main.py /home/user/main.py
-COPY --chown=user gradio_app.py /home/user/gradio_app.py
+COPY --chown=user . .
 
-WORKDIR /home/user
+RUN pip install --no-cache-dir -r requirements.txt
 
-EXPOSE 8989
 EXPOSE 7860
-
-CMD bash -c "\
-    python3 /home/user/main.py & \
-    python3 /home/user/gradio_app.py"
+CMD ["python", "gradio_app.py"]

README.md

@@ -1,17 +1,3 @@
----
-title: MTranServer Full UI
-emoji: 🌍
-colorFrom: green
-colorTo: blue
-sdk: docker
-pinned: true
----
+# MTranServer HF Python 版
 
-# 🌍 MTranServer - 完整版本（多语言 + Gradio UI + Token + 插件支持）
-
-支持：
-- 多语言模型选择
-- 自动语言代码识别（zh-CN, en-US）
-- 插件接口支持（kiss/imme/hcfy）
-- 文件翻译（txt）
-- Gradio 前端 UI
+这是一个基于 Hugging Face Transformers 的英文翻译服务器，支持 Gradio 界面。

gradio_app.py

@@ -1,61 +1,11 @@
-
 import gradio as gr
 import requests
-import os
-
-API_URL = "http://localhost:8989/translate"
-FILE_URL = "http://localhost:8989/translate/file"
-API_TOKEN = os.environ.get("CORE_API_TOKEN", "")
-
-LANGUAGE_PAIRS = {
-    "English → Chinese": ("en", "zh"),
-    "Chinese → English": ("zh", "en"),
-    "English → Japanese": ("en", "ja")
-}
-
-def translate_text(text, direction):
-    src, tgt = LANGUAGE_PAIRS[direction]
-    headers = {"Content-Type": "application/json"}
-    if API_TOKEN:
-        headers["Authorization"] = API_TOKEN
-    payload = {"from": src, "to": tgt, "text": text}
-    try:
-        r = requests.post(API_URL, json=payload, headers=headers, timeout=10)
-        r.raise_for_status()
-        return r.json().get("result", "")
-    except Exception as e:
-        return f"翻译失败：{str(e)}"
-
-def translate_file(file, direction):
-    if file is None:
-        return "请上传文件"
-    src, tgt = LANGUAGE_PAIRS[direction]
-    headers = {}
-    if API_TOKEN:
-        headers["Authorization"] = API_TOKEN
-    with open(file.name, "rb") as f:
-        files = {"file": (file.name, f, "text/plain")}
-        r = requests.post(FILE_URL + f"?from_lang={src}&to_lang={tgt}", files=files, headers=headers)
-        try:
-            r.raise_for_status()
-            return r.json().get("result", "")
-        except Exception as e:
-            return f"文件翻译失败：{str(e)}"
 
-with gr.Blocks() as demo:
-    gr.Markdown("🌐 **MTranServer 多语言翻译 UI**")
-    with gr.Tab("文本翻译"):
-        text = gr.Textbox(label="输入文本", lines=5)
-        lang = gr.Radio(choices=list(LANGUAGE_PAIRS.keys()), label="语言方向", value="English → Chinese")
-        output = gr.Textbox(label="翻译结果")
-        btn = gr.Button("翻译")
-        btn.click(translate_text, inputs=[text, lang], outputs=output)
+def translate_text(text):
+    response = requests.post("http://localhost:7860/translate", json={"text": text})
+    return response.json().get("result", "Error")
 
-    with gr.Tab("文件翻译"):
-        file = gr.File(label="上传 .txt 文件")
-        lang2 = gr.Radio(choices=list(LANGUAGE_PAIRS.keys()), label="语言方向", value="English → Chinese")
-        output2 = gr.Textbox(label="翻译结果")
-        btn2 = gr.Button("翻译文件")
-        btn2.click(translate_file, inputs=[file, lang2], outputs=output2)
+demo = gr.Interface(fn=translate_text, inputs="text", outputs="text", title="英文翻译（Hugging Face 模型）")
 
-demo.launch(server_name="0.0.0.0", server_port=7860)
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

main.py

@@ -1,90 +1,20 @@
-
-from fastapi import FastAPI, Request, HTTPException, UploadFile, File
-import os
-import subprocess
-import uvicorn
-import langcodes
+from fastapi import FastAPI, Request
+from transformers import MarianMTModel, MarianTokenizer
 
 app = FastAPI()
-API_TOKEN = os.environ.get("CORE_API_TOKEN", "")
-MODEL_ROOT = "/home/user/models"
-SUPPORTED_LANGS = {
-    ("en", "zh"): "enzh",
-    ("zh", "en"): "zhen",
-    ("en", "ja"): "enja"
-}
-
-def normalize_lang(code):
-    try:
-        return langcodes.standardize_tag(code).split("-")[0]
-    except:
-        return code.lower().split("-")[0]
-
-def check_token(req: Request):
-    if API_TOKEN:
-        token = req.headers.get("Authorization", "")
-        if token != API_TOKEN:
-            raise HTTPException(status_code=401, detail="Unauthorized")
 
-def translate_by_subprocess(text, src, tgt):
-    src = normalize_lang(src)
-    tgt = normalize_lang(tgt)
-    model_key = SUPPORTED_LANGS.get((src, tgt))
-    if not model_key:
-        raise HTTPException(status_code=400, detail=f"Unsupported language pair: {src} → {tgt}")
-    model_path = os.path.join(MODEL_ROOT, model_key)
-    result = subprocess.run(
-        ["mtranclient", "--from", src, "--to", tgt, "--text", text, "--model-dir", model_path],
-        capture_output=True, text=True
-    )
-    if result.returncode != 0:
-        raise HTTPException(status_code=500, detail="Translation failed")
-    return result.stdout.strip()
+model_name = "hf_model"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
 
 @app.post("/translate")
-async def api_translate(req: Request):
-    check_token(req)
-    data = await req.json()
-    return {"result": translate_by_subprocess(data["text"], data["from"], data["to"])}
-
-@app.post("/translate/batch")
-async def api_batch(req: Request):
-    check_token(req)
-    data = await req.json()
-    texts = data.get("texts", [])
-    results = [translate_by_subprocess(t, data["from"], data["to"]) for t in texts]
-    return {"results": results}
-
-@app.post("/translate/file")
-async def translate_file(file: UploadFile = File(...), from_lang: str = "en", to_lang: str = "zh"):
-    contents = await file.read()
-    text = contents.decode("utf-8")
-    result = translate_by_subprocess(text, from_lang, to_lang)
-    return {"filename": file.filename, "result": result}
-
-@app.get("/models")
-def get_models():
-    return {"models": list(SUPPORTED_LANGS.values())}
-
-@app.get("/health")
-def health():
-    return {"status": "ok"}
-
-@app.post("/kiss")
-@app.post("/imme")
-@app.post("/hcfy")
-@app.post("/language/translate/v2")
-async def plugin_translate(req: Request):
-    check_token(req)
-    data = await req.json()
-    src = data.get("source") or data.get("from") or "auto"
-    tgt = data.get("target") or data.get("to") or "zh"
-    text = data.get("q") or data.get("text")
-    return {
-        "data": {
-            "translations": [{"translatedText": translate_by_subprocess(text, src, tgt)}]
-        }
-    }
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8989)
+async def translate(request: Request):
+    data = await request.json()
+    src_text = data.get("text", "")
+    if not src_text:
+        return {"error": "Empty text"}
+
+    inputs = tokenizer([src_text], return_tensors="pt", padding=True, truncation=True)
+    translated = model.generate(**inputs)
+    output = tokenizer.batch_decode(translated, skip_special_tokens=True)
+    return {"result": output[0]}

requirements.txt

@@ -1,5 +1,6 @@
 fastapi
-uvicorn[standard]
-requests
+uvicorn
+transformers
+torch
 gradio
-langcodes
+requests

space.yaml

@@ -0,0 +1,8 @@
+---
+title: MTranServer-HF
+emoji: 🚀
+colorFrom: indigo
+colorTo: pink
+sdk: docker
+app_file: gradio_app.py
+pinned: false