Upload 4 files

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README (1).md

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+---
+title: VoiceAura Translation API
+emoji: 🌐
+colorFrom: blue
+colorTo: green
+sdk: docker
+pinned: false
+---

app.py

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+"""
+VoiceAura Translation API
+HuggingFace Space pe deploy karo — SLPG model use karta hai
+"""
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import os
+import requests
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ── Sahi file names (HF repo se match karte hain) ────────
+MODEL_URL   = "https://huggingface.co/SLPG/English_to_Urdu_Unsupervised_MT/resolve/main/checkpoint_8_96000.pt"
+DICT_EN_URL = "https://huggingface.co/SLPG/English_to_Urdu_Unsupervised_MT/resolve/main/dict.en.txt"
+DICT_UR_URL = "https://huggingface.co/SLPG/English_to_Urdu_Unsupervised_MT/resolve/main/dict.ur.txt"
+
+MODEL_PATH   = "checkpoint_8_96000.pt"   # ✅ Sahi naam
+DICT_EN_PATH = "dict.en.txt"
+DICT_UR_PATH = "dict.ur.txt"
+
+en_ur_model = None
+
+def download_file(url: str, path: str):
+    if os.path.exists(path):
+        print(f"[✓] Already exists: {path}")
+        return
+    print(f"[↓] Downloading: {path}...")
+    with requests.get(url, stream=True) as r:
+        r.raise_for_status()
+        with open(path, "wb") as f:
+            for chunk in r.iter_content(chunk_size=8192):
+                f.write(chunk)
+    print(f"[✓] Done: {path}")
+
+def load_model():
+    global en_ur_model
+    if en_ur_model is not None:
+        return
+
+    download_file(MODEL_URL,   MODEL_PATH)
+    download_file(DICT_EN_URL, DICT_EN_PATH)
+    download_file(DICT_UR_URL, DICT_UR_PATH)
+
+    from fairseq.models.transformer import TransformerModel
+    en_ur_model = TransformerModel.from_pretrained(
+        ".",
+        checkpoint_file=MODEL_PATH,  # ✅ Sahi naam
+        data_name_or_path="."
+    )
+    en_ur_model.eval()
+    print("[✓] Model ready!")
+
+@app.on_event("startup")
+async def startup():
+    load_model()
+
+class Req(BaseModel):
+    text: str
+
+@app.get("/")
+def root():
+    return {"status": "VoiceAura API ✓", "model_loaded": en_ur_model is not None}
+
+@app.post("/translate")
+def translate(req: Req):
+    if not req.text.strip():
+        return {"success": False, "translation": ""}
+    try:
+        result = en_ur_model.translate(req.text.strip())
+        return {"success": True, "translation": result}
+    except Exception as e:
+        return {"success": False, "translation": str(e)}

requirements.txt

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+fastapi
+uvicorn[standard]
+fairseq
+torch
+requests