Upload app.py

app.py

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+"""
+VoiceAura Translation API
+Models:
+  1. SLPG/English_to_Urdu_Unsupervised_MT       (en → ur)
+  2. SLPG/Punjabi_Shahmukhi_to_Gurmukhi          (pa-s → pa-g)
+  3. SLPG/Punjabi_Gurmukhi_to_Shahmukhi          (pa-g → pa-s)
+"""
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import os, requests, argparse, torch
+
+# ✅ PyTorch 2.6 fix
+torch.serialization.add_safe_globals([argparse.Namespace])
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ── Model URLs ───────────────────────────────────────────
+MODELS_CONFIG = {
+    "en-ur": {
+        "files": {
+            "checkpoint_8_96000.pt": "https://huggingface.co/SLPG/English_to_Urdu_Unsupervised_MT/resolve/main/checkpoint_8_96000.pt",
+            "dict.en.txt":           "https://huggingface.co/SLPG/English_to_Urdu_Unsupervised_MT/resolve/main/dict.en.txt",
+            "dict.ur.txt":           "https://huggingface.co/SLPG/English_to_Urdu_Unsupervised_MT/resolve/main/dict.ur.txt",
+        },
+        "dir":        "models/en_ur",
+        "checkpoint": "checkpoint_8_96000.pt",
+        "instance":   None,
+    },
+    "pa-s-pa-g": {
+        "files": {
+            "checkpoint_5_78000.pt": "https://huggingface.co/SLPG/Punjabi_Shahmukhi_to_Gurmukhi_Transliteration/resolve/main/checkpoint_5_78000.pt",
+            "dict.pa.txt":           "https://huggingface.co/SLPG/Punjabi_Shahmukhi_to_Gurmukhi_Transliteration/resolve/main/dict.pa.txt",
+            "dict.pk.txt":           "https://huggingface.co/SLPG/Punjabi_Shahmukhi_to_Gurmukhi_Transliteration/resolve/main/dict.pk.txt",
+        },
+        "dir":        "models/pa_s_pa_g",
+        "checkpoint": "checkpoint_5_78000.pt",
+        "instance":   None,
+    },
+}
+
+# ── Helpers ──────────────────────────────────────────────
+def download_file(url: str, path: str):
+    if os.path.exists(path):
+        print(f"[✓] Exists: {path}")
+        return
+    print(f"[↓] Downloading: {path} ...")
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    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 patched_torch_load(*args, **kwargs):
+    kwargs["weights_only"] = False
+    return _original_torch_load(*args, **kwargs)
+
+_original_torch_load = torch.load
+
+def load_model(pair: str):
+    cfg = MODELS_CONFIG[pair]
+    if cfg["instance"] is not None:
+        return cfg["instance"]
+
+    # Download files
+    for fname, url in cfg["files"].items():
+        download_file(url, os.path.join(cfg["dir"], fname))
+
+    # Patch torch.load for fairseq
+    torch.load = patched_torch_load
+    from fairseq.models.transformer import TransformerModel
+    model = TransformerModel.from_pretrained(
+        cfg["dir"],
+        checkpoint_file=cfg["checkpoint"],
+        data_name_or_path=cfg["dir"],
+    )
+    torch.load = _original_torch_load
+    model.eval()
+    cfg["instance"] = model
+    print(f"[✓] Model ready: {pair}")
+    return model
+
+# ── Startup — load all models ────────────────────────────
+@app.on_event("startup")
+async def startup():
+    for pair in MODELS_CONFIG:
+        load_model(pair)
+
+# ── Endpoints ────────────────────────────────────────────
+class Req(BaseModel):
+    text: str
+    from_lang: str = "en"
+    to_lang: str   = "ur"
+
+@app.get("/")
+def root():
+    loaded = {k: MODELS_CONFIG[k]["instance"] is not None for k in MODELS_CONFIG}
+    return {"status": "VoiceAura API ✓", "models": loaded}
+
+@app.post("/translate")
+def translate(req: Req):
+    if not req.text.strip():
+        return {"success": False, "translation": ""}
+
+    pair = f"{req.from_lang}-{req.to_lang}"
+
+    if pair not in MODELS_CONFIG:
+        return {"success": False, "translation": f"⚠️ Pair '{pair}' not supported."}
+
+    try:
+        model  = load_model(pair)
+        result = model.translate(req.text.strip())
+        return {"success": True, "translation": result, "pair": pair}
+    except Exception as e:
+        print(f"Error [{pair}]: {e}")
+        return {"success": False, "translation": str(e)}