Update app.py

app.py

@@ -1,19 +1,5 @@
 """
 Arabic Sign Language Interpreter - FastAPI Server (Optimized)
-
-Pipeline:
-  Image Input
-    ──► YOLO Detection        (hand crop)
-    ──► WiLoR 3D Pose         (extract 3D joints + MANO params)
-    ──► Stage-1: classifier.pkl    →  "letter" or "number"?
-    ──► Stage-2: MLP_letters.pkl   →  specific Arabic letter
-             OR  MLP_numbers.pkl   →  specific digit
-    ──► JSON Response
-
-Modes (set MODE env var):
-  full        : YOLO + WiLoR FP32  + MLP   (~1.1–2.5 GB)
-  quantized   : YOLO + WiLoR INT8  + MLP   (~600 MB–1.2 GB)
-  lightweight : MediaPipe           + MLP   (~50 MB)
 """
 
 import io
@@ -22,6 +8,7 @@ import inspect
 import sys
 import os
 import types
+import shutil
 from unittest.mock import MagicMock
 
 import numpy as np
@@ -39,6 +26,7 @@ from fastapi import FastAPI, File, UploadFile, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
 import uvicorn
+from huggingface_hub import hf_hub_download
 
 # ─── Runtime mode ──────────────────────────────────────────────────────────────
 MODE = os.environ.get("MODE", "full").lower()
@@ -71,41 +59,49 @@ for _mod in ["OpenGL", "OpenGL.GL", "OpenGL.GL.framebufferobjects",
 
 os.environ["PYOPENGL_PLATFORM"] = "osmesa"
 
-# ─── Configuration ─────────────────────────────────────────────────────────────
-WILOR_REPO_PATH  = "./WiLoR"
-WILOR_CKPT       = "./WiLoR/pretrained_models/wilor_final.ckpt"
-WILOR_CFG        = "./WiLoR/pretrained_models/model_config.yaml"
-CLASSIFIER_PATH  = "./classifier.pkl"
-MLP_LETTERS_PATH = "./MLP_letters.pkl"
-MLP_NUMBERS_PATH = "./MLP_numbers.pkl"
-DEVICE           = "cuda" if torch.cuda.is_available() else "cpu"
+# ─── Hugging Face Model Integration ───────────────────────────────────────────
+REPO_ID = "SondosM/api_GP"
+
+def get_hf_file(filename, is_mano=False):
+    print(f"Downloading {filename} from {REPO_ID}...")
+    temp_path = hf_hub_download(repo_id=REPO_ID, filename=filename)
+
+    if is_mano:
+        os.makedirs("./mano_data", exist_ok=True)
+        target_path = os.path.join("./mano_data", os.path.basename(filename))
+        if not os.path.exists(target_path):
+            shutil.copy(temp_path, target_path)
+            print(f"Copied {filename} to {target_path}")
+        return target_path
+
+    return temp_path
+
+# ─── Download all required files at startup ───────────────────────────────────
+print("Initializing model file paths...")
+
+# MANO files
+get_hf_file("mano_data/mano_data/mano_mean_params.npz", is_mano=True)
+get_hf_file("mano_data/mano_data/MANO_LEFT.pkl",        is_mano=True)
+get_hf_file("mano_data/mano_data/MANO_RIGHT.pkl",       is_mano=True)
+
+# Model weights
+WILOR_REPO_PATH   = "./WiLoR"
+WILOR_CKPT        = get_hf_file("pretrained_models/pretrained_models/wilor_final.ckpt")
+WILOR_CFG         = get_hf_file("pretrained_models/pretrained_models/model_config.yaml")
+DETECTOR_PATH     = get_hf_file("pretrained_models/pretrained_models/detector.pt")
+
+# Classifiers
+CLASSIFIER_PATH   = get_hf_file("classifier.pkl")
+MLP_LETTERS_PATH  = get_hf_file("MLP_letters.pkl")
+MLP_NUMBERS_PATH  = get_hf_file("MLP_numbers.pkl")
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 
 WILOR_TRANSFORM = transforms.Compose([
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 ])
 
-def _resolve_detector_path() -> str:
-    candidates = [
-        "./detector.pt",
-        "./detector",
-        "./pretrained_models/detector.pt",
-        "./pretrained_models/detector",
-        "./MANO/detector.pt",
-        "./WiLoR/detector.pt",
-    ]
-    for c in candidates:
-        if Path(c).exists():
-            print(f"[INFO] Detector found at: {c}")
-            return c
-    import glob
-    found = glob.glob("/app/**/detector.pt", recursive=True) + glob.glob("/app/**/detector", recursive=True)
-    if found:
-        print(f"[INFO] Detector found at: {found[0]}")
-        return found[0]
-    all_files = list(Path("/app").rglob("detector*"))
-    raise FileNotFoundError(f"Detector not found! Searched everywhere. Found these detector* files: {all_files}")
-
 # ─── Global model handles ──────────────────────────────────────────────────────
 wilor_model    = None
 yolo_detector  = None
@@ -174,10 +170,9 @@ def load_models():
         mp_hands_model = _load_mediapipe()
         print("✅ MediaPipe loaded.")
     else:
-        detector_path = _resolve_detector_path()
         from ultralytics import YOLO
-        print(f"[INFO] Loading YOLO detector from {detector_path} ...")
-        yolo_detector = YOLO(detector_path)
+        print(f"[INFO] Loading YOLO detector from {DETECTOR_PATH} ...")
+        yolo_detector = YOLO(DETECTOR_PATH)
         print("[INFO] YOLO loaded.")
 
         if MODE == "full":