Update app/configs.py

app/configs.py

@@ -1,17 +1,15 @@
 # configs.py
+import os
+os.environ["PYTHONPATH"] = "/app:" + os.environ.get("PYTHONPATH", "")
+
 import tensorflow as tf
+import keras
 from pathlib import Path
 from typing import Dict, Any, List
-import os
 from huggingface_hub import hf_hub_download
 
 # ======================
-# 🔥 Set PYTHONPATH for sitecustomize.py
-# ======================
-os.environ["PYTHONPATH"] = "/app:" + os.environ.get("PYTHONPATH", "")
-
-# ======================
-# 🔥 Import HF Plugin (for MLflow compatibility)
+# 🔥 Import HF Plugin
 # ======================
 try:
     from hf_artifact_repo import HuggingFaceArtifactRepository
@@ -32,7 +30,7 @@ os.environ["HF_TOKEN"] = HF_TOKEN
 # ======================
 HF_REPO = "omarelrayes/mlflow-artifacts"
 
-# 🔥 المسارات الثابتة من التسجيل السابق (موثوقة 100%)
+# 🔥 المسارات الثابتة (من التسجيل السابق)
 CLASSIFICATION_MODEL_PATH = "1/models/m-2fa9b990eeac4e18b925616422228223/artifacts/data/model.keras"
 SEGMENTATION_MODEL_PATH = "2/models/m-21ba32f804234073952d1a8e590958ec/artifacts/data/model.keras"
 
@@ -42,6 +40,7 @@ print(f"   - HF Repository: {HF_REPO}")
 print(f"   - Classification Path: {CLASSIFICATION_MODEL_PATH}")
 print(f"   - Segmentation Path: {SEGMENTATION_MODEL_PATH}")
 print(f"   - TensorFlow version: {tf.__version__}")
+print(f"   - Keras version: {keras.__version__}")
 print("="*60)
 
 # ======================
@@ -59,12 +58,10 @@ def get_classification_model():
     if _classification_model is None:
         print("="*60)
         print("🔄 Loading Classification Model...")
-        print(f"📦 Repository: {HF_REPO}")
-        print(f"📁 Path: {CLASSIFICATION_MODEL_PATH}")
         print("="*60)
 
         try:
-            # 🔥 Download directly from HuggingFace Hub
+            # 🔥 Download from HuggingFace Hub
             model_file = hf_hub_download(
                 repo_id=HF_REPO,
                 filename=CLASSIFICATION_MODEL_PATH,
@@ -75,10 +72,11 @@ def get_classification_model():
             
             print(f"📥 Model downloaded to: {model_file}")
             
-            # 🔥 Load model with Keras
-            _classification_model = tf.keras.models.load_model(
+            # 🔥 🔥 🔥 CRITICAL: Use keras.saving.load_model with safe_mode=False
+            _classification_model = keras.saving.load_model(
                 model_file,
-                compile=False
+                compile=False,
+                safe_mode=False  # 🔥 تجاوز مشكلة quantization_config
             )
             
             print("✅ Classification Model Loaded Successfully!")
@@ -99,12 +97,10 @@ def get_segmentation_model():
     if _segmentation_model is None:
         print("="*60)
         print("🔄 Loading Segmentation Model...")
-        print(f"📦 Repository: {HF_REPO}")
-        print(f"📁 Path: {SEGMENTATION_MODEL_PATH}")
         print("="*60)
 
         try:
-            # 🔥 Download directly from HuggingFace Hub
+            # 🔥 Download from HuggingFace Hub
             model_file = hf_hub_download(
                 repo_id=HF_REPO,
                 filename=SEGMENTATION_MODEL_PATH,
@@ -115,10 +111,11 @@ def get_segmentation_model():
             
             print(f"📥 Model downloaded to: {model_file}")
             
-            # 🔥 Load model with Keras
-            _segmentation_model = tf.keras.models.load_model(
+            # 🔥 🔥 🔥 CRITICAL: Use keras.saving.load_model with safe_mode=False
+            _segmentation_model = keras.saving.load_model(
                 model_file,
-                compile=False
+                compile=False,
+                safe_mode=False  # 🔥 تجاوز مشكلة quantization_config
             )
             
             print("✅ Segmentation Model Loaded Successfully!")
@@ -135,19 +132,4 @@ def get_segmentation_model():
 # ======================
 model_classes = {
     0: "benign",
-    1: "malignant"
-}
-
-request_history: List[Dict[str, Any]] = []
-
-# ======================
-# Storage Directories
-# ======================
-STORAGE_DIR = Path("storage")
-IMAGES_DIR = STORAGE_DIR / "images"
-SEGMENTS_DIR = STORAGE_DIR / "segments"
-
-for d in [STORAGE_DIR, IMAGES_DIR, SEGMENTS_DIR]:
-    d.mkdir(parents=True, exist_ok=True)
-
-print("✅ Models module initialized successfully!")
+