Update app/configs.py

app/configs.py

@@ -1,41 +1,66 @@
+# configs.py
 import mlflow
 import mlflow.tensorflow
+import tensorflow as tf
 from pathlib import Path
 from typing import Dict, Any, List
 import os
 
-# Import custom artifact repository
-import sys
-sys.path.insert(0, "/app")
+# ======================
+# 🔥 CRITICAL: Set PYTHONPATH for sitecustomize.py
+# ======================
+os.environ["PYTHONPATH"] = "/app:" + os.environ.get("PYTHONPATH", "")
+
+# ======================
+# 🔥 Import HF Plugin (will be auto-registered by sitecustomize.py)
+# ======================
 try:
     from hf_artifact_repo import HuggingFaceArtifactRepository
-except ImportError:
-    print("⚠️ Custom artifact repo not found, using default MLflow")
+    print("✅ HuggingFace Artifact Repository loaded!")
+except ImportError as e:
+    print(f"⚠️ Could not load HF plugin: {e}")
 
 # ======================
-# MLflow Configuration
+# Environment Variables
 # ======================
-MLFLOW_URI = os.getenv("MLFLOW_TRACKING_URI", "https://omarelrayes-mlflow-server.hf.space")
-mlflow.set_tracking_uri(MLFLOW_URI)
-
-# HuggingFace Hub Configuration
 HF_TOKEN = os.getenv("HF_TOKEN")
 if not HF_TOKEN:
-    raise ValueError("HF_TOKEN environment variable is not set!")
+    raise ValueError(
+        "❌ HF_TOKEN environment variable is not set! "
+        "Please add it in HuggingFace Space Settings -> Variables and secrets"
+    )
 os.environ["HF_TOKEN"] = HF_TOKEN
 
-# Model URIs
-CLASSIFICATION_RUN_ID = os.getenv("CLASSIFICATION_RUN_ID")
-SEGMENTATION_RUN_ID = os.getenv("SEGMENTATION_RUN_ID")
+MLFLOW_TRACKING_URI = os.getenv(
+    "MLFLOW_TRACKING_URI", 
+    "https://omarelrayes-mlflow-server.hf.space"
+)
+
+CLASSIFICATION_RUN_ID = os.getenv(
+    "CLASSIFICATION_RUN_ID",
+    "330d8a6678574cb3ae2caa296d927a6d"  # 🔥 Your new Run ID
+)
 
-if not CLASSIFICATION_RUN_ID:
-    raise ValueError("CLASSIFICATION_RUN_ID not set!")
-if not SEGMENTATION_RUN_ID:
-    raise ValueError("SEGMENTATION_RUN_ID not set!")
+SEGMENTATION_RUN_ID = os.getenv(
+    "SEGMENTATION_RUN_ID",
+    "c321c4e944064127a1198e781c83cad8"  # 🔥 Your new Run ID
+)
+
+# ======================
+# MLflow Configuration
+# ======================
+mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
 
 CLASSIFICATION_MODEL_URI = f"runs:/{CLASSIFICATION_RUN_ID}/model"
 SEGMENTATION_MODEL_URI = f"runs:/{SEGMENTATION_RUN_ID}/model"
 
+print("="*60)
+print("🔧 MLflow Configuration:")
+print(f"   - Tracking URI: {MLFLOW_TRACKING_URI}")
+print(f"   - Classification Run ID: {CLASSIFICATION_RUN_ID}")
+print(f"   - Segmentation Run ID: {SEGMENTATION_RUN_ID}")
+print("="*60)
+
 # ======================
 # Cached models
 # ======================
@@ -52,7 +77,7 @@ def get_classification_model():
         print("="*60)
         print("🔄 Loading Classification Model via MLflow...")
         print(f"🔗 URI: {CLASSIFICATION_MODEL_URI}")
-        print(f"🌐 Tracking URI: {MLFLOW_URI}")
+        print(f"🌐 Tracking URI: {MLFLOW_TRACKING_URI}")
         print("="*60)
 
         try:
@@ -61,7 +86,9 @@ def get_classification_model():
             )
             print("✅ Classification Model Loaded Successfully!")
         except Exception as e:
-            print(f"❌ Error: {e}")
+            print(f"❌ Error loading classification model: {e}")
+            import traceback
+            traceback.print_exc()
             raise
 
     return _classification_model
@@ -84,9 +111,31 @@ def get_segmentation_model():
             )
             print("✅ Segmentation Model Loaded Successfully!")
         except Exception as e:
-            print(f"❌ Error: {e}")
+            print(f"❌ Error loading segmentation model: {e}")
+            import traceback
+            traceback.print_exc()
             raise
 
     return _segmentation_model
 
-# ... rest of the code
+# ======================
+# Model Classes
+# ======================
+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!")