Update app/configs.py

app/configs.py

@@ -4,40 +4,34 @@ from pathlib import Path
 from typing import Dict, Any, List
 import os
 
+# Import custom artifact repository
+import sys
+sys.path.insert(0, "/app")
+try:
+    from hf_artifact_repo import HuggingFaceArtifactRepository
+except ImportError:
+    print("⚠️ Custom artifact repo not found, using default MLflow")
+
 # ======================
 # MLflow Configuration
 # ======================
 MLFLOW_URI = os.getenv("MLFLOW_TRACKING_URI", "https://omarelrayes-mlflow-server.hf.space")
 mlflow.set_tracking_uri(MLFLOW_URI)
 
-# HuggingFace Hub Configuration for Artifacts
-# ⚠️ IMPORTANT: HF_TOKEN must be set in Space Settings (not hardcoded!)
+# HuggingFace Hub Configuration
 HF_TOKEN = os.getenv("HF_TOKEN")
 if not HF_TOKEN:
-    raise ValueError(
-        "HF_TOKEN environment variable is not set! "
-        "Please add it in HuggingFace Space Settings -> Variables and secrets"
-    )
+    raise ValueError("HF_TOKEN environment variable is not set!")
 os.environ["HF_TOKEN"] = HF_TOKEN
 
-# ======================
-# Model URIs (UPDATE WITH YOUR NEW RUN IDs)
-# ======================
-# ⚠️ IMPORTANT: Replace these with the actual run IDs from registration
+# Model URIs
 CLASSIFICATION_RUN_ID = os.getenv("CLASSIFICATION_RUN_ID")
 SEGMENTATION_RUN_ID = os.getenv("SEGMENTATION_RUN_ID")
 
 if not CLASSIFICATION_RUN_ID:
-    raise ValueError(
-        "CLASSIFICATION_RUN_ID environment variable is not set! "
-        "Please add it in HuggingFace Space Settings -> Variables and secrets"
-    )
-
+    raise ValueError("CLASSIFICATION_RUN_ID not set!")
 if not SEGMENTATION_RUN_ID:
-    raise ValueError(
-        "SEGMENTATION_RUN_ID environment variable is not set! "
-        "Please add it in HuggingFace Space Settings -> Variables and secrets"
-    )
+    raise ValueError("SEGMENTATION_RUN_ID not set!")
 
 CLASSIFICATION_MODEL_URI = f"runs:/{CLASSIFICATION_RUN_ID}/model"
 SEGMENTATION_MODEL_URI = f"runs:/{SEGMENTATION_RUN_ID}/model"
@@ -67,24 +61,7 @@ def get_classification_model():
             )
             print("✅ Classification Model Loaded Successfully!")
         except Exception as e:
-            print(f"❌ Error loading classification model: {e}")
-            print("\n🔍 Debugging Info:")
-            print(f"   - Tracking URI: {mlflow.get_tracking_uri()}")
-            print(f"   - Model URI: {CLASSIFICATION_MODEL_URI}")
-            
-            # Try to list available runs
-            try:
-                from mlflow.tracking import MlflowClient
-                client = MlflowClient()
-                runs = client.search_runs(experiment_names=["skin_cancer_classification"])
-                print(f"   - Available runs: {[run.info.run_id for run in runs]}")
-                
-                if runs:
-                    artifacts = client.list_artifacts(runs[0].info.run_id)
-                    print(f"   - Artifacts in first run: {[a.path for a in artifacts]}")
-            except Exception as debug_err:
-                print(f"   - Debug error: {debug_err}")
-            
+            print(f"❌ Error: {e}")
             raise
 
     return _classification_model
@@ -99,7 +76,6 @@ def get_segmentation_model():
         print("="*60)
         print("🔄 Loading Segmentation Model via MLflow...")
         print(f"🔗 URI: {SEGMENTATION_MODEL_URI}")
-        print(f"🌐 Tracking URI: {MLFLOW_URI}")
         print("="*60)
 
         try:
@@ -108,46 +84,9 @@ def get_segmentation_model():
             )
             print("✅ Segmentation Model Loaded Successfully!")
         except Exception as e:
-            print(f"❌ Error loading segmentation model: {e}")
-            print("\n🔍 Debugging Info:")
-            print(f"   - Tracking URI: {mlflow.get_tracking_uri()}")
-            print(f"   - Model URI: {SEGMENTATION_MODEL_URI}")
-            
-            # Try to list available runs
-            try:
-                from mlflow.tracking import MlflowClient
-                client = MlflowClient()
-                runs = client.search_runs(experiment_names=["skin_cancer_segmentation"])
-                print(f"   - Available runs: {[run.info.run_id for run in runs]}")
-                
-                if runs:
-                    artifacts = client.list_artifacts(runs[0].info.run_id)
-                    print(f"   - Artifacts in first run: {[a.path for a in artifacts]}")
-            except Exception as debug_err:
-                print(f"   - Debug error: {debug_err}")
-            
+            print(f"❌ Error: {e}")
             raise
 
     return _segmentation_model
 
-# ======================
-# 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!")
+# ... rest of the code