Update app/configs.py

app/configs.py

@@ -1,13 +1,15 @@
 # app/configs.py
 import os
-os.environ["PYTHONPATH"] = "/app:" + os.environ.get("PYTHONPATH", "")
-
-import tensorflow as tf
+import uuid
 from pathlib import Path
 from typing import Dict, Any, List
+from datetime import datetime
 from huggingface_hub import hf_hub_download
 import zipfile
 import shutil
+import tensorflow as tf
+
+os.environ["PYTHONPATH"] = "/app:" + os.environ.get("PYTHONPATH", "")
 
 try:
     from hf_artifact_repo import HuggingFaceArtifactRepository
@@ -34,6 +36,34 @@ print("="*60)
 _classification_model = None
 _segmentation_model = None
 
+# 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)
+
+model_classes = {
+    0: "benign",
+    1: "malignant"
+}
+
+request_history: List[Dict[str, Any]] = []
+
+def save_image(image_bytes: bytes, filename: str) -> str:
+    """Save uploaded image to storage"""
+    image_path = IMAGES_DIR / filename
+    with open(image_path, 'wb') as f:
+        f.write(image_bytes)
+    return str(image_path)
+
+def save_segmentation(mask_image, request_id: str) -> str:
+    """Save segmentation mask"""
+    segmentation_path = SEGMENTS_DIR / f"{request_id}_mask.png"
+    mask_image.save(segmentation_path, format='PNG')
+    return str(segmentation_path)
+
 def download_and_extract_savedmodel(zip_path_in_repo, extract_dir):
     print(f"📥 Downloading {zip_path_in_repo}...")
     
@@ -117,18 +147,4 @@ def get_segmentation_model():
 
     return _segmentation_model
 
-model_classes = {
-    0: "benign",
-    1: "malignant"
-}
-
-request_history: List[Dict[str, Any]] = []
-
-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!")