Update app/model_loader.py

app/model_loader.py

@@ -1,11 +1,34 @@
-from huggingface_hub import hf_hub_download
+import os
 import tensorflow as tf
+from huggingface_hub import hf_hub_download
+
+# ✅ Force Hugging Face cache to a writable directory
+os.environ["HF_HUB_CACHE"] = "/tmp/huggingface"
+
+# ✅ Model directory and path
+MODEL_DIR = "/tmp/model"
+MODEL_PATH = os.path.join(MODEL_DIR, "efficientnetv2s.h5")
+
+REPO_ID = "Miguel764/efficientnetv2s-skin-cancer-classifier"
+FILENAME = "efficientnetv2s.h5"
 
 def load_model():
-    # Download the model file from the public repo
-    model_path = hf_hub_download(
-        repo_id="Miguel764/efficientnetv2s-skin-cancer-classifier",  # public repo
-        filename="efficientnetv2s.h5"  # file name inside the repo
-    )
-    # Load the downloaded model
-    return tf.keras.models.load_model(model_path)
+    # Create directories if not exist
+    os.makedirs(MODEL_DIR, exist_ok=True)
+    os.makedirs(os.environ["HF_HUB_CACHE"], exist_ok=True)
+
+    # ✅ Only download if the model is missing
+    if not os.path.exists(MODEL_PATH):
+        print("Model not found locally. Downloading from Hugging Face...")
+        downloaded_path = hf_hub_download(
+            repo_id=REPO_ID,
+            filename=FILENAME,
+            local_dir=MODEL_DIR,
+            local_dir_use_symlinks=False
+        )
+        print(f"Model downloaded to: {downloaded_path}")
+    else:
+        print("Model already exists locally.")
+
+    # ✅ Load the model safely
+    return tf.keras.models.load_model(MODEL_PATH)