bugfix

main.py

@@ -46,15 +46,28 @@ app.add_middleware(
 
 ANIMALS = ['Cat', 'Dog', 'Panda'] # Animal names here, these represent the labels of the images that we trained our model on.
 
-# 1) download your SavedModel from the Hub:
-# so refer to the repository where your model is, not the one for the space! 
+# 1) download your SavedModel from the Hub into a writable directory (Spaces often
+# run with the repository checkout read-only). Prefer TMPDIR or /tmp.
 repo_id = "IDS75912/masterclass-2025"
-hf_hub_download(repo_id, filename="config.json", repo_type="model", local_dir="./model")
-hf_hub_download(repo_id, filename="metadata.json", repo_type="model", local_dir="./model")
-hf_hub_download(repo_id, filename="model.weights.h5", repo_type="model", local_dir="./model")
+local_model_dir = os.environ.get('HF_MODEL_DIR') or os.environ.get('TMPDIR') or '/tmp/model'
+
+# Ensure the directory exists and is writable. If creating fails, raise a clear error.
+try:
+    os.makedirs(local_model_dir, exist_ok=True)
+except PermissionError:
+    raise RuntimeError(f"Cannot create model directory '{local_model_dir}'. Ensure the process has write access or set HF_MODEL_DIR to a writable path.")
+
+# download files into local_model_dir
+hf_hub_download(repo_id, filename="config.json", repo_type="model", local_dir=local_model_dir)
+hf_hub_download(repo_id, filename="metadata.json", repo_type="model", local_dir=local_model_dir)
+hf_hub_download(repo_id, filename="model.weights.h5", repo_type="model", local_dir=local_model_dir)
 
 # 2) load it
-model = tf.keras.models.load_model("./model")
+# If the hub returns an unpacked model folder, point load_model at that directory.
+try:
+    model = tf.keras.models.load_model(local_model_dir)
+except (IOError, OSError, ValueError) as e:
+    raise RuntimeError(f"Failed to load model from '{local_model_dir}': {e}")
 
 
 
@@ -76,7 +89,7 @@ async def uploadImage(img: UploadFile = File(...)):
 @app.get("/")
 def read_root() -> Dict[str, Any]:
     """Root endpoint."""
-    return {"message": "Hello from FastAPI in the 'aai9' conda env"}
+    return {"message": "Hello from FastAPI in "}
 
 
 # @app.post("/echo")