Fix: Pin numpy<2 and handle model load errors gracefully

app.py

@@ -157,7 +157,15 @@ def load_model():
     idx_to_class = {v: k for k, v in class_to_idx.items()}
     return model, idx_to_class
 
-MODEL, IDX_TO_CLASS = load_model()
+MODEL = None
+IDX_TO_CLASS = {}
+LOAD_ERROR = None
+
+try:
+    MODEL, IDX_TO_CLASS = load_model()
+except Exception as e:
+    LOAD_ERROR = str(e)
+    print(f"Failed to load model: {e}")
 
 # ============================================================================
 # Prediction
@@ -165,8 +173,10 @@ MODEL, IDX_TO_CLASS = load_model()
 
 def predict(strokes_json):
     """Predict from JSON stroke data."""
+    if LOAD_ERROR:
+        return {"error": f"Model load failed: {LOAD_ERROR}"}
     if MODEL is None:
-        return {"error": "Model not loaded"}
+        return {"error": "Model not loaded (unknown reason)"}
     
     try:
         raw_strokes = json.loads(strokes_json) if isinstance(strokes_json, str) else strokes_json

requirements.txt

@@ -1,3 +1,3 @@
 gradio>=4.0.0
 torch>=2.0.0
-numpy>=1.24.0
+numpy>=1.24.0,<2.0.0