Update app.py

app.py

@@ -11,27 +11,54 @@ LOCAL_MODEL_PATH = "./maritime_classifier"
 
 # Load model
 print("Loading model...")
+print(f"MODEL_PATH: {MODEL_PATH}")
+print(f"LOCAL_MODEL_PATH: {LOCAL_MODEL_PATH}")
+model = None
+
 try:
-    # Try Hugging Face Hub first
+    # Check if MODEL_PATH is a Hugging Face repo (contains "/" and doesn't exist locally)
     if "/" in MODEL_PATH and not Path(MODEL_PATH).exists():
+        print(f"Loading from Hugging Face Hub: {MODEL_PATH}")
+        model = SetFitModel.from_pretrained(MODEL_PATH)
+        print(f"✓ Successfully loaded model from Hugging Face: {MODEL_PATH}")
+    # Check if local model path exists
+    elif Path(LOCAL_MODEL_PATH).exists():
+        print(f"Loading from local path: {LOCAL_MODEL_PATH}")
+        model = SetFitModel.from_pretrained(LOCAL_MODEL_PATH)
+        print(f"✓ Successfully loaded model from local path: {LOCAL_MODEL_PATH}")
+    # If MODEL_PATH is a local path that exists
+    elif Path(MODEL_PATH).exists():
+        print(f"Loading from local path: {MODEL_PATH}")
         model = SetFitModel.from_pretrained(MODEL_PATH)
-        print(f"✓ Loaded model from Hugging Face: {MODEL_PATH}")
+        print(f"✓ Successfully loaded model from local path: {MODEL_PATH}")
+    # Default: try MODEL_PATH as Hugging Face repo
     else:
-        # Try local path
-        if Path(LOCAL_MODEL_PATH).exists():
-            model = SetFitModel.from_pretrained(LOCAL_MODEL_PATH)
-            print(f"✓ Loaded model from local path: {LOCAL_MODEL_PATH}")
-        else:
-            raise FileNotFoundError(f"Model not found at {MODEL_PATH} or {LOCAL_MODEL_PATH}")
+        print(f"Attempting to load from Hugging Face Hub: {MODEL_PATH}")
+        model = SetFitModel.from_pretrained(MODEL_PATH)
+        print(f"✓ Successfully loaded model from Hugging Face: {MODEL_PATH}")
 except Exception as e:
-    print(f"⚠️  Error loading model: {e}")
-    print("Make sure the model is trained or uploaded to Hugging Face")
+    print(f"❌ Error loading model: {e}")
+    print(f"   Attempted paths:")
+    print(f"   - Hugging Face: {MODEL_PATH}")
+    print(f"   - Local: {LOCAL_MODEL_PATH}")
+    import traceback
+    print("\nFull traceback:")
+    traceback.print_exc()
     model = None
 
+if model is None:
+    print("\n⚠️  WARNING: Model failed to load. The app will not work correctly.")
+    print("   Please check:")
+    print(f"   1. Model exists at: https://huggingface.co/{MODEL_PATH}")
+    print("   2. Internet connection is available")
+    print("   3. All dependencies are installed (setfit, sentence-transformers, etc.)")
+else:
+    print("\n✅ Model loaded successfully! Ready for inference.")
+
 def predict_text(text):
     """Predict whether text is actionable (YES) or not (NO)."""
     if model is None:
-        return "Error: Model not loaded. Please train the model first.", 0.0, "error"
+        return "Error: Model not loaded. Please check the console logs.", 0.0, "error"
     
     if not text or not text.strip():
         return "Please enter some text to classify.", 0.0, "neutral"
@@ -52,7 +79,11 @@ def predict_text(text):
         
         return label, confidence, status
     except Exception as e:
-        return f"Error during prediction: {str(e)}", 0.0, "error"
+        error_msg = f"Error during prediction: {str(e)}"
+        print(error_msg)
+        import traceback
+        traceback.print_exc()
+        return error_msg, 0.0, "error"
 
 def get_explanation(status):
     """Get explanation based on prediction status."""