Update api.py

api.py

@@ -134,14 +134,41 @@ class DermFoundationNeuralNetwork:
             self.confidence_scaler = model_data['confidence_scaler']
             self.weighted_scaler = model_data['weighted_scaler']
             
+            # Get the original keras model path from pickle
             keras_model_path = model_data['keras_model_path']
-            if os.path.exists(keras_model_path):
-                self.model = tf.keras.models.load_model(keras_model_path)
-                return True
+            
+            # If the path doesn't exist, try looking in the same directory as the pickle file
+            if not os.path.exists(keras_model_path):
+                print(f"Original keras path not found: {keras_model_path}")
+                
+                # Get the directory where the pickle file is located
+                pickle_dir = os.path.dirname(os.path.abspath(filepath))
+                
+                # Extract just the filename from the original path
+                keras_filename = os.path.basename(keras_model_path)
+                
+                # Try looking for it in the same directory as the pickle
+                alternative_path = os.path.join(pickle_dir, keras_filename)
+                print(f"Trying alternative path: {alternative_path}")
+                
+                if os.path.exists(alternative_path):
+                    keras_model_path = alternative_path
+                    print(f"✓ Found keras model at: {keras_model_path}")
+                else:
+                    print(f"✗ Keras model not found at alternative path either")
+                    return False
             else:
-                return False
+                print(f"✓ Found keras model at original path: {keras_model_path}")
+            
+            # Load the keras model
+            self.model = tf.keras.models.load_model(keras_model_path)
+            print(f"✓ Keras model loaded successfully")
+            return True
+            
         except Exception as e:
             print(f"Error loading model: {e}")
+            import traceback
+            traceback.print_exc()
             return False
     
     def predict(self, embedding):