diff --git a/logic/models/run.go b/logic/models/run.go
index 5e9844a..decb7c5 100644
--- a/logic/models/run.go
+++ b/logic/models/run.go
@@ -70,7 +70,7 @@ func runModelExp(c *Context, model *BaseModel, def_id string, inputImage *tf.Ten
     base_model := tg.LoadModel(path.Join("savedData", model.Id, "defs", def_id, "base", "model"), []string{"serve"}, nil)
 
 	//results := base_model.Exec([]tf.Output{
-	base_model.Exec([]tf.Output{
+    base_results := base_model.Exec([]tf.Output{
 		base_model.Op("StatefulPartitionedCall", 0),
 	}, map[tf.Output]*tf.Tensor{
 		//base_model.Op("serving_default_rescaling_input", 0): inputImage,
@@ -87,6 +87,27 @@ func runModelExp(c *Context, model *BaseModel, def_id string, inputImage *tf.Ten
         return
     }
 
+    var vmax float32 = 0.0
+
+    for _, element := range heads {
+        head_model := tg.LoadModel(path.Join("savedData", model.Id, "defs", def_id, "head", element.Id, "model"), []string{"serve"}, nil)
+
+        results := head_model.Exec([]tf.Output{
+            head_model.Op("StatefulPartitionedCall", 0),
+        }, map[tf.Output]*tf.Tensor{
+            head_model.Op("serving_default_input_2", 0): base_results[0],
+        })
+
+        var predictions = results[0].Value().([][]float32)[0]
+
+        for i, v := range predictions {
+            if v > vmax {
+                order = element.Range_start + i
+                vmax = v
+            }
+        }
+    }
+
     // TODO runthe head model
 
     c.Logger.Info("Got", "heads", len(heads))