wrap up

.gitattributes

@@ -34,4 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 *.csv filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+__pycache__/
+ *.pyc

src/prediction_pipeline.py

@@ -19,19 +19,32 @@ from src.mhnfs.model import MHNfs
 
 class ActivityPredictor:
     
-    def __init__(self):
+    def __init__(self, streamlit=True):
 
-        @st.cache_resource # Caching for streamlit
-        def load_model():
-            pl.seed_everything(1234)
-            current_loc = __file__.rsplit("/",2)[0]
-            model = MHNfs.load_from_checkpoint(current_loc +
-                                                    "/assets/mhnfs_data/"
-                                                    "mhnfs_checkpoint.ckpt")
-            model._update_context_set_embedding()
-            model.eval()
-    
-            return model
+        if streamlit:
+            @st.cache_resource # Caching for streamlit
+            def load_model():
+                pl.seed_everything(1234)
+                current_loc = __file__.rsplit("/",2)[0]
+                model = MHNfs.load_from_checkpoint(current_loc +
+                                                        "/assets/mhnfs_data/"
+                                                        "mhnfs_checkpoint.ckpt")
+                model._update_context_set_embedding()
+                model.eval()
+        
+                return model
+        else:
+            def load_model():
+                pl.seed_everything(1234)
+                current_loc = __file__.rsplit("/",2)[0]
+                model = MHNfs.load_from_checkpoint(current_loc +
+                                                        "/assets/mhnfs_data/"
+                                                        "mhnfs_checkpoint.ckpt")
+                model._update_context_set_embedding()
+                model.eval()
+        
+                return model
+            
         
         # Load model
         self.model = load_model()
@@ -55,7 +68,12 @@ class ActivityPredictor:
         support_inactives_input, support_inactives_size = create_support_set_input(
             support_inactives_smiles
         )
-        
+
+        # save inputs
+        import pickle
+        with open("/system/user/publicwork/luukkonen/mhnfs-benchmark/js_code/preprocess_data/ap_inputs.pkl", "wb") as f:
+            pickle.dump((query_input, support_actives_input, support_inactives_input, support_actives_size, support_inactives_size), f)
+
         # Make predictions
         predictions = self.model(
             query_input,