Upload folder using huggingface_hub

.gitattributes

@@ -34,3 +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
 minires.keras filter=lfs diff=lfs merge=lfs -text
+minires_xgb.json filter=lfs diff=lfs merge=lfs -text

minires.keras

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bd39d8bac5fbc4058646a1e7f1650921dc1ab445bb84bdfbb85815868588b174
+oid sha256:2ade3b309ca10520d90ca79c267d6472c6ee9ff4d0436e288d7ff4f14053e4f7
 size 9741158

minires.py

@@ -1,11 +1,11 @@
 # minires.py
 
 import json
-import joblib
 import numpy as np
 import pandas as pd
 from pathlib import Path
 from tensorflow.keras.models import load_model
+from xgboost import XGBRegressor 
 from huggingface_hub import hf_hub_download
 
 
@@ -16,8 +16,8 @@ from huggingface_hub import hf_hub_download
 REPO_ID = "nicolamustone/minires"
 
 NN_FILENAME   = "minires.keras"
-XGB_FILENAME  = "minires.joblib"
-META_FILENAME = "minires.json"
+XGB_FILENAME  = "minires_xgb.json"
+META_FILENAME = "minires_meta.json"
 
 
 # ---------------------------------------------------------------------
@@ -61,9 +61,10 @@ class minires:
             **download_kwargs,
         )
 
-        # For inference we don't need optimizer state
         self.nn = load_model(self.nn_path, compile=False)
-        self.xgb = joblib.load(self.xgb_path)
+        self.xgb = XGBRegressor()
+        
+        self.xgb.load_model(self.xgb_path)
 
         with open(self.meta_path, "r") as f:
             meta = json.load(f)

minires_meta.json

@@ -0,0 +1,13 @@
+{
+  "w_nn": 0.35000000000000003,
+  "features": [
+    "artist",
+    "volume",
+    "mass",
+    "volume_mass_interaction",
+    "surface_volume_ratio",
+    "surface_mass_ratio",
+    "base_mm",
+    "euler_number"
+  ]
+}

minires_xgb.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93399051e31020cdf57dba9dda2f51fcd55ca03ed78194877e8d70c3911471ca
+size 17584077