Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import re
 import inspect
+import numpy as np
 
 from sentence_transformers import SentenceTransformer
 from sentence_transformers.util import cos_sim
@@ -86,7 +87,6 @@ codes = """001 - Pendaftaran Kendaraan
 009 - Penangguhan atau Deklarasi Perubahan Penggunaan Kendaraan""".split("\n")
 
 
-
 examples = [
     {"code": "001", "examples": [
         "Register a new vehicle",
@@ -305,7 +305,11 @@ model_id = model_ids[-1]
 model = SentenceTransformer(model_id, trust_remote_code=True)
 
 # codes_emb = model.encode([x[6:] for x in codes])
-codes_emb = model.encode([x["examples"] for x in examples])#.mean(axis=1)
+# codes_emb = model.encode([x["examples"] for x in examples])#.mean(axis=1)
+codes_emb = np.mean([model.encode(x["examples"]) for x in examples], axis=1)
+# for x in examples:
+#   codes_emb.append(model.encode(x["examples"]))
+# codes_emb = np.mean(codes_emb, axis=1)
 
 
 def censor_middle(number, num_to_hide=4):
@@ -586,7 +590,8 @@ def reload(chosen_model_id):
         model = SentenceTransformer(chosen_model_id, trust_remote_code=True)
         model_id = chosen_model_id
         # codes_emb = model.encode([x[6:] for x in codes])
-        codes_emb = model.encode([x["examples"] for x in examples])#.mean(axis=1)
+        # codes_emb = model.encode([x["examples"] for x in examples])#.mean(axis=1)
+        codes_emb = np.mean([model.encode(x["examples"]) for x in examples], axis=1)
         return f"Model {chosen_model_id} has been succesfully loaded!"
     return f"Model {chosen_model_id} is ready!"