Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import plotly.express as px
 import pandas as pd
 import random
+import logging
 from umap import UMAP
 from sentence_transformers import SentenceTransformer, util
 from datasets import load_dataset
@@ -18,19 +19,19 @@ def load_words_dataset():
     return dataset["Word"]
 
 
-@st.cache_resource
-def prepare_umap():
-    all_enc = model.encode(all_words)
-    umap_3d = UMAP(n_components=3, init='random', random_state=0)
-    proj_3d = umap_3d.fit_transform(random.sample(all_enc.tolist(), k=2000))
-    return umap_3d
+# @st.cache_resource
+# def prepare_umap():
+#     all_enc = model.encode(all_words)
+#     umap_3d = UMAP(n_components=3, init='random', random_state=0)
+#     proj_3d = umap_3d.fit_transform(random.sample(all_enc.tolist(), k=2000))
+#     return umap_3d
 
 
 all_words = load_words_dataset()
 
 model = load_model()
 
-umap_3d = prepare_umap()
+#umap_3d = prepare_umap()
 
 
 secret_word = random.choice(all_words)
@@ -49,7 +50,8 @@ if 'words_umap_df' not in st.session_state:
         "s": [],
         "l": [],
     })
-    secret_embedding_3d = umap_3d.transform([secret_embedding])[0]
+    #secret_embedding_3d = umap_3d.transform([secret_embedding])[0]
+    secret_embedding_3d = [0, 1, 2]
     words_umap_df.loc[len(words_umap_df)] = {
         "x": secret_embedding_3d[0],
         "y": secret_embedding_3d[1],
@@ -78,7 +80,8 @@ if st.button("Guess") or word:
         ).cpu().numpy()[0][0]
         st.session_state['words'].append((str(word), similarity))
 
-        pt = umap_3d.transform([word_embedding])[0]
+        #pt = umap_3d.transform([word_embedding])[0]
+        pt = [0, 1, 2]
         words_umap_df = st.session_state['words_umap_df']
         words_umap_df.loc[len(words_umap_df)] = {
             "x": pt[0],