cache_data to cache

app.py

@@ -11,7 +11,7 @@ from annotated_text import annotated_text
 
 ORG_ID = "cornell-authorship"
 
-@st.cache_data
+@st.cache
 def preprocess_text(s):
     return list(filter(lambda x: x!= '', (''.join(c if c.isalnum() or c == ' ' else ' ' for c in s)).split(' ')))
 
@@ -21,7 +21,7 @@ def get_pairwise_distances(model):
     df = pd.DataFrame(dataset).set_index('index')
     return df
 
-@st.cache_data
+@st.cache
 def get_pairwise_distances_chunked(model, chunk):
     # for df in pd.read_csv(f"{ASSETS_PATH}/{model}/pairwise_distances.csv", chunksize = 16):
     # 	print(df.iloc[0]['queries'])
@@ -29,7 +29,7 @@ def get_pairwise_distances_chunked(model, chunk):
     # 		return df
     return get_pairwise_distances(model)
 
-@st.cache_data
+@st.cache
 def get_query_strings():
     # df = pd.read_json(hf_hub_download(repo_id=repo_id, filename="IUR_Reddit_test_queries_english.jsonl"), lines = True)
     dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_queries_english")["train"]
@@ -41,7 +41,7 @@ def get_query_strings():
     
     # return pd.read_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_queries_english.parquet", columns=['fullText', 'index', 'authorIDs'])
 
-@st.cache_data
+@st.cache
 def get_candidate_strings():
     # df = pd.read_json(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.jsonl", lines = True)
     dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_candidates_english")["train"]
@@ -52,28 +52,28 @@ def get_candidate_strings():
     # df.to_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.parquet", index = 'index', partition_cols = 'partition')
     # return pd.read_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.parquet", columns=['fullText', 'index', 'authorIDs'])
 
-@st.cache_data
+@st.cache
 def get_embedding_dataset(model):
     # data = load_from_disk(f"{ASSETS_PATH}/{model}/embedding")
     data = load_dataset(f"{ORG_ID}/{model}_embedding")
     return data
 
-@st.cache_data
+@st.cache
 def get_bad_queries(model):
     df = get_query_strings().iloc[list(get_pairwise_distances(model)['queries'].unique())][['fullText', 'index', 'authorIDs']]
     return df
 
-@st.cache_data
+@st.cache
 def get_gt_candidates(model, author):
     gt_candidates = get_candidate_strings()
     df = gt_candidates[gt_candidates['authorIDs'].apply(lambda x: x[0]) == author]
     return df
 
-@st.cache_data
+@st.cache
 def get_candidate_text(l):
     return get_candidate_strings().at[l,'fullText']
 
-@st.cache_data
+@st.cache
 def get_annotated_text(text, word, pos):
     # print("here", word, pos)
     start= text.index(word, pos)