fix

app.py

@@ -18,7 +18,7 @@ from nltk.data import find
 import nltk
 import gensim
 
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def get_embed_model():
   nltk.download("word2vec_sample")
   word2vec_sample = str(find('models/word2vec_sample/pruned.word2vec.txt'))
@@ -26,7 +26,7 @@ def get_embed_model():
   model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_sample, binary=False)
   return model
 
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def get_top_n_closest(query_word, candidate, n):
   model = get_embed_model()
   t = time.time()
@@ -44,7 +44,7 @@ def get_top_n_closest(query_word, candidate, n):
   top = [p_c[i] for i in sorted]
   return  top
 
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def annotate_text(text, words):
   annotated = [text]
   for word in words:
@@ -63,23 +63,23 @@ def annotate_text(text, words):
   return tuple(annotated)
 
 
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def preprocess_text(s):
  return list(filter(lambda x: x!= '', (''.join(c if c.isalnum() or c == ' ' else ' ' for c in s)).split(' ')))
 
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def get_pairwise_distances(model):
 	df = pd.read_csv(f"{ASSETS_PATH}/{model}/pairwise_distances.csv").set_index('index')
 	return df
 
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 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'])
 	# 	if chunk == int(df.iloc[0]['queries']):
 	# 		return df
 	return get_pairwise_distances(model)
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def get_query_strings():
 	df = pd.read_json(f"{ASSETS_PATH}/IUR_Reddit_test_queries_english.jsonl", lines = True)
 	df['index'] = df.reset_index().index
@@ -88,7 +88,7 @@ def get_query_strings():
 	# df.to_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_queries_english.parquet", index = 'index', partition_cols = 'partition')
 	
 	# return pd.read_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_queries_english.parquet", columns=['fullText', 'index', 'authorIDs'])
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def get_candidate_strings():
 	df = pd.read_json(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.jsonl", lines = True)
 	df['i'] = df['index']
@@ -99,24 +99,24 @@ def get_candidate_strings():
 	# df['partition'] = df['index']%100
 	# 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(suppress_st_warning=True, allow_output_mutation=True)
 def get_embedding_dataset(model):
 	data = load_from_disk(f"{ASSETS_PATH}/{model}/embedding")
 	return data
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 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(suppress_st_warning=True, allow_output_mutation=True)
 def get_gt_candidates(model, author):
 	gt_candidates = get_candidate_strings()
 	df = gt_candidates[gt_candidates['authorIDs'] == author]
 	return df
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def get_candidate_text(l):
 	return get_candidate_strings().at[l,'fullText']
 
-@st.cache_data
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
 def get_annotated_text(text, word, pos):
 	print("here", word, pos)
 	start= text.index(word, pos)

requirements.txt

@@ -4,4 +4,3 @@ pandas==1.5.2
 st-annotated-text==3.0.0
 nltk==3.8.1
 gensim==4.3.1
-streamlit==1.20.0