Add application file

app.py

@@ -1,4 +1,183 @@
 import streamlit as st
+import pandas as pd
+import sys
+import os
+from datasets import load_from_disk, load_dataset
+from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import time
+from annotated_text import annotated_text
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+from huggingface_hub import hf_hub_download
+repo_id = "friendshipkim/IUR_Reddit"
+
+# ABSOLUTE_PATH = os.path.dirname(__file__)
+# ASSETS_PATH = os.path.join(ABSOLUTE_PATH, 'model_assets')
+
+@st.cache_data
+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
+def get_pairwise_distances(model):
+    # df = pd.read_csv(f"{ASSETS_PATH}/{model}/pairwise_distances.csv").set_index('index')
+    df = pd.read_csv(hf_hub_download(repo_id=repo_id, filename="pairwise_distances.csv")).set_index('index')
+    return df
+
+@st.cache_data
+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
+def get_query_strings():
+    # df = pd.read_json(f"{ASSETS_PATH}/IUR_Reddit_test_queries_english.jsonl", lines = True)
+    df = pd.read_json(hf_hub_download(repo_id=repo_id, filename="IUR_Reddit_test_queries_english.jsonl"), lines = True)
+    df['index'] = df.reset_index().index
+    return df
+	# df['partition'] = df['index']%100
+	# 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
+def get_candidate_strings():
+    # df = pd.read_json(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.jsonl", lines = True)
+    df = pd.read_json(hf_hub_download(repo_id=repo_id, filename="IUR_Reddit_test_candidates_english.jsonl"), lines = True)
+    df['index'] = df.reset_index().index
+    return df
+	# 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
+def get_embedding_dataset(model):
+    # data = load_from_disk(f"{ASSETS_PATH}/{model}/embedding")
+    data = load_dataset("friendshipkim/luar_clone2_top_100_embedding")
+    return data
+
+@st.cache_data
+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
+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
+def get_candidate_text(l):
+	return get_candidate_strings().at[l,'fullText']
+
+@st.cache_data
+def get_annotated_text(text, word, pos):
+	# print("here", word, pos)
+	start= text.index(word, pos)
+	end = start+len(word)
+	return (text[:start], (text[start:end ], 'SELECTED'), text[end:]), end
+
+class AgGridBuilder:
+	__static_key = 0
+	def build_ag_grid(table, display_columns):
+		AgGridBuilder.__static_key += 1
+		options_builder = GridOptionsBuilder.from_dataframe(table[display_columns])
+		options_builder.configure_pagination(paginationAutoPageSize=False, paginationPageSize=10)
+		options_builder.configure_selection(selection_mode= 'single', pre_selected_rows = [0])
+		options = options_builder.build()
+		return AgGrid(table, gridOptions = options, fit_columns_on_grid_load=True, key = AgGridBuilder.__static_key, reload_data = True, update_mode = GridUpdateMode.SELECTION_CHANGED | GridUpdateMode.VALUE_CHANGED)
+
+if __name__ == '__main__':
+	st.set_page_config(layout="wide")
+
+	# models = filter(lambda file_name: os.path.isdir(f"{ASSETS_PATH}/{file_name}") and not file_name.endswith(".parquet"), os.listdir(ASSETS_PATH))
+	models = ['luar_clone2_top_100']
+ 
+	with st.sidebar:
+		current_model = st.selectbox(
+			"Select Model to analyze", 
+			models
+			)
+
+	pairwise_distances = get_pairwise_distances(current_model)
+	embedding_dataset = get_embedding_dataset(current_model)
+
+	candidate_string_grid = None
+	gt_candidate_string_grid  = None
+	with st.container():
+		t1 = time.time()
+		st.title("Full Text")
+		col1, col2 = st.columns([14, 2])
+		t2 = time.time()
+		query_table = get_bad_queries(current_model)
+		t3 = time.time()
+		# print(query_table)
+		with col2:
+			index = st.number_input('Enter Query number to inspect', min_value = 0, max_value = query_table.shape[0], step = 1)
+			query_text = query_table.loc[index]['fullText']
+			preprocessed_query_text = preprocess_text(query_text)
+			text_highlight_index = st.number_input('Enter word #', min_value = 0, max_value = len(preprocessed_query_text), step = 1)
+			query_index = int(query_table.iloc[index]['index'])
+
+		with col1:
+			if 'pos_highlight' not in st.session_state or text_highlight_index == 0:
+				st.session_state['pos_highlight'] = text_highlight_index
+				st.session_state['pos_history'] = [0]
+
+			if st.session_state['pos_highlight'] > text_highlight_index:
+				st.session_state['pos_history'] = st.session_state['pos_history'][:-2]
+				if len(st.session_state['pos_history']) == 0:
+					st.session_state['pos_history'] = [0]
+			# print("pos", st.session_state['pos_history'], st.session_state['pos_highlight'], text_highlight_index)
+			anotated_text_, pos = get_annotated_text(query_text, preprocessed_query_text[text_highlight_index-1], st.session_state['pos_history'][-1]) if text_highlight_index >= 1 else ((query_text), 0)
+			if st.session_state['pos_highlight'] < text_highlight_index:
+				st.session_state['pos_history'].append(pos)
+			st.session_state['pos_highlight'] = text_highlight_index
+			annotated_text(*anotated_text_)
+			# annotated_text("Lol, this" , ('guy', 'SELECTED') , "is such a PR chameleon. \n\n In the Chan Zuckerberg Initiative announcement, he made it sound like he was giving away all his money to charity <PERSON> or <PERSON>. http://www.businessinsider.in/Mark-Zuckerberg-says-hes-giving-99-of-his-Facebook-shares-45-billion-to-charity/articleshow/50005321.cms Apparently, its just a VC fund. And there are still people out there who believe Facebook.org was an initiative to bring Internet to the poor.")
+			t4 = time.time()
+
+		# print(f"query time query text: {t3-t2}, total time: {t4-t1}")
+	with st.container():
+		st.title("Top 16 Recommended Candidates")
+		col1, col2, col3 = st.columns([10, 4, 2])	
+		rec_candidates = pairwise_distances[pairwise_distances["queries"]==query_index]['candidates']
+		# print(rec_candidates)
+		l = list(rec_candidates)
+		with col3:
+			candidate_rec_index = st.number_input('Enter recommended candidate number to inspect', min_value = 0, max_value = len(l), step = 1)
+			print("l:",l, query_index)
+			pairwise_candidate_index = int(l[candidate_rec_index])
+		with col1:
+			st.header("Text")
+			t1 = time.time()
+			st.write(get_candidate_text(pairwise_candidate_index))
+			t2 = time.time()
+		with col2:
+			st.header("Cosine Distance")
+			st.write(float(pairwise_distances[\
+				( pairwise_distances['queries'] == query_index ) \
+				&
+				( pairwise_distances['candidates'] == pairwise_candidate_index)]['distances']))
+		print(f"candidate string retreival: {t2-t1}")
+	with st.container():
+		t1 = time.time()
+		st.title("Candidates With Same Authors As Query")
+		col1, col2, col3 = st.columns([10, 4, 2])
+		t2 = time.time()
+		gt_candidates = get_gt_candidates(current_model, query_table.iloc[query_index]['authorIDs'][0])
+		t3 = time.time()
+
+		with col3:
+			candidate_index = st.number_input('Enter ground truthnumber to inspect', min_value = 0, max_value = gt_candidates.shape[0], step = 1)
+			gt_candidate_index = int(gt_candidates.iloc[candidate_index]['index'])
+		with col1:
+			st.header("Text")
+			st.write(gt_candidates.iloc[candidate_index]['fullText'])
+		with col2:
+			t4 = time.time()
+			st.header("Cosine Distance")
+			st.write(1-cosine_similarity(np.array([embedding_dataset['queries'][query_index]['embedding']]), np.array([embedding_dataset['candidates'][gt_candidate_index]['embedding']]))[0,0])
+			t5 = time.time()
+		print(f"find gt candidates: {t3-t2}, find cosine: {t5-t4}, total: {t5-t1}")