Spaces:
Sleeping
Sleeping
File size: 5,667 Bytes
74ce942 d5f15cb 74ce942 ea72d75 74ce942 ea72d75 74ce942 ea72d75 74ce942 ea72d75 74ce942 ea72d75 356174d ea72d75 74ce942 356174d 74ce942 176bc83 ea72d75 74ce942 ea72d75 74ce942 ea72d75 74ce942 ea72d75 74ce942 ea72d75 74ce942 d5f15cb 74ce942 d5f15cb ea72d75 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
from logging import getLogger
from pathlib import Path
import pandas as pd
import plotly.express as px
import streamlit as st
from st_aggrid import AgGrid, ColumnsAutoSizeMode, GridOptionsBuilder
from streamlit_plotly_events import plotly_events
from utilities import initialization
initialization()
# @st.cache(show_spinner=False)
# def initialize_state():
# with st.spinner("Loading app..."):
# if 'model' not in st.session_state:
# model = Top2Vec.load('models/model.pkl')
# model._check_model_status()
# model.hierarchical_topic_reduction(num_topics=20)
#
# st.session_state.model = model
# st.session_state.umap_model = joblib.load(proj_dir / 'models' / 'umap.sav')
# logger.info("loading data...")
#
# if 'data' not in st.session_state:
# logger.info("loading data...")
# data = pd.read_csv(proj_dir / 'data' / 'data.csv')
# data['topic_id'] = data['topic_id'].apply(lambda x: f'{x:02d}')
# st.session_state.data = data
# st.session_state.selected_data = data
# st.session_state.all_topics = list(data.topic_id.unique())
#
# if 'topics' not in st.session_state:
# logger.info("loading topics...")
# topics = pd.read_csv(proj_dir / 'data' / 'topics.csv')
# topics['topic_id'] = topics['topic_id'].apply(lambda x: f'{x:02d}')
# st.session_state.topics = topics
def reset():
logger.info("Resetting...")
st.session_state.selected_data = st.session_state.data
st.session_state.selected_points = []
def filter_df():
if st.session_state.selected_points:
points_df = pd.DataFrame(st.session_state.selected_points).loc[:, ['x', 'y']]
st.session_state.selected_data = st.session_state.data.merge(points_df, on=['x', 'y'])
logger.info(f"Updates selected_data: {len(st.session_state.selected_data)}")
else:
logger.info(f"Lame")
def reset():
st.session_state.selected_data = st.session_state.data
st.session_state.selected_points = []
def main():
st.write("""
# Topic Modeling
This shows a 2d representation of documents embeded in a semantic space. Each dot is a document
and the dots close represent documents that are close in meaning.
Zoom in and explore a topic of your choice. You can see the documents you select with the `lasso` or `box`
tool below in the corresponding tabs."""
)
st.button("Reset", help="Will Reset the selected points and the selected topics", on_click=reset)
data_to_model = st.session_state.data.sort_values(by='topic_id',
ascending=True) # to make legend sorted https://bioinformatics.stackexchange.com/a/18847
data_to_model['topic_id'].replace(st.session_state.topic_str_to_word, inplace=True)
fig = px.scatter(data_to_model, x='x', y='y', color='topic_id', template='plotly_dark',
hover_data=['id', 'topic_id', 'x', 'y'])
st.session_state.selected_points = plotly_events(fig, select_event=True, click_event=False)
filter_df()
tab1, tab2 = st.tabs(["Docs", "Topics"])
with tab1:
if st.session_state.selected_points:
filter_df()
cols = ['id', 'topic_id', 'documents']
data = st.session_state.selected_data[cols]
data['topic_word'] = data.topic_id.replace(st.session_state.topic_str_to_word)
ordered_cols = ['id', 'topic_id', 'topic_word', 'documents']
builder = GridOptionsBuilder.from_dataframe(data[ordered_cols])
builder.configure_pagination()
go = builder.build()
AgGrid(data[ordered_cols], theme='streamlit', gridOptions=go,
columns_auto_size_mode=ColumnsAutoSizeMode.FIT_CONTENTS)
else:
st.markdown('Select points in the graph with the `lasso` or `box` select tools to populate this table.')
def get_topics_counts() -> pd.DataFrame:
topic_counts = st.session_state.selected_data["topic_id"].value_counts().to_frame()
merged = topic_counts.merge(st.session_state.topics, left_index=True, right_on='topic_id')
cleaned = merged.drop(['topic_id_y'], axis=1).rename({'topic_id_x': 'topic_count'}, axis=1)
cols = ['topic_id'] + [col for col in cleaned.columns if col != 'topic_id']
return cleaned[cols]
with tab2:
if st.session_state.selected_points:
filter_df()
cols = ['topic_id', 'topic_count', 'topic_0']
topic_counts = get_topics_counts()
# st.write(topic_counts.columns)
builder = GridOptionsBuilder.from_dataframe(topic_counts[cols])
builder.configure_pagination()
builder.configure_column('topic_0', header_name='Topic Word', wrap_text=True)
go = builder.build()
AgGrid(topic_counts.loc[:, cols], theme='streamlit', gridOptions=go,
columns_auto_size_mode=ColumnsAutoSizeMode.FIT_ALL_COLUMNS_TO_VIEW)
else:
st.markdown('Select points in the graph with the `lasso` or `box` select tools to populate this table.')
if __name__ == "__main__":
# Setting up Logger and proj_dir
logger = getLogger(__name__)
proj_dir = Path(__file__).parents[2]
# For max width tables
pd.set_option('display.max_colwidth', 0)
# Streamlit settings
# st.set_page_config(layout="wide")
md_title = "# Document Explorer 📖"
st.markdown(md_title)
st.sidebar.markdown(md_title)
# initialize_state()
main()
|