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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 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
#
# st.session_state.selected_points = []
def main():
max_docs = st.sidebar.slider("# docs", 10, 100, value=50)
to_search = st.text_input("Write your query here", "") or ""
with st.spinner('Embedding Query...'):
vector = st.session_state.model.embed([to_search])
with st.spinner('Dimension Reduction...'):
point = st.session_state.umap_model.transform(vector.reshape(1, -1))
documents, document_scores, document_ids = st.session_state.model.search_documents_by_vector(vector.flatten(),
num_docs=max_docs)
st.session_state.search_raw_df = pd.DataFrame({'document_ids': document_ids, 'document_scores': document_scores})
st.session_state.data_to_model = st.session_state.data.merge(st.session_state.search_raw_df, left_on='id',
right_on='document_ids').drop(['document_ids'], axis=1)
st.session_state.data_to_model = st.session_state.data_to_model.sort_values(by='document_scores',
ascending=False) # to make legend sorted https://bioinformatics.stackexchange.com/a/18847
st.session_state.data_to_model.loc[len(st.session_state.data_to_model.index)] = ['Point', *point[0].tolist(),
to_search, 'Query', 0]
st.session_state.data_to_model_with_point = st.session_state.data_to_model
st.session_state.data_to_model_without_point = st.session_state.data_to_model.iloc[:-1]
def get_topics_counts() -> pd.DataFrame:
topic_counts = st.session_state.data_to_model_without_point["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]
st.write("""
# Semantic Search
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.
Note that the distance metrics were computed at a higher dimension so take the representation with
a grain of salt.
The Query is shown with the documents in yellow.
"""
)
df = st.session_state.data_to_model_with_point.sort_values(by='topic_id', ascending=True)
fig = px.scatter(df.iloc[:-1], x='x', y='y', color='topic_id', template='plotly_dark',
hover_data=['id', 'topic_id', 'x', 'y'])
fig.add_traces(px.scatter(df.tail(1), x="x", y="y").update_traces(marker_size=10, marker_color="yellow").data)
st.plotly_chart(fig, use_container_width=True)
tab1, tab2 = st.tabs(["Docs", "Topics"])
with tab1:
cols = ['id', 'document_scores', 'topic_id', 'documents']
data = st.session_state.data_to_model_without_point.loc[:, cols]
data['topic_word'] = data.topic_id.replace(st.session_state.topic_str_to_word)
ordered_cols = ['id', 'document_scores', 'topic_id', 'topic_word', 'documents']
builder = GridOptionsBuilder.from_dataframe(data[ordered_cols])
builder.configure_pagination()
builder.configure_column('document_scores', type=["numericColumn", "numberColumnFilter", "customNumericFormat"],
precision=2)
go = builder.build()
AgGrid(data[ordered_cols], theme='streamlit', gridOptions=go,
columns_auto_size_mode=ColumnsAutoSizeMode.FIT_CONTENTS)
with tab2:
cols = ['topic_id', 'topic_count', 'topic_0']
topic_counts = get_topics_counts()
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)
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 = "# Semantic Search 🔍"
st.markdown(md_title)
st.sidebar.markdown(md_title)
# initialize_state()
main()
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