Elasticsearch search viewer with Streamlit example

app.py

import streamlit as st
from elasticsearch import Elasticsearch


def main():
    st.title("Streamlit ES Viewer")
    with st.form("my_form", clear_on_submit=False):
        keyword = st.text_input("keyword", "Python")
        title_weight = st.number_input(
            label="title weight", value=1.0, min_value=0.0, max_value=5.0, step=0.1
        )
        caption_weight = st.number_input(
            label="caption weight", value=1.0, min_value=0.0, max_value=5.0, step=0.1
        )
        submitted = st.form_submit_button("Search")

    if submitted:
        es = Elasticsearch(hosts="http://localhost:9200")
        body = {"query": {"simple_query_string": {}}}
        body["query"]["simple_query_string"]["query"] = keyword
        body["query"]["simple_query_string"]["fields"] = [
            f"title^{title_weight}",
            f"itemCaption^{caption_weight}",
        ]
        st.write(body)
        result = es.search(index="book", body=body, size=100, explain=True)
        result_num = result["hits"]["total"]["value"]
        books = result["hits"]["hits"]

        st.write(f"Found {result_num} results.")
        for book in books:
            st.subheader(book["_source"]["title"])
            st.image(book["_source"]["largeImageUrl"], width=100)
            st.link_button("Link", book["_source"]["itemUrl"])
            st.text_area(
                key=f"itemCaption_{book['_id']}",
                label="商品説明",
                value=book["_source"]["itemCaption"],
                height=200,
            )
            st.text(f'score: {book["_score"]}')
            st.text(f'author: {book["_source"]["author"]}')
            with st.expander("Explain", expanded=False):
                st.json(book["_explanation"])
            st.divider()
        st.write("Done!")

# streamlit run app.py
if __name__ == "__main__":
    main()