searchApp.py

import streamlit as st
from elasticsearch import Elasticsearch
from sentence_transformers import SentenceTransformer

indexName = "all_products"

try:
    es = Elasticsearch(
    "https://192.168.18.136:9200",
    basic_auth=("elastic","FIRpdx+ua5jlE0K6Q3fs"),
    ca_certs="http_ca.crt"
    )
except ConnectionError as e:
    print("Connection Error:", e)
    
if es.ping():
    print("Succesfully connected to ElasticSearch!!")
else:
    print("Oops!! Can not connect to Elasticsearch!")




def search(input_keyword):
    model = SentenceTransformer('all-mpnet-base-v2')
    vector_of_input_keyword = model.encode(input_keyword)

    query = {
        "field" : "DescriptionVector",
        "query_vector" : vector_of_input_keyword,
        'k':50,
        "num_candidates" : 500,
    }

    res = es.knn_search(index="all_products", knn=query , source=["ProductName","Description"])

    result=res["hits"]["hits"]
    return result




def main():
    st.title("Search Products with Description")

    # Input: User enters search query
    search_query = st.text_input("Enter your search query")

    # Button: User triggers the search
    if st.button("Search"):
        if search_query:
            # Perform the search and get results
            results = search(search_query)

            # Display search results
            st.subheader("Search Results")
            for result in results:
                with st.container():
                    if '_source' in result:
                        try:
                            st.header(f"{result['_source']['ProductName']}")
                        except Exception as e:
                            print(e)
                        
                        try:
                            st.write(f"Description: {result['_source']['Description']}")
                        except Exception as e:
                            print(e)
                        st.divider()

                    
if __name__ == "__main__":
    main()