app.py

import streamlit as st

import os

from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain_community.vectorstores import FAISS

st.set_page_config(page_title="Educate Kids", page_icon=":robot:")
st.header("Hey, Ask me something & I will give out similar things")

embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")

from langchain.document_loaders.csv_loader import CSVLoader

loader = CSVLoader(file_path="myData.csv",
                   csv_args={
                       'delimiter': ',',
                       'quotechar': '"',
                       'fieldnames': ['Words']
                   })

data = loader.load()

print(data)

db = FAISS.from_documents(data, embeddings)

def get_text():
  input_text = st.text_input("You: ", key=input)
  return input_text

user_input = get_text()
submit = st.button("Find similar things")

if submit:
  docs = db.similarity_search(user_input)
  print(docs)
  st.subheader("Top Matches: ")
  st.text(docs[0])
  st.text(docs[1])