app.py

#Allows you to use Streamlit, a framework for building interactive web applications.
#It provides functions for creating UIs, displaying data, and handling user inputs.
import streamlit as st


#This module provides a way to interact with the operating system, such as accessing environment variables, working with files
#and directories, executing shell commands, etc
import os

#Helps us generate embeddings
#An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. 
#Small distances suggest high relatedness and large distances suggest low relatedness.
from langchain.embeddings import HuggingFaceEmbeddings


#FAISS is an open-source library developed by Facebook AI Research for efficient similarity search and clustering of large-scale datasets, particularly with high-dimensional vectors. 
#It provides optimized indexing structures and algorithms for tasks like nearest neighbor search and recommendation systems.
from langchain.vectorstores import FAISS


#load_dotenv() is a function that loads variables from a .env file into environment variables in a Python script. 
#It allows you to store sensitive information or configuration settings separate from your code
#and access them within your application.
from dotenv import load_dotenv


load_dotenv()


#By using st.set_page_config(), you can customize the appearance of your Streamlit application's web page
st.set_page_config(page_title="Educate Kids", page_icon=":robot:")
st.header("Hey, Ask me something & I will give out similar things")

#Initialize the OpenAIEmbeddings object
# embeddings = OpenAIEmbeddings()
embeddings= HuggingFaceEmbeddings()

#The below snippet helps us to import CSV file data for our tasks
from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='myData.csv', csv_args={
    'delimiter': ',',
    'quotechar': '"',
    'fieldnames': ['Words']
})

#Assigning the data inside the csv to our variable here
data = loader.load()

#Display the data
print(data)

db = FAISS.from_documents(data, embeddings)

#Function to receive input from user and store it in a variable
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:
    
    #If the button is clicked, the below snippet will fetch us the similar text
    docs = db.similarity_search(user_input)
    print(docs)
    st.subheader("Top Matches:")
    st.text(docs[0])
    st.text(docs[1].page_content)