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

import streamlit as st

# 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
from langchain.document_loaders.csv_loader import CSVLoader

# 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 OpenAIEmbeddings

# 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()


# 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()

# The below snippet helps us to import CSV file data for our tasks

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)
    st.text(docs[0])
    st.text(docs[1].page_content)
    st.text([doc.page_content for doc in docs[:3]])