Upload app.py

app.py

@@ -1,70 +1,67 @@
-#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
+# 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
 
-#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
+# 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
 
-#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.
+# 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.
+# 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
+# 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
+# Initialize the OpenAIEmbeddings object
 embeddings = OpenAIEmbeddings()
 
-#The below snippet helps us to import CSV file data for our tasks
+# 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
+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
+# Display the data
 print(data)
 
 db = FAISS.from_documents(data, embeddings)
 
-#Function to receive input from user and store it in a variable
+
+# Function to receive input from user and store it in a variable
 def get_text():
-    input_text = st.text_input("You: ", key= input)
+    input_text = st.text_input("You: ", key=input)
     return input_text
 
 
-user_input=get_text()
-submit = st.button('Find similar Things')  
+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
+
+    # 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)
-
+    st.text(docs[:3].page_content)