Create app.py

app.py

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+
+import streamlit as st
+import os
+from langchain_openai import OpenAIEmbeddings
+
+from langchain_community.vectorstores import FAISS
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+#customize the appearance of then 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()
+
+# import CSV file data
+from langchain.document_loaders.csv_loader import CSVLoader
+loader = CSVLoader(file_path='myData.csv', csv_args={
+    'delimiter': ',',
+    'quotechar': '"',
+    'fieldnames': ['Words']
+})
+
+data = loader.load()
+
+#Display the data
+print(data)
+
+db = FAISS.from_documents(data, embeddings)
+
+#Function to receive input from user
+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:
+    
+    #fetch the similar text
+    docs = db.similarity_search(user_input)
+    print(docs)
+    st.subheader("Top Matches:")
+    st.text(docs[0].page_content)
+    st.text(docs[1].page_content)