Update app.py

app.py

@@ -1,22 +1,21 @@
-
 import streamlit as st
 import os
-from langchain_openai import OpenAIEmbeddings
-
+# This is an open source developed by Facebook , helps us perform similariy search
 from langchain_community.vectorstores import FAISS
 
 from dotenv import load_dotenv
-
+from sentence_transformers import SentenceTransformer #for embedding
 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")
+key = os.getenv("GOOGLE_API_KEY")
+os.environ["GOOGLE_API_KEY"]=key
+
+# st.set_page_config(page_title="Educate Kids", page_icon=":robot:")
+# st.header("Hey, Ask me something & I will give out similar things")
+
+model = SentenceTransformer('sentence-transformers/average_word_embeddings_glove.6B.300d')
 
-#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': ',',
@@ -26,8 +25,11 @@ loader = CSVLoader(file_path='myData.csv', csv_args={
 
 data = loader.load()
 
-#Display the data
-print(data)
+db = FAISS.from_documents(data, model)
+
+def get_text():
+    input_text = st.text_input("You: ", key= input)
+    return input_text
 
 db = FAISS.from_documents(data, embeddings)
 
@@ -48,3 +50,7 @@ if submit:
     st.subheader("Top Matches:")
     st.text(docs[0].page_content)
     st.text(docs[1].page_content)
+# print(data)
+
+
+