Update app.py

app.py

@@ -1,40 +1,40 @@
-import streamlit as st
-
-import os
-
-from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
-from langchain_community.vectorstores import FAISS
-
-st.set_page_config(page_title="Educate Kids", page_icon=":robot:")
-st.header("Hey, Ask me something & I will give out similar things")
-
-embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
-
-from langchain.document_loaders.csv_loader import CSVLoader
-
-loader = CSVLoader(file_path="myData.csv",
-                   csv_args={
-                       'delimiter': ',',
-                       'quotechar': '"',
-                       'fieldnames': ['Words']
-                   })
-
-data = loader.load()
-
-print(data)
-
-db = FAISS.from_documents(data, embeddings)
-
-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:
-  docs = db.similarity_search(user_input)
-  print(docs)
-  st.subheader("Top Matches: ")
-  st.text(docs[0])
-  st.text(docs[1].page_content)
+import streamlit as st
+
+import os
+
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain_community.vectorstores import FAISS
+
+st.set_page_config(page_title="Educate Kids", page_icon=":robot:")
+st.header("Hey, Ask me something & I will give out similar things")
+
+embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+
+from langchain.document_loaders.csv_loader import CSVLoader
+
+loader = CSVLoader(file_path="myData.csv",
+                   csv_args={
+                       'delimiter': ',',
+                       'quotechar': '"',
+                       'fieldnames': ['Words']
+                   })
+
+data = loader.load()
+
+print(data)
+
+db = FAISS.from_documents(data, embeddings)
+
+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:
+  docs = db.similarity_search(user_input)
+  print(docs)
+  st.subheader("Top Matches: ")
+  st.text(docs[0])
+  st.text(docs[1])