Create app.py

app.py

@@ -0,0 +1,33 @@
+import streamlit as st
+
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+
+sample_words = ['apple', 'orange', 'rose', 'chocolate', 'pen', 'school', 'book', 'computer']
+
+#Define the HuggingFaceEmbeddings model
+model_path = 'sentence-transformers/all-MiniLM-l6-v2'
+
+embeddings = HuggingFaceEmbeddings(
+    model_name= model_path,
+    model_kwargs={'device':'cpu'},
+    encode_kwargs={'normalize_embeddings': False}
+)
+
+
+db = FAISS.from_texts(sample_words, embeddings)
+
+# UI 
+st.header('Ask me a question, and I'll provide similar words!')
+
+input_word = st.text_input("You: ", key= input)
+
+if input_word:
+    submit = st.button('Show me similar words')
+
+    if submit:
+        results = db.similarity_search(input_word)
+        st.subheader("Top Words:")
+        st.text(docs[0].page_content)
+        st.text(docs[1].page_content)
+