Update app.py

app.py

@@ -6,7 +6,7 @@ import os
 import streamlit as st
 
 
-# @st.cache_resource()
+@st.cache()
 def load_model():
     print("Loading model...")
     # Load the pre-trained ResNet-50 model and set it to eval mode
@@ -56,7 +56,7 @@ def load_model():
     return features_dict
 
 
-# @st.cache_resource()
+@st.cache()
 def create_nearest_neighbors_object(features_dict):
     print("Creating nearest neighbors object...")
     import numpy as np
@@ -99,10 +99,6 @@ def get_nearest_neighbors(image_path):
                     use_column_width=True,
                 )
 
-
-# call the function with George W. Bush's image
-# get_nearest_neighbors('lfw/George_W_Bush/George_W_Bush_0001.jpg')
-
 # SteamLit App
 
 allow_output_mutation = True
@@ -115,30 +111,6 @@ st.title("Similarity Search")
 # Subtitle
 st.write("This app finds the 10 most similar images to a query image.")
 
-# Upload the query image Or Select the query image from ./lfw folder start with None
-# upload_query = st.file_uploader("Upload an image", type=["jpg", "png"])
-# if upload_query is not None:
-#     # Display the query image
-#     st.image(upload_query, caption="Query Image", use_column_width=True)
-#     # Temp save the query image to ./query_image/query_image.jpg
-#     try:
-#         os.mkdir("query_image")
-#     except:
-#         pass
-#     with open("query_image/query_image.jpg", "wb") as f:
-#         f.write(upload_query.getbuffer())
-
-#     # save path to query_image
-#     upload_query = "query_image/query_image.jpg"
-
-#     # Find the 10 most similar images
-#     if st.button("Find Similar Images"):
-#         # Call the get nearest neighbors function
-#         nearest10 = get_nearest_neighbors(upload_query)
-# Delete the query image
-# os.remove(upload_query)
-
-
 query_image = st.selectbox("Or select an image from the list", os.listdir("lfw"))
 # Display the query image from dir ./lfw/query_image/query_image_0001.jpg
 print("lfw/" + query_image + "/" + query_image + "_0001.jpg")