added streamlit api

app.py

@@ -6,9 +6,16 @@ Automatically generated by Colaboratory.
 Original file is located at
     https://colab.research.google.com/drive/1H-R9L74rpYOoQJOnTLLbUpcNpd9Tty_D
 """
+import streamlit as st
+st.markdown("This is a image classification program, please enter the image that you would like to process.")
+st.markdown("Please keep in mind that the dataset is very small (around 100-2000 imgs only")
+path = ['Coretta_Scott_King','Saddam_Hussein','Augustin_Calleri','Peter_Hunt']
 
-!wget http://vis-www.cs.umass.edu/lfw/lfw.tgz
-!tar -xvf /content/lfw.tgz
+select_path = st.selectbox('Which of the three photos would you like to process', options = path)
+st.write("You've select", select_path)
+
+# !wget http://vis-www.cs.umass.edu/lfw/lfw.tgz
+# !tar -xvf /content/lfw.tgz
 import tensorflow as tf
 from sklearn.datasets import load_sample_image
 import os
@@ -42,21 +49,26 @@ for dir in os.listdir(directory):
     new_dir = '/content/lfw/'+dir
     if os.path.isdir(new_dir):
         for files in os.listdir(new_dir):
-            feature_dict[new_dir+'/'+files] = preprocess_image(new_dir+'/'+files, target_size).flatten()
+            feature_dict[dir] = preprocess_image(new_dir+'/'+files, target_size).flatten()
     if i >= 100:
         break
 
-for file, features in feature_dict.items():
-    print(file, features)
+# for file, features in feature_dict.items():
+#     print(file, features)
 
 feature_map = np.array(list(feature_dict.values()))
 
 NearNeigh = NearestNeighbors(n_neighbors=10,algorithm='auto').fit(feature_map)
-
-for image_path in feature_dict:
-  img = feature_dict[image_path].reshape(1,-1)
-  distance,indices = NearNeigh.kneighbors(img)
-  print('Similar images for', image_path)
-  for i, index in enumerate(indices[0]):
-    similar_img_path = list(feature_dict.keys())[index]
-    print(i+1,similar_img_path)
+img = feature_dict[select_path].reshape(1,-1)
+distance,indices = NearNeigh.kneighbors(img)
+st.write('Similar images for', select_path)
+for i,index in enumerate(indices[0]):
+    similar_img_path = list(feature.keys())[index]
+    print(i+1,similar_img_path)
+# for image_path in feature_dict:
+#   img = feature_dict[image_path].reshape(1,-1)
+#   distance,indices = NearNeigh.kneighbors(img)
+#   print('Similar images for', image_path)
+#   for i, index in enumerate(indices[0]):
+#     similar_img_path = list(feature_dict.keys())[index]
+#     print(i+1,similar_img_path)