update app.py

app.py

@@ -2,14 +2,15 @@ import pickle
 import os
 from sklearn.neighbors import NearestNeighbors
 import numpy as np
-num_nn = 20
 import gradio as gr
 from PIL import Image
 
-data_root = '/dccstor/elishc1/datasets/DomainNet'
-feat_dir = 'brad_feats' 
+data_root = 'https://ai-vision-public-datasets.s3.eu.cloud-object-storage.appdomain.cloud/DomainNet'
+feat_dir = 'brad_feats'
 domains = ['real', 'painting', 'clipart', 'sketch']
 shots = '-1'
+num_nn = 20
+
 search_domain = 'all'
 num_results_per_domain = 5
 src_data_dict = {}
@@ -18,16 +19,13 @@ if search_domain == 'all':
         with open(os.path.join(feat_dir, f'dst_{d}_{shots}.pkl'), 'rb') as fp:
             src_data = pickle.load(fp)
 
-            src_nn_fit = NearestNeighbors(n_neighbors=num_results_per_domain, 
-algorithm='auto', n_jobs=-1).fit(src_data[1])
+            src_nn_fit = NearestNeighbors(n_neighbors=num_results_per_domain, algorithm='auto', n_jobs=-1).fit(src_data[1])
             src_data_dict[d] = (src_data,src_nn_fit)
 else:
 
-    with open(os.path.join(feat_dir, f'dst_{search_domain}_{shots}.pkl'), 'rb') as 
-fp:
+    with open(os.path.join(feat_dir, f'dst_{search_domain}_{shots}.pkl'), 'rb') as fp:
         src_data = pickle.load(fp)
-        src_nn_fit = NearestNeighbors(n_neighbors=num_results_per_domain, 
-algorithm='auto', n_jobs=-1).fit(src_data[1])
+        src_nn_fit = NearestNeighbors(n_neighbors=num_results_per_domain, algorithm='auto', n_jobs=-1).fit(src_data[1])
         src_data_dict[search_domain] = (src_data,src_nn_fit)
 
 dst_data_dict = {}
@@ -41,27 +39,23 @@ def query(query_index, query_domain):
     img_paths = [dst_img_path]
     q_cl = dst_img_path.split('/')[-2]
     captions = [f'Query: {q_cl}']
-    for s_domain, s_data in src_data_dict.items():  
-        _, top_n_matches_ids = 
-s_data[1].kneighbors(dst_data[1][query_index:query_index+1])
+    for s_domain, s_data in src_data_dict.items():
+        _, top_n_matches_ids = s_data[1].kneighbors(dst_data[1][query_index:query_index+1])
         top_n_labels = s_data[0][2][top_n_matches_ids][0]
-        src_img_pths = [os.path.join(data_root, s_data[0][0][ix]) for ix in 
-top_n_matches_ids[0]]
+        src_img_pths = [os.path.join(data_root, s_data[0][0][ix]) for ix in top_n_matches_ids[0]]
         img_paths += src_img_pths
 
         for p in src_img_pths:
             src_cl = p.split('/')[-2]
             src_file =  p.split('/')[-1]
-            captions.append(src_cl)    
-    return tuple([Image.open(p) for p in img_paths])+ tuple(captions)
-try:
-    demo.close()
-except:
-    pass
+            captions.append(src_cl)
+    print(img_paths)
+    return tuple([p for p in img_paths])+ tuple(captions)
+
 demo = gr.Blocks()
 with demo:
     gr.Markdown('## Select Query Domain: ')
-    domain_drop = gr.Dropdown(domains)    
+    domain_drop = gr.Dropdown(domains)
 #     domain_select_button = gr.Button("Select Domain")
     slider = gr.Slider(0, 1000)
     image_button = gr.Button("Run")
@@ -70,7 +64,7 @@ with demo:
     src_cap = gr.Label()
     src_img = gr.Image()
 
-    
+
     out_images = []
     out_captions = []
     for d in domains:
@@ -81,7 +75,6 @@ with demo:
                     out_captions.append(gr.Label())
                     out_images.append(gr.Image())
 
-    image_button.click(query, inputs=[slider, domain_drop], 
-outputs=[src_img]+out_images +[src_cap]+ out_captions)
-demo.launch(share=True)
+    image_button.click(query, inputs=[slider, domain_drop], outputs=[src_img]+out_images +[src_cap]+ out_captions)
 
+demo.launch(share=True)