add requirements and app.py

app.py

@@ -0,0 +1,87 @@
+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' 
+domains = ['real', 'painting', 'clipart', 'sketch']
+shots = '-1'
+search_domain = 'all'
+num_results_per_domain = 5
+src_data_dict = {}
+if search_domain == 'all':
+    for d in domains:
+        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_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:
+        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_data_dict[search_domain] = (src_data,src_nn_fit)
+
+dst_data_dict = {}
+for d in domains:
+    with open(os.path.join(feat_dir, f'src_{d}_{shots}.pkl'), 'rb') as fp:
+        dst_data_dict[d] = pickle.load(fp)
+
+def query(query_index, query_domain):
+    dst_data = dst_data_dict[query_domain]
+    dst_img_path = os.path.join(data_root, dst_data[0][query_index])
+    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])
+        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]]
+        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
+demo = gr.Blocks()
+with demo:
+    gr.Markdown('## Select Query Domain: ')
+    domain_drop = gr.Dropdown(domains)    
+#     domain_select_button = gr.Button("Select Domain")
+    slider = gr.Slider(0, 1000)
+    image_button = gr.Button("Run")
+
+    gr.Markdown('# Query Image')
+    src_cap = gr.Label()
+    src_img = gr.Image()
+
+    
+    out_images = []
+    out_captions = []
+    for d in domains:
+        gr.Markdown(f'# {d.title()} Domain Images')
+        with gr.Row():
+            for _ in range(num_results_per_domain):
+                with gr.Column():
+                    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)
+

requirements.txt

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+numpy==1.20.1
+Pillow==8.2.0
+scikit-learn==0.24.1
+