more choices to dis

data/vase_annotated_files/sim_Fabric_seeds0000-20000.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20fa48320e20998aad5665610083843705608a5f06ff081e4395ee4b5ac9cba3
+size 9731011

data/vase_annotated_files/sim_Provenance_seeds0000-20000.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a421ffd119eee312249c9fbd05ac65460849e71f538d05fad223cb55423f315f
+size 18066428

data/vase_annotated_files/sim_Technique_seeds0000-20000.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3d3425e15d76d47a8829783cadbd7072698083df199617a8423d5ccb9d88714
+size 2484876

pages/3_Oxford_Vases_Disentanglement.py

@@ -31,8 +31,6 @@ annotations_file = './data/vase_annotated_files/seeds0000-20000.pkl'
 with open(annotations_file, 'rb') as f:
     annotations = pickle.load(f)
 
-ann_df = pd.read_csv('./data/vase_annotated_files/sim_Shape Name_seeds0000-20000.csv')
-labels = ann_df.columns
 
 if 'image_id' not in st.session_state:
     st.session_state.image_id = 0
@@ -53,7 +51,12 @@ with input_col_1:
         
         # image_id = st.number_input('Image ID: ', format='%d', step=1)
         st.write('**Choose two options to disentangle**')
-        concept_ids = st.multiselect('Concepts:', tuple(labels), max_selections=2, default=['AMPHORA', 'CHALICE'])
+        type_col = st.selectbox('Concept category:', tuple(['Provenance', 'Shape Name', 'Fabric', 'Technique']))
+        
+        ann_df = pd.read_csv(f'./data/vase_annotated_files/sim_{type_col}_seeds0000-20000.csv')
+        labels = ann_df.columns
+
+        concept_ids = st.multiselect('Concepts:', tuple(labels), max_selections=2, default=[labels[0], labels[1]])
 
         st.write('**Choose a latent space to disentangle**')
         space_id = st.selectbox('Space:', tuple(['W', 'Z']))
@@ -142,12 +145,8 @@ if st.session_state.space_id == 'Z':
 else:
     original_image_vec = annotations['w_vectors'][st.session_state.image_id]
 
-print(ann_df.head())
 img = generate_original_image(original_image_vec, model, latent_space=st.session_state.space_id)
 cols = list(ann_df.columns)
-cols.remove('Unnamed: 0')
-cols.remove('ID')
-print(ann_df.loc[st.session_state.image_id, cols])
 top_pred = ann_df.loc[st.session_state.image_id, cols].astype(float).idxmax()
 # input_image = original_image_dict['image']
 # input_label = original_image_dict['label']