update with GemRec-18k

Archive/test.py

@@ -1,26 +1,45 @@
 import streamlit as st
+from streamlit_sortables import sort_items
 
 if __name__ == "__main__":
-    if 'check_dict' not in st.session_state:
-        st.session_state.check_dict = {'check1': False, 'check2': False, 'check3': False}
+    # if 'check_dict' not in st.session_state:
+    #     st.session_state.check_dict = {'check1': False, 'check2': False, 'check3': False}
+    #
+    # with st.form('my_form'):
+    #     st.session_state.check_dict['check1'] = st.checkbox('Check 1 out')
+    #     st.session_state.check_dict['check2'] = st.checkbox('Check 2 out')
+    #     st.session_state.check_dict['check3'] = st.checkbox('Check 3 out')
+    #
+    #     check21 = st.checkbox('Check 21 out')
+    #     if check21:
+    #         st.write('check21 is checked')
+    #     check22 = st.checkbox('Check 22 out')
+    #     if check22:
+    #         st.write('check22 is checked')
+    #     check23 = st.checkbox('Check 23 out')
+    #     if check23:
+    #         st.write('check23 is checked')
+    #
+    #     # Every form must have a submit button.
+    #     submitted = st.form_submit_button('Submit')
+    #
+    #     items = ['a', 'b', 'c']
+    #     sorted_items = sort_items(items)
+    #
+    # for key, value in st.session_state.check_dict.items():
+    #     st.write(key, value)
 
-    with st.form('my_form'):
-        st.session_state.check_dict['check1'] = st.checkbox('Check 1 out')
-        st.session_state.check_dict['check2'] = st.checkbox('Check 2 out')
-        st.session_state.check_dict['check3'] = st.checkbox('Check 3 out')
+    from tqdm import tqdm
+    import time
 
-        check21 = st.checkbox('Check 21 out')
-        if check21:
-            st.write('check21 is checked')
-        check22 = st.checkbox('Check 22 out')
-        if check22:
-            st.write('check22 is checked')
-        check23 = st.checkbox('Check 23 out')
-        if check23:
-            st.write('check23 is checked')
+    outer_list = [1, 2, 3, 4]
+    inner_list = [5, 6, 7, 8]
+
+    # Outer loop
+    for item in tqdm(outer_list, desc="Outer Loop", position=0):
+        # Inner loop with nested=True
+        for inner_item in tqdm(inner_list, desc="Inner Loop", leave=False, position=1):
+            # Your nested loop logic here
+            time.sleep(0.1)
 
-        # Every form must have a submit button.
-        submitted = st.form_submit_button('Submit')
 
-    for key, value in st.session_state.check_dict.items():
-        st.write(key, value)

Home.py

@@ -31,10 +31,16 @@ def save_user_id(user_id):
     st.session_state.user_id = [user_id, time.time()]
 
 
+def logout():
+    st.session_state.pop('user_id')
+    st.session_state.pop('selected_dict')
+    st.session_state.pop('score_weights')
+
+
 if __name__ == '__main__':
     st.set_page_config(page_title="Login", page_icon="🏠", layout="wide")
-
-    st.title("Personalized Image Ranking")
+    st.write('A Research by MAPS Lab, NYU Shanghai')
+    st.title("Personalized Model Coffer")
     st.write(
         "This is an web application to collect personal preference to ai generated images. \
         You can know which model you like most after you finish the survey."
@@ -44,5 +50,5 @@ if __name__ == '__main__':
         login()
     else:
         st.write('You have already logged in as ' + st.session_state.user_id[0])
-        st.button('Log out', on_click=lambda: st.session_state.pop('user_id'))
+        st.button('Log out', on_click=logout)
 

data/download_script.py

@@ -27,4 +27,4 @@ def test():
 
 
 if __name__ == '__main__':
-    main()
+    main()

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-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00012-of-00022-8303ad8493c4744b.parquet": {
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-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00013-of-00022-6767a389f1309f8d.parquet": {
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-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00014-of-00022-5ede5c4cbbe329b3.parquet": {
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+    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/b1796a1297ac1c43eee27f504c7c2f6e86b3b6d0/data/train-00014-of-00021-2afe87982509f967.parquet": {
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-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00015-of-00022-bf1cd54c3a1910ec.parquet": {
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+    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/b1796a1297ac1c43eee27f504c7c2f6e86b3b6d0/data/train-00015-of-00021-eec7637847985493.parquet": {
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-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00016-of-00022-ce47fe50aa1ba900.parquet": {
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+    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/b1796a1297ac1c43eee27f504c7c2f6e86b3b6d0/data/train-00016-of-00021-d6dea60081409784.parquet": {
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-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00017-of-00022-d66aeaf6eae64479.parquet": {
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+    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/b1796a1297ac1c43eee27f504c7c2f6e86b3b6d0/data/train-00017-of-00021-5add9142d5ead5f9.parquet": {
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-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00018-of-00022-07436ca459c527b4.parquet": {
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+    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/b1796a1297ac1c43eee27f504c7c2f6e86b3b6d0/data/train-00018-of-00021-39b85ca86366c05f.parquet": {
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-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00019-of-00022-409bfea998c296dd.parquet": {
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+    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/b1796a1297ac1c43eee27f504c7c2f6e86b3b6d0/data/train-00019-of-00021-677afc50ca78013a.parquet": {
+      "num_bytes": 492125355,
       "checksum": null
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-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00020-of-00022-03c16c0058c5d083.parquet": {
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-      "checksum": null
-    },
-    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/784dc12ec8b5191524bb8ae145dd2a41658cb5ec/data/train-00021-of-00022-663fb7b32c13cf84.parquet": {
-      "num_bytes": 476572989,
+    "https://huggingface.co/datasets/NYUSHPRP/ModelCofferPromptBook/resolve/b1796a1297ac1c43eee27f504c7c2f6e86b3b6d0/data/train-00020-of-00021-47bac33a72742d9d.parquet": {
+      "num_bytes": 492454315,
       "checksum": null
     }
   },
-  "download_size": 10512555846,
+  "download_size": 10369122623,
   "features": {
     "image": {
       "_type": "Image"
@@ -134,66 +130,66 @@
       "_type": "Value"
     },
     "cfgScale": {
-      "dtype": "float64",
+      "dtype": "int64",
       "_type": "Value"
     },
     "sampler": {
       "dtype": "string",
       "_type": "Value"
     },
-    "clip_score": {
-      "dtype": "float64",
+    "note": {
+      "dtype": "string",
       "_type": "Value"
     },
-    "avg_rank": {
+    "clip_score": {
       "dtype": "float64",
       "_type": "Value"
     },
-    "norm_pop": {
+    "msq_score": {
       "dtype": "float64",
       "_type": "Value"
     },
-    "norm_clip": {
+    "nsfw_score": {
       "dtype": "float64",
       "_type": "Value"
     },
-    "clip+rank+pop": {
+    "norm_clip": {
       "dtype": "float64",
       "_type": "Value"
     },
-    "clip+rank": {
+    "norm_nsfw": {
       "dtype": "float64",
       "_type": "Value"
     },
-    "clip+pop": {
+    "norm_pop": {
       "dtype": "float64",
       "_type": "Value"
     },
-    "rank+pop": {
+    "norm_msq": {
       "dtype": "float64",
       "_type": "Value"
     }
   },
   "homepage": "",
   "license": "",
-  "size_in_bytes": 21029856153,
+  "size_in_bytes": 20741214973,
   "splits": {
     "train": {
       "name": "train",
-      "num_bytes": 10517300307,
-      "num_examples": 16000,
+      "num_bytes": 10372092350,
+      "num_examples": 18000,
       "shard_lengths": [
-        1456,
-        1456,
-        1456,
-        1454,
-        1454,
-        1454,
-        1454,
-        1454,
-        1454,
-        1454,
-        1454
+        1716,
+        1715,
+        1714,
+        1714,
+        1714,
+        1714,
+        1714,
+        1714,
+        1714,
+        1714,
+        857
       ],
       "dataset_name": "parquet"
     }

data/promptbook/state.json

@@ -1,73 +1,70 @@
 {
   "_data_files": [
     {
-      "filename": "data-00000-of-00022.arrow"
+      "filename": "data-00000-of-00021.arrow"
     },
     {
-      "filename": "data-00001-of-00022.arrow"
+      "filename": "data-00001-of-00021.arrow"
     },
     {
-      "filename": "data-00002-of-00022.arrow"
+      "filename": "data-00002-of-00021.arrow"
     },
     {
-      "filename": "data-00003-of-00022.arrow"
+      "filename": "data-00003-of-00021.arrow"
     },
     {
-      "filename": "data-00004-of-00022.arrow"
+      "filename": "data-00004-of-00021.arrow"
     },
     {
-      "filename": "data-00005-of-00022.arrow"
+      "filename": "data-00005-of-00021.arrow"
     },
     {
-      "filename": "data-00006-of-00022.arrow"
+      "filename": "data-00006-of-00021.arrow"
     },
     {
-      "filename": "data-00007-of-00022.arrow"
+      "filename": "data-00007-of-00021.arrow"
     },
     {
-      "filename": "data-00008-of-00022.arrow"
+      "filename": "data-00008-of-00021.arrow"
     },
     {
-      "filename": "data-00009-of-00022.arrow"
+      "filename": "data-00009-of-00021.arrow"
     },
     {
-      "filename": "data-00010-of-00022.arrow"
+      "filename": "data-00010-of-00021.arrow"
     },
     {
-      "filename": "data-00011-of-00022.arrow"
+      "filename": "data-00011-of-00021.arrow"
     },
     {
-      "filename": "data-00012-of-00022.arrow"
+      "filename": "data-00012-of-00021.arrow"
     },
     {
-      "filename": "data-00013-of-00022.arrow"
+      "filename": "data-00013-of-00021.arrow"
     },
     {
-      "filename": "data-00014-of-00022.arrow"
+      "filename": "data-00014-of-00021.arrow"
     },
     {
-      "filename": "data-00015-of-00022.arrow"
+      "filename": "data-00015-of-00021.arrow"
     },
     {
-      "filename": "data-00016-of-00022.arrow"
+      "filename": "data-00016-of-00021.arrow"
     },
     {
-      "filename": "data-00017-of-00022.arrow"
+      "filename": "data-00017-of-00021.arrow"
     },
     {
-      "filename": "data-00018-of-00022.arrow"
+      "filename": "data-00018-of-00021.arrow"
     },
     {
-      "filename": "data-00019-of-00022.arrow"
+      "filename": "data-00019-of-00021.arrow"
     },
     {
-      "filename": "data-00020-of-00022.arrow"
-    },
-    {
-      "filename": "data-00021-of-00022.arrow"
+      "filename": "data-00020-of-00021.arrow"
     }
   ],
-  "_fingerprint": "c573a12f4a0f0965",
+  "_fingerprint": "70624c30882c6104",
   "_format_columns": null,
   "_format_kwargs": {},
   "_format_type": null,

pages/Gallery.py

@@ -10,7 +10,7 @@ from bs4 import BeautifulSoup
 import altair as alt
 from streamlit_extras.switch_page_button import switch_page
 
-SCORE_NAME_MAPPING = {'clip': 'clip_score', 'rank': 'avg_rank', 'pop': 'model_download_count'}
+SCORE_NAME_MAPPING = {'clip': 'clip_score', 'rank': 'msq_score', 'pop': 'model_download_count'}
 
 
 # hist_data = pd.DataFrame(np.random.normal(42, 10, (200, 1)), columns=["x"])
@@ -84,6 +84,9 @@ class GalleryApp:
     def selection_panel(self, items):
         selecters = st.columns([1, 4])
 
+        if 'score_weights' not in st.session_state:
+            st.session_state.score_weights = [1.0, 0.8, 0.2, 0.8]
+
         # select sort type
         with selecters[0]:
             sort_type = st.selectbox('Sort by', ['Scores', 'IDs and Names'])
@@ -97,7 +100,7 @@ class GalleryApp:
                 # select sort by
                 with sub_selecters[0]:
                     sort_by = st.selectbox('Sort by',
-                                           ['model_name', 'model_id', 'modelVersion_name', 'modelVersion_id'],
+                                           ['model_name', 'model_id', 'modelVersion_name', 'modelVersion_id', 'norm_nsfw'],
                                            label_visibility='hidden')
 
                 continue_idx = 1
@@ -106,28 +109,30 @@ class GalleryApp:
                 # add custom weights
                 sub_selecters = st.columns([1, 1, 1, 1])
 
-                if 'score_weights' not in st.session_state:
-                    st.session_state.score_weights = [1.0, 0.8, 0.2, 0.84]
-
                 with sub_selecters[0]:
                     clip_weight = st.number_input('Clip Score Weight', min_value=-100.0, max_value=100.0, value=st.session_state.score_weights[0], step=0.1, help='the weight for normalized clip score')
                 with sub_selecters[1]:
-                    rank_weight = st.number_input('Distinctiveness Weight', min_value=-100.0, max_value=100.0, value=st.session_state.score_weights[1], step=0.1, help='the weight for average rank')
+                    msq_weight = st.number_input('mSQ Weight', min_value=-100.0, max_value=100.0, value=st.session_state.score_weights[1], step=0.1, help='the weight for m(eam) s(imilarity) q(antile) score for measuring distinctiveness')
                 with sub_selecters[2]:
                     pop_weight = st.number_input('Popularity Weight', min_value=-100.0, max_value=100.0, value=st.session_state.score_weights[2], step=0.1, help='the weight for normalized popularity score')
 
-                items.loc[:, 'weighted_score_sum'] = round(items['norm_clip'] * clip_weight + items['avg_rank'] * rank_weight + items[
+                items.loc[:, 'weighted_score_sum'] = round(items['norm_clip'] * clip_weight + items['norm_msq'] * msq_weight + items[
                     'norm_pop'] * pop_weight, 4)
 
                 continue_idx = 3
 
+                # save latest weights
+                st.session_state.score_weights[0] = clip_weight
+                st.session_state.score_weights[1] = msq_weight
+                st.session_state.score_weights[2] = pop_weight
+
             # select threshold
             with sub_selecters[continue_idx]:
-                dist_threshold = st.number_input('Distinctiveness Threshold', min_value=0.0, max_value=1.0, value=st.session_state.score_weights[3], step=0.01, help='Only show models with distinctiveness score lower than this threshold, set 1.0 to show all images')
-                items = items[items['avg_rank'] < dist_threshold].reset_index(drop=True)
+                nsfw_threshold = st.number_input('NSFW Score Threshold', min_value=0.0, max_value=1.0, value=st.session_state.score_weights[3], step=0.01, help='Only show models with nsfw score lower than this threshold, set 1.0 to show all images')
+                items = items[items['norm_nsfw'] <= nsfw_threshold].reset_index(drop=True)
 
-            # save latest weights
-            st.session_state.score_weights = [clip_weight, rank_weight, pop_weight, dist_threshold]
+            # save latest threshold
+            st.session_state.score_weights[3] = nsfw_threshold
 
         # draw a distribution histogram
         if sort_type == 'Scores':
@@ -167,9 +172,9 @@ class GalleryApp:
 
             # select info to show
             info = st.multiselect('Show Info',
-                                  ['model_download_count', 'clip_score', 'avg_rank', 'model_name', 'model_id',
-                                   'modelVersion_name', 'modelVersion_id', 'clip+rank', 'clip+pop', 'rank+pop',
-                                   'clip+rank+pop', 'weighted_score_sum'],
+                                  ['model_name', 'model_id', 'modelVersion_name', 'modelVersion_id',
+                                   'weighted_score_sum', 'model_download_count', 'clip_score', 'msq_score',
+                                   'nsfw_score', 'norm_nsfw'],
                                   default=sort_by)
 
         # apply sorting to dataframe
@@ -190,20 +195,13 @@ class GalleryApp:
 
             items = self.promptBook[self.promptBook['tag'] == tag].reset_index(drop=True)
 
-            original_prompts = np.sort(items['prompt'].unique())[::-1]
+            prompts = np.sort(items['prompt'].unique())[::-1]
 
-            # remove the first four items in the prompt, which are mostly the same
-            if tag != 'abstract':
-                prompts = [', '.join(x.split(', ')[4:]) for x in original_prompts]
-                prompt = st.selectbox('Select prompt', prompts)
-
-                idx = prompts.index(prompt)
-                prompt_full = ', '.join(original_prompts[idx].split(', ')[:4]) + ', ' + prompt
-            else:
-                prompt_full = st.selectbox('Select prompt', original_prompts)
+            selected_prompt = st.selectbox('Select prompt', prompts)
 
-            items = items[items['prompt'] == prompt_full].reset_index(drop=True)
+            items = items[items['prompt'] == selected_prompt].reset_index(drop=True)
             prompt_id = items['prompt_id'].unique()[0]
+            note = items['note'].unique()[0]
 
             # show image metadata
             image_metadatas = ['prompt_id', 'prompt', 'negativePrompt', 'sampler', 'cfgScale', 'size', 'seed']
@@ -215,11 +213,11 @@ class GalleryApp:
                 else:
                     st.caption(f"{items[key][0]}")
 
-            # for tag as civitai, add civitai reference
-            if tag == 'civitai':
+            # for note as civitai image id, add civitai reference
+            if isinstance(note, str) and note.isdigit():
                 try:
                     st.write('**Civitai Reference**')
-                    res = requests.get(f'https://civitai.com/images/{prompt_id.item()}')
+                    res = requests.get(f'https://civitai.com/images/{note}')
                     # st.write(res.text)
                     soup = BeautifulSoup(res.text, 'html.parser')
                     image_section = soup.find('div', {'class': 'mantine-12rlksp'})

pages/Ranking.py

@@ -14,21 +14,23 @@ if __name__ == "__main__":
             switch_page("home")
 
     else:
-        all_checked = 0
+        selected_modelVersions = []
         for key, value in st.session_state.selected_dict.items():
             for v in value:
-                all_checked += 1
+                if v not in selected_modelVersions:
+                    selected_modelVersions.append(v)
 
-        if all_checked == 0:
+        if len(selected_modelVersions) == 0:
             st.info('You have not checked any image yet. Please go back to the gallery page and check some images.')
             gallery_btn = st.button('Go to Gallery')
             if gallery_btn:
                 switch_page('gallery')
         else:
-            st.write('You have checked ' + str(all_checked) + ' images.')
+            st.write('You have checked ' + str(len(selected_modelVersions)) + ' images.')
             roster, promptBook, images_ds = load_hf_dataset()
             st.write("## roster")
-            st.write(roster)
-            st.write("## promptBook")
-            st.write(promptBook)
+            st.write(roster[roster['modelVersion_id'].isin(selected_modelVersions)])
+            # st.write(roster)
+            # st.write("## promptBook")
+            # st.write(promptBook)