update with new scores

app.py

@@ -5,12 +5,13 @@ import pandas as pd
 import glob
 import csv
 from PIL import Image
+import datasets
 from datasets import load_dataset, Dataset, load_from_disk
 from huggingface_hub import login
 import os
-import datasets
 import requests
-from bs4 import BeautifulSoup
+
+SCORE_NAME_MAPPING = {'clip': 'clip_score', 'rank': 'avg_rank', 'pop': 'model_download_count'}
 
 
 class GalleryApp:
@@ -127,16 +128,42 @@ class GalleryApp:
             #     st.image(image_url, use_column_width=True)
 
         # with images:
-        selecters = st.columns([1, 1, 2, 0.5])
+        # selecters = st.columns([2, 1, 2, 0.5])
+        selecters = st.columns([4, 1, 1])
 
         with selecters[0]:
-            # sort_by = st.selectbox('Sort by', items.columns[11: -1])
-            sort_by = st.selectbox('Sort by', ['model_download_count', 'clip_score', 'avg_rank', 'model_name', 'model_id',
-                                   'modelVersion_name', 'modelVersion_id'])
+            # # sort_by = st.selectbox('Sort by', items.columns[11: -1])
+            # sort_by = st.selectbox('Sort by', ['model_download_count', 'clip_score', 'avg_rank', 'model_name', 'model_id',
+            #                        'modelVersion_name', 'modelVersion_id'])
             print(items.columns)
+            types = st.columns([1, 3])
+            with types[0]:
+                sort_type = st.selectbox('Sort by', ['IDs and Names', 'Scores'])
+            with types[1]:
+                if sort_type == 'IDs and Names':
+                    sort_by = st.selectbox('Sort by', ['model_name', 'model_id', 'modelVersion_name', 'modelVersion_id'], label_visibility='hidden')
+                elif sort_type == 'Scores':
+                    sort_by = st.multiselect('Sort by', ['clip_score', 'avg_rank', 'popularity'], label_visibility='hidden', default=['clip_score', 'avg_rank', 'popularity'])
+                    # process sort_by to map to the column name
+
+                    if len(sort_by) == 3:
+                        sort_by = 'clip+rank+pop'
+                    elif len(sort_by) == 2:
+                        if 'clip_score' in sort_by and 'avg_rank' in sort_by:
+                            sort_by = 'clip+rank'
+                        elif 'clip_score' in sort_by and 'popularity' in sort_by:
+                            sort_by = 'clip+pop'
+                        elif 'avg_rank' in sort_by and 'popularity' in sort_by:
+                            sort_by = 'rank+pop'
+                    elif len(sort_by) == 1:
+                        if 'popularity' in sort_by:
+                            sort_by = 'model_download_count'
+                        else:
+                            sort_by = sort_by[0]
+                    print(sort_by)
 
         with selecters[1]:
-            order = st.selectbox('Order', ['Ascending', 'Descending'], index=1 if sort_by == 'clip_score' or sort_by == 'model_download_count' else 0)
+            order = st.selectbox('Order', ['Ascending', 'Descending'], index=1 if sort_type == 'Scores' else 0)
             if order == 'Ascending':
                 order = True
             else:
@@ -145,19 +172,31 @@ class GalleryApp:
         items = items.sort_values(by=[sort_by], ascending=order).reset_index(drop=True)
 
         with selecters[2]:
-            info = st.multiselect('Show Info',
-                                  ['model_download_count', 'clip_score', 'avg_rank', 'model_name', 'model_id',
-                                   'modelVersion_name', 'modelVersion_id'],
-                                  default=sort_by)
-
-        col_num = st.slider('Number of columns', min_value=1, max_value=9, value=4, step=1, key='col_num')
-
-        with selecters[3]:
             filter = st.selectbox('Filter', ['All', 'Checked', 'Unchecked'])
             if filter == 'Checked':
-                items = items[items['checked'] == True].reset_index(drop=True)
+                items = items[items['checked'] is True].reset_index(drop=True)
             elif filter == 'Unchecked':
-                items = items[items['checked'] == False].reset_index(drop=True)
+                items = items[items['checked'] is False].reset_index(drop=True)
+
+        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'],
+                              default=sort_by)
+
+        print('info', info)
+        # add one annotation
+        mentioned_scores = []
+        for i in info:
+            if '+' in i:
+                mentioned = i.split('+')
+                for m in mentioned:
+                    if SCORE_NAME_MAPPING[m] not in mentioned_scores:
+                        mentioned_scores.append(SCORE_NAME_MAPPING[m])
+        if len(mentioned_scores) > 0:
+            st.write(f"**Note: ** The scores {mentioned_scores} are normalized to [0, 1] for each score type, and then added together. The higher the score, the better the model.")
+
+
+        col_num = st.slider('Number of columns', min_value=1, max_value=9, value=4, step=1, key='col_num')
 
         with st.form(key=f'{prompt_id}', clear_on_submit=False):
             buttons = st.columns([1, 1, 1])

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@@ -148,15 +148,39 @@
     "avg_rank": {
       "dtype": "float64",
       "_type": "Value"
+    },
+    "norm_pop": {
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   "homepage": "",
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data/promptbook/state.json

@@ -67,7 +67,7 @@
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-  "_fingerprint": "a8406fc6fd9de22e",
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data/roster/dataset_info.json

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data/roster/state.json

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requirements.txt

@@ -1,4 +1,5 @@
 huggingface_hub
 streamlit-elements==0.1.0
 streamlit-extras
-altair<5
+altair<5
+streamlit-plotly-events