test using aws s3 as image host

pages/Gallery.py

@@ -15,33 +15,6 @@ from sklearn.svm import LinearSVC
 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"])
-@st.cache_resource
-def altair_histogram(hist_data, sort_by, mini, maxi):
-    brushed = alt.selection_interval(encodings=['x'], name="brushed")
-
-    chart = (
-        alt.Chart(hist_data)
-        .mark_bar(opacity=0.7, cornerRadius=2)
-        .encode(alt.X(f"{sort_by}:Q", bin=alt.Bin(maxbins=25)), y="count()")
-        # .add_selection(brushed)
-        # .properties(width=800, height=300)
-    )
-
-    # Create a transparent rectangle for highlighting the range
-    highlight = (
-        alt.Chart(pd.DataFrame({'x1': [mini], 'x2': [maxi]}))
-        .mark_rect(opacity=0.3)
-        .encode(x='x1', x2='x2')
-        # .properties(width=800, height=300)
-    )
-
-    # Layer the chart and the highlight rectangle
-    layered_chart = alt.layer(chart, highlight)
-
-    return layered_chart
-
-
 class GalleryApp:
     def __init__(self, promptBook, images_ds):
         self.promptBook = promptBook
@@ -58,7 +31,8 @@ class GalleryApp:
                     if idx + j < len(items):
                         with cols[j]:
                             # show image
-                            image = self.images_ds[items.iloc[idx + j]['row_idx'].item()]['image']
+                            # image = self.images_ds[items.iloc[idx + j]['row_idx'].item()]['image']
+                            image = f"https://modelcofferbucket.s3.us-east-2.amazonaws.com/{items.iloc[idx + j]['image_id']}.png"
                             st.image(image, use_column_width=True)
 
                             # handel checkbox information
@@ -404,7 +378,31 @@ class GalleryApp:
         st.session_state.score_weights[0: 3] = optimal_weight
 
 
+# hist_data = pd.DataFrame(np.random.normal(42, 10, (200, 1)), columns=["x"])
+@st.cache_resource
+def altair_histogram(hist_data, sort_by, mini, maxi):
+    brushed = alt.selection_interval(encodings=['x'], name="brushed")
+
+    chart = (
+        alt.Chart(hist_data)
+        .mark_bar(opacity=0.7, cornerRadius=2)
+        .encode(alt.X(f"{sort_by}:Q", bin=alt.Bin(maxbins=25)), y="count()")
+        # .add_selection(brushed)
+        # .properties(width=800, height=300)
+    )
+
+    # Create a transparent rectangle for highlighting the range
+    highlight = (
+        alt.Chart(pd.DataFrame({'x1': [mini], 'x2': [maxi]}))
+        .mark_rect(opacity=0.3)
+        .encode(x='x1', x2='x2')
+        # .properties(width=800, height=300)
+    )
+
+    # Layer the chart and the highlight rectangle
+    layered_chart = alt.layer(chart, highlight)
 
+    return layered_chart
 
 
 @st.cache_data
@@ -415,16 +413,13 @@ def load_hf_dataset():
     # load from huggingface
     roster = pd.DataFrame(load_dataset('NYUSHPRP/ModelCofferRoster', split='train'))
     promptBook = pd.DataFrame(load_dataset('NYUSHPRP/ModelCofferMetadata', split='train'))
-    images_ds = load_from_disk(os.path.join(os.getcwd(), 'data', 'promptbook'))
+    # images_ds = load_from_disk(os.path.join(os.getcwd(), 'data', 'promptbook'))
+    images_ds = None  # set to None for now since we use s3 bucket to store images
 
     # process dataset
     roster = roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name',
                                                        'model_download_count']].drop_duplicates().reset_index(drop=True)
 
-    # # add 'checked' column to promptBook if not exist
-    # if 'checked' not in promptBook.columns:
-    #     promptBook.loc[:, 'checked'] = False
-
     # add 'custom_score_weights' column to promptBook if not exist
     if 'weighted_score_sum' not in promptBook.columns:
         promptBook.loc[:, 'weighted_score_sum'] = 0

pages/Ranking.py

@@ -1,9 +1,33 @@
-import streamlit as st
 import numpy as np
 import pandas as pd
+import streamlit as st
+
+from streamlit_elements import elements, mui, html, dashboard, nivo
 from streamlit_extras.switch_page_button import switch_page
+
 from pages.Gallery import load_hf_dataset
 
+
+class RankingApp():
+    def __init__(self, promptBook, images_ds):
+        self.promptBook = promptBook
+        self.images_ds = images_ds
+
+    def draggable_images(self, items, layout='vertical'):
+        pass
+
+    def sidebar(self):
+        with st.sidebar:
+            prompt_tags = self.promptBook['tag'].unique()
+
+
+    def app(self):
+        st.title('Personal Image Ranking')
+        st.write('Here you can test out your selected images with any prompt you like.')
+
+        prompt_tags, tag, prompt_id, items= self.sidebar()
+
+
 if __name__ == "__main__":
     st.set_page_config(page_title="Personal Image Ranking", page_icon="🎖️️", layout="wide")