summary page new option

Home.py

@@ -38,22 +38,28 @@ def logout():
     st.session_state.pop('score_weights', None)
 
 
+def info():
+    with st.sidebar:
+        st.write('## About')
+
+        st.write(
+            "This is an web application to collect personal preference to images synthesised by generative models fine-tuned on stable diffusion. \
+            **You might consider it as a tool for quickly digging out the most suitable text-to-image generation model for you from [civitai](https://civitai.com/).**"
+        )
+        st.write(
+            "After you picking images from gallery page, and ranking them in the ranking page, you will be able to see a dashboard showing your preferred models in the summary page, **with download links of the models ready to use in [Automatic1111 webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)!**"
+        )
+
+
 if __name__ == '__main__':
     # print(st.source_util.get_pages('Home.py'))
-
     st.set_page_config(page_title="Login", page_icon="🏠", layout="wide")
+    info()
     st.write('A Research by [MAPS Lab](https://whongyi.github.io/MAPS-research), [NYU Shanghai](https://shanghai.nyu.edu)')
     st.title("🙌 Welcome to GEMRec Gallery Webapp!")
     st.info("Getting obsessed with tons of different text-to-image generation models available online?  \n \
             Want to find the most suitable one for your taste?  \n \
             **GEMRec** is here to help you!"
-             )
-    st.write(
-        "This is an web application to collect personal preference to images synthesised by generative models fine-tuned on stable diffusion. \
-        **You might consider it as a tool for quickly digging out the most suitable text-to-image generation model for you from [civitai](https://civitai.com/).**"
-    )
-    st.write(
-        "After you picking images from gallery page, and ranking them in the ranking page, you will be able to see a dashboard showing your preferred models in the summary page, **with download links of the models ready to use in [Automatic1111 webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)!**"
     )
 
     if 'user_id' not in st.session_state:

pages/Summary.py

@@ -43,72 +43,89 @@ class DashboardApp:
 
         tab1, tab2 = st.tabs(['Top Picks', 'Detailed Info'])
 
-
         with tab1:
-            # show the top 3 in metric cards
-            st.write('## Top picks')
-            n = 3
-            metric_cols = st.columns(n)
-            image_display = st.empty()
-
-            for i in range(n):
-                with metric_cols[i]:
-                    modelVersion_id = modelVersion_standings[i][0]
-                    winning_times = modelVersion_standings[i][1]
-
-                    model_id, model_name, modelVersion_name, url = self.roster[self.roster['modelVersion_id'] == modelVersion_id][['model_id', 'model_name', 'modelVersion_name', 'modelVersion_url']].values[0]
-
-                    metric_card = stylable_container(
-                        key="container_with_border",
-                        css_styles="""
-                        {
-                            border: 1.5px solid rgba(49, 51, 63, 0.2);
-                            border-left: 0.5rem solid gold;
-                            border-radius: 5px;
-                            padding: calc(1em + 5px);
-                            gap: 0.5em;
-                            box-shadow: 0 0 2rem rgba(0, 0, 0, 0.08);
-                            overflow-x: scroll;
-                        }
-                        """,
-                    )
-
-                    with metric_card:
-                        icon = '🥇'if i == 0 else '🥈' if i == 1 else '🥉'
-                        # st.write(model_id)
-                        st.write(f'### {icon} {model_name}, [{modelVersion_name}](https://civitai.com/models/{model_id}?modelVersionId={modelVersion_id})')
-                        st.write(f'Ranking Score: {winning_times}')
-
-                    show_image = st.button('Show Image', key=modelVersion_id, use_container_width=True)
-                    if show_image:
-
-                        images = self.promptBook[self.promptBook['modelVersion_id'] == modelVersion_id]['image_id'].values
-                        with image_display.container():
-                            st.write('---')
-                            st.write(f'### Images generated with {icon} {model_name}, {modelVersion_name}')
-                            col_num = 4
-                            image_cols = st.columns(col_num)
-                            for i in range(len(images)):
-                                with image_cols[i % col_num]:
-                                    image = f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{images[i]}.png"
-                                    st.image(image, use_column_width=True)
-
-
-                # # st.write('---')
-                # expander = st.expander(f'# {icon} {model_name}, [{modelVersion_name}](https://civitai.com/models/{modelVersion_id})')
-                # with expander:
-                #     images = self.promptBook[self.promptBook['modelVersion_id'] == modelVersion_id]['image_id'].values
-                #     st.write(images)
-
-        st.chat_input('Please leave your comments here.', key='comment')
+            # self.podium(modelVersion_standings)
+            self.podium_expander(modelVersion_standings)
 
         with tab2:
             st.write('## Detailed information of all selected models')
             detailed_info = pd.merge(pd.DataFrame(modelVersion_standings, columns=['modelVersion_id', 'ranking_score']), self.roster, on='modelVersion_id')
             st.data_editor(detailed_info, hide_index=True, disabled=True)
 
-
-
+    def podium(self, modelVersion_standings, n=3):
+        st.write('## Top picks')
+        metric_cols = st.columns(n)
+        image_display = st.empty()
+
+        for i in range(n):
+            with metric_cols[i]:
+                modelVersion_id = modelVersion_standings[i][0]
+                winning_times = modelVersion_standings[i][1]
+
+                model_id, model_name, modelVersion_name, url = self.roster[self.roster['modelVersion_id'] == modelVersion_id][['model_id', 'model_name', 'modelVersion_name', 'modelVersion_url']].values[0]
+
+                metric_card = stylable_container(
+                    key="container_with_border",
+                    css_styles="""
+                    {
+                        border: 1.5px solid rgba(49, 51, 63, 0.2);
+                        border-left: 0.5rem solid gold;
+                        border-radius: 5px;
+                        padding: calc(1em + 5px);
+                        gap: 0.5em;
+                        box-shadow: 0 0 2rem rgba(0, 0, 0, 0.08);
+                        overflow-x: scroll;
+                    }
+                    """,
+                )
+
+                with metric_card:
+                    icon = '🥇'if i == 0 else '🥈' if i == 1 else '🥉'
+                    # st.write(model_id)
+                    st.write(f'### {icon} {model_name}, [{modelVersion_name}](https://civitai.com/models/{model_id}?modelVersionId={modelVersion_id})')
+                    st.write(f'Ranking Score: {winning_times}')
+
+                show_image = st.button('Show Image', key=modelVersion_id, use_container_width=True)
+                if show_image:
+
+                    images = self.promptBook[self.promptBook['modelVersion_id'] == modelVersion_id]['image_id'].values
+                    with image_display.container():
+                        st.write('---')
+                        st.write(f'### Images generated with {icon} {model_name}, {modelVersion_name}')
+                        col_num = 4
+                        image_cols = st.columns(col_num)
+                        for i in range(len(images)):
+                            with image_cols[i % col_num]:
+                                image = f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{images[i]}.png"
+                                st.image(image, use_column_width=True)
+
+
+            # # st.write('---')
+            # expander = st.expander(f'# {icon} {model_name}, [{modelVersion_name}](https://civitai.com/models/{modelVersion_id})')
+            # with expander:
+            #     images = self.promptBook[self.promptBook['modelVersion_id'] == modelVersion_id]['image_id'].values
+            #     st.write(images)
+
+    def podium_expander(self, modelVersion_standings, n=3):
+        st.write('## Top picks')
+        # metric_cols = st.columns(n)
+        for i in range(n):
+            # with metric_cols[i]:
+            modelVersion_id = modelVersion_standings[i][0]
+            winning_times = modelVersion_standings[i][1]
+
+            model_id, model_name, modelVersion_name, url = self.roster[self.roster['modelVersion_id'] == modelVersion_id][['model_id', 'model_name', 'modelVersion_name', 'modelVersion_url']].values[0]
+
+            icon = '🥇'if i == 0 else '🥈' if i == 1 else '🥉'
+            with st.expander(f'**{icon} {model_name}, [{modelVersion_name}](https://civitai.com/models/{model_id}?modelVersionId={modelVersion_id})**, Ranking Score: {winning_times}'):
+                images = self.promptBook[self.promptBook['modelVersion_id'] == modelVersion_id]['image_id'].values
+                st.write(f'### Images generated with {icon} {model_name}, {modelVersion_name}')
+                col_num = 4
+                image_cols = st.columns(col_num)
+                for i in range(len(images)):
+                    with image_cols[i % col_num]:
+                        image = f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{images[i]}.png"
+                        st.image(image, use_column_width=True)
 
     def score_calculator(self, results, db_table):
         # sort results by battle time