refine

app.py

@@ -183,7 +183,7 @@ with demo:
     gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="reference-text")
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("UnlearnDiffAtk Benchmark", elem_id="UnlearnDiffAtk-benchmark-tab-table", id=0):
+        with gr.TabItem("NSFW", elem_id="UnlearnDiffAtk-benchmark-tab-table", id=0):
             files = ['nudity','vangogh', 'church','garbage','parachute','tench']
             with gr.Row():
                 with gr.Column():
@@ -202,23 +202,9 @@ with demo:
                     )
                    
             for i in range(len(files)):
-                if files[i] == "church":
-                    name = "### [Unlearned Objects] "+" Church" 
-                    csv_path = './assets/'+files[i]+'.csv'
-                # elif files[i] == 'garbage':
-                #     name = "### [Unlearned Objects] "+" Garbage"
-                #     csv_path = './assets/'+files[i]+'.csv'
-                # elif files[i] == 'tench':
-                #     name = "### [Unlearned Objects] "+" Tench"
-                #     csv_path = './assets/'+files[i]+'.csv'
-                elif files[i] == 'parachute':
-                    name = "### [Unlearned Objects] "+" Parachute"
-                    csv_path = './assets/'+files[i]+'.csv'
-                elif files[i] == 'vangogh':
-                    name = "### [Unlearned Style] "+" Van Gogh"
-                    csv_path = './assets/'+files[i]+'.csv'
-                elif files[i] == 'nudity':
-                    name = "### Unlearned  Concepts "+" Nudity"
+                
+                if files[i] == 'nudity':
+                    name = "### [Unlearned  Concept]: "+" Nudity"
                     csv_path = './assets/'+files[i]+'.csv'
                 # elif files[i] == 'violence':
                 #     name = "### Unlearned Concepts "+" Violence"
@@ -290,9 +276,83 @@ with demo:
             for i in range(len(files)):
                 
                 if files[i] == 'vangogh':
-                    name = "### [Unlearned Style] "+" Van Gogh"
+                    name = "### [Unlearned Style]: "+" Van Gogh"
+                    csv_path = './assets/'+files[i]+'.csv'
+                
+                gr.Markdown(name)
+                df_results = load_data(csv_path)
+                df_results_init = df_results.copy()[show_columns]
+                leaderboard_table = gr.components.Dataframe(
+                value = df_results,
+                datatype = TYPES,
+                elem_id = "leaderboard-table",
+                interactive = False,
+                visible=True,
+                )
+
+            
+                hidden_leaderboard_table_for_search = gr.components.Dataframe(
+                    value=df_results_init,
+                    # value=df_results,
+                    interactive=False,
+                    visible=False,
+                )
+        
+                search_bar.submit(
+                    update_table,
+                    [   
+                        
+                        hidden_leaderboard_table_for_search,
+                        model1_column,
+                        search_bar,
+                    ],
+                    leaderboard_table,
+                )
+
+                for selector in [model1_column]:
+                    selector.change(
+                        update_table,
+                        [   
+                            hidden_leaderboard_table_for_search,
+                            model1_column,
+                            search_bar,
+                        ],
+                        leaderboard_table,
+                )
+        
+        with gr.TabItem("Object", elem_id="UnlearnDiffAtk-benchmark-tab-table", id=2):
+            files = ['church','garbage','parachute','tench']
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        search_bar = gr.Textbox(
+                            placeholder=" 🔍 Search for your model (separate multiple queries with `;`) and press ENTER...",
+                            show_label=False,
+                            elem_id="search-bar",
+                        )
+                    with gr.Row():
+                        model1_column = gr.CheckboxGroup(
+                        label="Evaluation Metrics",
+                        choices=['Pre-ASR','Post-ASR','FID','CLIP-Score'],
+                        interactive=True,
+                        elem_id="column-select",
+                    )
+                   
+            for i in range(len(files)):
+                if files[i] == "church":
+                    name = "### [Unlearned Object]: "+" Church" 
+                    csv_path = './assets/'+files[i]+'.csv'
+                elif files[i] == 'garbage':
+                    name = "### [Unlearned Object]: "+" Garbage"
+                    csv_path = './assets/'+files[i]+'.csv'
+                elif files[i] == 'tench':
+                    name = "### [Unlearned Object]: "+" Tench"
+                    csv_path = './assets/'+files[i]+'.csv'
+                elif files[i] == 'parachute':
+                    name = "### [Unlearned Object]: "+" Parachute"
                     csv_path = './assets/'+files[i]+'.csv'
                 
+            
                 gr.Markdown(name)
                 df_results = load_data(csv_path)
                 df_results_init = df_results.copy()[show_columns]

src/about.py

@@ -21,10 +21,10 @@ NUM_FEWSHOT = 0 # Change with your few shot
 
 
 # Your leaderboard name
-TITLE = """<h1 align="center" id="space-title">UnlearnDiffAtk Benchmark</h1>"""
+TITLE = """<h1 align="center" id="space-title">UnlearnDiffAtk: Unlearned Diffusion Model Benchmark</h1>"""
 
 # subtitle
-SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adversarial prompt generation approach for diffusion models</h2>"""
+SUB_TITLE = """<h2 align="center" id="space-title">Effective and efficient adversarial prompt generation approach for unlearned diffusion model evaluations.</h2>"""
 
 # What does your leaderboard evaluate?
 INTRODUCTION_TEXT = """