Update app.py

app.py

@@ -94,10 +94,10 @@ def restart_space():
 # }
 # def color_model_type_column(df, color_map):
 #     """
-#     Apply color to the 'Model Type' column of the DataFrame based on a given color mapping.
+#     Apply color to the 'Modality' column of the DataFrame based on a given color mapping.
 
 #     Parameters:
-#     df (pd.DataFrame): The DataFrame containing the 'Model Type' column.
+#     df (pd.DataFrame): The DataFrame containing the 'Modality' column.
 #     color_map (dict): A dictionary mapping model types to colors.
 
 #     Returns:
@@ -113,7 +113,7 @@ def restart_space():
 #     format_dict['Overall Score'] = "{:.2f}"
 #     format_dict[''] = "{:d}"
 
-#     return df.style.applymap(apply_color, subset=['Model Type']).format(format_dict, na_rep='')
+#     return df.style.applymap(apply_color, subset=['Modality']).format(format_dict, na_rep='')
 
 def regex_table(dataframe, regex, filter_button, style=True):
     """
@@ -127,10 +127,10 @@ def regex_table(dataframe, regex, filter_button, style=True):
     # if filter_button, remove all rows with "ai2" in the model name
     update_scores = False
     if isinstance(filter_button, list) or isinstance(filter_button, str):
-        if "Integrated LVLM" not in filter_button:
-            dataframe = dataframe[~dataframe["Model Type"].str.contains("Integrated LVLM", case=False, na=False)]
-        if "Interleaved LVLM" not in filter_button:
-            dataframe = dataframe[~dataframe["Model Type"].str.contains("Interleaved LVLM", case=False, na=False)]
+        if "Image-Text-to-Text" not in filter_button:
+            dataframe = dataframe[~dataframe["Modality"].str.contains("Image-Text-to-Text", case=False, na=False)]
+        if "Video-Text-to-Text" not in filter_button:
+            dataframe = dataframe[~dataframe["Modality"].str.contains("Video-Text-to-Text", case=False, na=False)]
     # Filter the dataframe such that 'model' contains any of the regex patterns
     data = dataframe[dataframe["Model"].str.contains(combined_regex, case=False, na=False)]
 
@@ -177,46 +177,46 @@ def avg_all_perspective(orig_df: pd.DataFrame, columns_name: list, meta_data=MET
     
 data = {
     "Model": [
-        "MiniGPT-5", "EMU-2", "GILL", "Anole",
-        "GPT-4o | Openjourney", "GPT-4o | SD-3", "GPT-4o | SD-XL", "GPT-4o | Flux",
-        "Gemini-1.5 | Openjourney", "Gemini-1.5 | SD-3", "Gemini-1.5 | SD-XL", "Gemini-1.5 | Flux",
-        "LLAVA-34b | Openjourney", "LLAVA-34b | SD-3", "LLAVA-34b | SD-XL", "LLAVA-34b | Flux",
-        "Qwen-VL-70b | Openjourney", "Qwen-VL-70b | SD-3", "Qwen-VL-70b | SD-XL", "Qwen-VL-70b | Flux"
+        "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B",
+        "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B",
+        "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B",
+        "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B",
+        "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B", "Beaver-Vision-11B",
     ],
-    "Model Type":[
-        "Interleaved LVLM", "Interleaved LVLM", "Interleaved LVLM", "Interleaved LVLM",
-        "Integrated LVLM", "Integrated LVLM", "Integrated LVLM", "Integrated LVLM",
-        "Integrated LVLM", "Integrated LVLM", "Integrated LVLM", "Integrated LVLM",
-        "Integrated LVLM", "Integrated LVLM", "Integrated LVLM", "Integrated LVLM",
-        "Integrated LVLM", "Integrated LVLM", "Integrated LVLM", "Integrated LVLM",
+    "Modality":[
+        "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text",
+        "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text",
+        "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text",
+        "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text",
+        "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text", "Image-Text-to-Text",
     ],
-    "Situational analysis": [
-        47.63, 39.65, 46.72, 48.95,
-        53.05, 53.00, 56.12, 54.97,
-        48.08, 47.48, 49.43, 47.07,
-        54.12, 54.72, 55.97, 54.23,
-        52.73, 54.98, 52.58, 54.23
+    "Correctness of Information": [
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
     ],
-    "Project-based learning": [
-        55.12, 46.12, 57.57, 59.05,
-        71.40, 71.20, 73.25, 68.80,
-        67.93, 68.70, 71.85, 68.33,
-        73.47, 72.55, 74.60, 71.32,
-        71.63, 71.87, 73.57, 69.47
+    "Detail Orientation": [
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
     ],
-    "Multi-step reasoning": [
-        42.17, 50.75, 39.33, 51.72,
-        53.67, 53.67, 53.67, 53.67,
-        60.05, 60.05, 60.05, 60.05,
-        47.28, 47.28, 47.28, 47.28,
-        55.63, 55.63, 55.63, 55.63
+    "Safety": [
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
     ],
     "AVG": [
-        50.92, 45.33, 51.58, 55.22,
-        63.65, 63.52, 65.47, 62.63,
-        61.57, 61.87, 64.15, 61.55,
-        63.93, 63.57, 65.05, 62.73,
-        64.05, 64.75, 65.12, 63.18
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
+        100.00, 100.00, 100.00, 100.00,
     ]
 }
 df = pd.DataFrame(data)
@@ -239,9 +239,9 @@ with gr.Blocks(css=custom_css) as app:
                     show_label=False
                 )
                 model_type_overall = gr.CheckboxGroup(
-                    choices=["Interleaved LVLM", "Integrated LVLM"], 
-                    value=["Interleaved LVLM", "Integrated LVLM"], 
-                    label="Model Type", 
+                    choices=["Image-Text-to-Text", "Video-Text-to-Text"], 
+                    value=["Image-Text-to-Text", "Video-Text-to-Text"], 
+                    label="Modality", 
                     show_label=False, 
                     interactive=True,
                 )
@@ -257,7 +257,7 @@ with gr.Blocks(css=custom_css) as app:
                     regex_table(
                         df.copy(), 
                         "", 
-                        ["Interleaved LVLM", "Integrated LVLM"]
+                        ["Video-Text-to-Text", "Image-Text-to-Text"]
                      ),
                     headers=df.columns.tolist(),
                     elem_id="Align_Anything_leadboard_overall",