add organizations filter

app.py

@@ -98,6 +98,7 @@ merged_dfs = {k: format_data(v) for k, v in merged_dfs.items()}
 # get constants
 min_elo_score, max_elo_score, upper_models_per_month = get_constants(merged_dfs)
 date_updated = elo_results["full"]["last_updated_datetime"].split(" ")[0]
+orgs = merged_dfs["Overall"].Organization.unique().tolist()
 
 ###################
 ### Build and Plot Data
@@ -109,11 +110,13 @@ def get_data_split(dfs, set_name):
     return df.reset_index(drop=True)
 
 
-def filter_df(min_score, max_models_per_month, set_selector):
+def filter_df(min_score, max_models_per_month, set_selector, org_selector):
     df = get_data_split(merged_dfs, set_name=set_selector)
 
     # filter data
-    filtered_df = df[(df["rating"] >= min_score)]
+    filtered_df = df[
+        (df["rating"] >= min_score) & (df["Organization"].isin(org_selector))
+    ]
 
     filtered_df = (
         filtered_df.groupby(["Month-Year", "License"], group_keys=False)
@@ -216,36 +219,45 @@ with gr.Blocks(
         </div>
         """
     )
-
-    with gr.Row(variant="compact"):
-        set_selector = gr.Dropdown(
-            choices=list(CAT_NAME_TO_EXPLANATION.keys()),
-            label="Select Category",
-            value="Overall",
-            info="Select the category to visualize",
-        )
-        min_score = gr.Slider(
-            minimum=min_elo_score,
-            maximum=max_elo_score,
-            value=(max_elo_score - min_elo_score) * 0.3 + min_elo_score,
-            step=50,
-            label="Minimum ELO Score",
-            info="Filter out low scoring models",
-        )
-        max_models_per_month = gr.Slider(
-            value=upper_models_per_month - 2,
-            minimum=1,
-            maximum=upper_models_per_month,
-            step=1,
-            label="Max Models per Month (per License)",
-            info="Limit to N best models per month per license to reduce clutter",
-        )
-        toggle_annotations = gr.Radio(
-            choices=[True, False],
-            label="Overlay Best Model Name",
-            value=True,
-            info="Toggle to overlay the name of the best model per month per license",
-        )
+    with gr.Group():
+        with gr.Row(variant="compact"):
+            set_selector = gr.Dropdown(
+                choices=list(CAT_NAME_TO_EXPLANATION.keys()),
+                label="Select Category",
+                value="Overall",
+                info="Select the category to visualize",
+            )
+            min_score = gr.Slider(
+                minimum=min_elo_score,
+                maximum=max_elo_score,
+                value=(max_elo_score - min_elo_score) * 0.3 + min_elo_score,
+                step=50,
+                label="Minimum ELO Score",
+                info="Filter out low scoring models",
+            )
+            max_models_per_month = gr.Slider(
+                value=upper_models_per_month - 2,
+                minimum=1,
+                maximum=upper_models_per_month,
+                step=1,
+                label="Max Models per Month (per License)",
+                info="Limit to N best models per month per license to reduce clutter",
+            )
+            toggle_annotations = gr.Radio(
+                choices=[True, False],
+                label="Overlay Best Model Name",
+                value=True,
+                info="Toggle to overlay the name of the best model per month per license",
+            )
+        with gr.Row(variant="compact"):
+            with gr.Accordion("More options", open=False):
+                org_selector = gr.Dropdown(
+                    choices=orgs,
+                    label="Filter by Organization",
+                    value=orgs,
+                    multiselect=True,
+                    info="Limit organizations included in plot",
+                )
 
     # Show plot
     plot = gr.Plot()
@@ -253,31 +265,37 @@ with gr.Blocks(
 
     demo.load(
         fn=filter_df,
-        inputs=[min_score, max_models_per_month, set_selector],
+        inputs=[min_score, max_models_per_month, set_selector, org_selector],
         outputs=filtered_df,
     ).then(fn=build_plot, inputs=[toggle_annotations, filtered_df], outputs=plot)
 
     min_score.change(
         fn=filter_df,
-        inputs=[min_score, max_models_per_month, set_selector],
+        inputs=[min_score, max_models_per_month, set_selector, org_selector],
         outputs=filtered_df,
     ).then(fn=build_plot, inputs=[toggle_annotations, filtered_df], outputs=plot)
 
     max_models_per_month.change(
         fn=filter_df,
-        inputs=[min_score, max_models_per_month, set_selector],
+        inputs=[min_score, max_models_per_month, set_selector, org_selector],
         outputs=filtered_df,
     ).then(fn=build_plot, inputs=[toggle_annotations, filtered_df], outputs=plot)
 
     toggle_annotations.change(
         fn=filter_df,
-        inputs=[min_score, max_models_per_month, set_selector],
+        inputs=[min_score, max_models_per_month, set_selector, org_selector],
         outputs=filtered_df,
     ).then(fn=build_plot, inputs=[toggle_annotations, filtered_df], outputs=plot)
 
     set_selector.change(
         fn=filter_df,
-        inputs=[min_score, max_models_per_month, set_selector],
+        inputs=[min_score, max_models_per_month, set_selector, org_selector],
+        outputs=filtered_df,
+    ).then(fn=build_plot, inputs=[toggle_annotations, filtered_df], outputs=plot)
+
+    org_selector.change(
+        fn=filter_df,
+        inputs=[min_score, max_models_per_month, set_selector, org_selector],
         outputs=filtered_df,
     ).then(fn=build_plot, inputs=[toggle_annotations, filtered_df], outputs=plot)