moving parts to fragments

app.py

@@ -119,13 +119,19 @@ def get_unique_teams(teams):
 
 @st.cache_data
 def filter_teams(temp, selected_team):
-    return temp.query(f"team=='{selected_team}'")
+    mask = temp.loc[:,"team"].isin(selected_team)
+    return temp.loc[mask]
 
 def make_roc_curves(task, submission_ids):
 
     rocs = load_roc_file(task, submission_ids)
 
-    roc_chart = alt.Chart(rocs).mark_line().encode(x="fpr", y="tpr", color="team:N", detail="submission_id:N")
+    # if rocs["team"].nunique() > 1:
+    color_field = "team:N"
+    # else:
+    #     color_field = "submission_id:N"
+
+    roc_chart = alt.Chart(rocs).mark_line().encode(x="fpr", y="tpr", color=color_field, detail="submission_id:N")
 
     return roc_chart
 
@@ -145,6 +151,7 @@ st.set_page_config(
 with st.sidebar:
 
     hf_token = os.getenv("HF_TOKEN")
+    st.session_state["hf_token"] = hf_token
     password = st.text_input("Admin login:", type="password")
 
     if password == hf_token:
@@ -189,8 +196,14 @@ with st.sidebar:
     else:
         split = "public"
 
+    st.session_state["split"] = split
+
 
-def show_leaderboard(results, task):
+@st.fragment
+def show_leaderboard(task):
+    split = st.session_state.get("split","public")
+    results = load_results(task, best_only=True)
+    temp = results[f"{split}_score"].reset_index()
     source_split_map = {}
     if split == "private":
         _sol_df = pd.read_csv(COMP_CACHE / task / "solution.csv")
@@ -410,7 +423,7 @@ def make_acc(results):
         alt.Chart(results)
         .mark_circle(size=200)
         .encode(
-            x=alt.X("total_time:Q", title="🕒 Inference Time", scale=alt.Scale(type = "log")),
+            x=alt.X("total_time:Q", title="🕒 Inference Time (sec)", scale=alt.Scale(type = "log",domain = [100,100000])),
             y=alt.Y(
                 "balanced_accuracy:Q",
                 title="Balanced Accuracy",
@@ -421,7 +434,7 @@ def make_acc(results):
         .properties(width=400, height=400, title="Inference Time vs Balanced Accuracy")
     )
     diag_line = (
-        alt.Chart(pd.DataFrame(dict(t=[100, results["total_time"].max()], y=[0.5, 0.5])))
+        alt.Chart(pd.DataFrame(dict(t=[100, 100000], y=[0.5, 0.5])))
         .mark_line(color="lightgray", strokeDash=[8, 4])
         .encode(x="t", y="y")
     )
@@ -440,52 +453,61 @@ def get_heatmaps(temp):
         st.altair_chart(h3, use_container_width=True)
 
 
-def make_plots_for_task(task, split, best_only):
-    
-                
-    results = load_results(task, best_only=best_only)
+@st.fragment
+def show_charts(task):
+    split = st.session_state.get("split","public")
+    hf_token = st.session_state.get("hf_token",None)
+    results = load_results(task, best_only=True)
     temp = results[f"{split}_score"].reset_index()
     teams = get_unique_teams(temp["team"])
+   
+    if split == "private":
+        best_only = st.toggle("Best Only", value=True, key = f"best only {task}")
+        if not best_only:
+            results = load_results(task, best_only=best_only)
+            temp = results[f"{split}_score"].reset_index()
+        selected_team = st.pills("Team",["ALL"] + teams, key = f"teams {task}", default=["ALL"],selection_mode="multi")
+        
+        if selected_team is None or len(selected_team) == 0:
+            return
+
+        if "ALL" in selected_team:
+            selected_team = ["ALL"]
+        
+        if  "ALL" not in selected_team:
+            temp = filter_teams(temp, selected_team)
 
+        # with st.spinner("making plots...", show_time=True):
+    roc_scatter = make_roc(temp)
+    acc_vs_time = make_acc(temp)
 
-    t1, t2 = st.tabs(["Tables", "Charts"])
-    with t1:
-        show_leaderboard(results, task)
+    if split == "private" and hf_token is not None:
+        full_curves = st.toggle("Full curve", value=True, key=f"all curves {task}")
 
-    with t2:
-        # st.write(temp)
-        if split == "private":
-            best_only = st.toggle("Best Only", value=True, key = f"best only {task}")
-            if not best_only:
-                results = load_results(task, best_only=best_only)
-                temp = results[f"{split}_score"].reset_index()
-            selected_team = st.pills("Team",["ALL"] + teams, key = f"teams {task}", default="ALL")
-            if not selected_team:
-                selected_team = "ALL"
-            if selected_team != "ALL":
-                temp = filter_teams(temp, selected_team)
+        if full_curves:
+            roc_scatter = make_roc_curves(task, temp["submission_id"].values.tolist()) + roc_scatter
 
-        # with st.spinner("making plots...", show_time=True):
-        roc_scatter = make_roc(temp)
-        acc_vs_time = make_acc(temp)
+        st.altair_chart(roc_scatter | acc_vs_time, use_container_width=False)
+    else:
+        st.altair_chart(roc_scatter | acc_vs_time, use_container_width=False)
 
-        if split == "private" and hf_token is not None:
-            full_curves = st.toggle("Full curve", value=True, key=f"all curves {task}")
+    st.info(f"loading {temp['submission_id'].nunique()} submissions")
 
-            if full_curves:
-                roc_scatter = make_roc_curves(task, temp["submission_id"].values.tolist()) + roc_scatter
 
-            st.altair_chart(roc_scatter | acc_vs_time, use_container_width=False)
-        else:
-            st.altair_chart(roc_scatter | acc_vs_time, use_container_width=False)
+def make_plots_for_task(task):
 
-        st.info(f"loading {temp['submission_id'].nunique()} submissions")
+    t1, t2 = st.tabs(["Tables", "Charts"])
+    with t1:
+        show_leaderboard(task)
+
+    with t2:
+        show_charts(task)
+        
 
 
 
 updated = get_updated_time()
 st.markdown(updated)
-best_only = True
 
 
 tp, t1, volume_tab, all_submission_tab = st.tabs(
@@ -493,10 +515,10 @@ tp, t1, volume_tab, all_submission_tab = st.tabs(
 )
 with tp:
     "*Detection of Synthetic Video Content. Video files are unmodified from the original output from the models or the real sources.*"
-    make_plots_for_task(TASKS[0], split, best_only)
+    make_plots_for_task(TASKS[0])
 with t1:
     "*Detection of Synthetic Video Content. Video files are unmodified from the original output from the models or the real sources.*"
-    make_plots_for_task(TASKS[1], split, best_only)
+    make_plots_for_task(TASKS[1])
 
 with volume_tab:
     subs = get_volume()