small fixes.

src/streamlit_app.py

@@ -72,42 +72,39 @@ else:
     else:
         gifs_df = pd.DataFrame(rows)
 
-        # Dropdowns (4-tuple)
-        feature_options = sorted(gifs_df["features"].unique().tolist())
-        hidden_options = sorted(gifs_df["hidden_dim"].unique().tolist())
-        outof_options = sorted(gifs_df["sparsity_out_of"].unique().tolist())
-
-        c1, c2, c3, c4 = st.columns([1, 1, 1, 1], gap="small")
-        with c1:
-            selected_features = st.selectbox("nf (features)", feature_options)
-        with c2:
-            selected_hidden = st.selectbox("nh (hidden dim)", hidden_options)
-        with c3:
-            selected_outof = st.selectbox("ni (out of)", outof_options)
-
-        # sparsity options depend on the first 3 selections, so compute them dynamically
-        sparsity_options = sorted(
-            gifs_df[
-                (gifs_df["features"] == selected_features)
-                & (gifs_df["hidden_dim"] == selected_hidden)
-                & (gifs_df["sparsity_out_of"] == selected_outof)
-                ]["sparsity"].unique().tolist()
+        # Single dropdown selecting a 4-tuple (nf, nh, sparsity, out_of)
+        tuples_df = (
+            gifs_df[["features", "hidden_dim", "sparsity", "sparsity_out_of"]]
+            .drop_duplicates()
+            .sort_values(["features", "hidden_dim", "sparsity_out_of", "sparsity"])
+            .reset_index(drop=True)
         )
 
-        with c4:
-            selected_sparsity = st.selectbox(
-                "instance+1 (sparsity)",
-                sparsity_options if sparsity_options else [None],
-                disabled=(len(sparsity_options) == 0),
-            )
+        # readable labels
+        labels = [
+            f"nf={r.features}, nh={r.hidden_dim}, sparsity={r.sparsity}/{r.sparsity_out_of}"
+            for r in tuples_df.itertuples(index=False)
+        ]
 
-        # Filter on the full 4-tuple
+        selected_label = st.selectbox("Select (nf, nh, sparsity/out_of)", labels)
+
+        # decode selection
+        idx = labels.index(selected_label)
+        sel = tuples_df.iloc[idx]
+
+        selected_features = int(sel["features"])
+        selected_hidden = int(sel["hidden_dim"])
+        selected_sparsity = int(sel["sparsity"])
+        selected_outof = int(sel["sparsity_out_of"])
+
+        # Filter on full 4-tuple
         filtered = gifs_df[
             (gifs_df["features"] == selected_features)
             & (gifs_df["hidden_dim"] == selected_hidden)
-            & (gifs_df["sparsity_out_of"] == selected_outof)
             & (gifs_df["sparsity"] == selected_sparsity)
-            ].copy()
+            & (gifs_df["sparsity_out_of"] == selected_outof)
+        ].copy()
+
 
         if filtered.empty:
             st.info("No GIFs found for that (nf, nh) combination.")