Improvements to the results analysis app

[fanny-riols]

Summary

• Cascading System filter: replaced the flat Model filter in the cross-run comparison with a System filter that shows a human-readable label and is constrained by the selected Provider, so irrelevant systems are hidden automatically.
• Complete-runs-only & hide-incomplete toggles: two new sidebar toggles filter out partial runs from the comparison table and scatter plot; both are URL-bound so the view is shareable.
• Per-sample heatmap: a new interactive heatmap below the comparison tables shows per-record metric scores across all runs, with a metric selector, swap-axes toggle, and correct RdYlGn / RdYlGn_r colorscale based on whether lower or higher is better.
• Pinned columns & row numbers: the #, link, and System columns are now pinned in the dataframe so they stay visible when scrolling horizontally; the link column is labelled "Run".
• URL-bound state: the latest-run toggle and show-sub-metrics toggle are now bound to query params, so deep-linking to a specific view works end-to-end.
• Lower-is-better metric set: restricted the inverted colorscale to response_speed and stt_wer only (was applied too broadly).
• Latest-run filter fix: timestamp-only folders now derive their system name from config.json so that different models sharing the same folder format are treated as distinct systems and deduplicated correctly.

[gabegma]

I detect automatically now from #65

[JosephMarinier]

Is it important that the # column start at 1? If it can start at 0, we can simply show the index column by removing hide_index=True.

Suggested change

	sub_df.insert(0, "#", range(1, len(sub_df) + 1))
	sub_df.insert(1, "link", link_series)
	sub_df = sub_df.rename(columns={**id_rename, **composite_rename, **col_rename})
	score_cols = [composite_rename[c] for c in composites] + [col_rename[m] for m in metrics]
	styled = sub_df.style.map(_color_cell, subset=score_cols)
	styled = styled.format(dict.fromkeys(score_cols, "{:.3f}"), na_rep="—")
	st.dataframe(
	styled,
	hide_index=True,
	sub_df.insert(0, "link", link_series)
	sub_df = sub_df.rename(columns={**id_rename, **composite_rename, **col_rename})
	score_cols = [composite_rename[c] for c in composites] + [col_rename[m] for m in metrics]
	styled = sub_df.style.map(_color_cell, subset=score_cols)
	styled = styled.format(dict.fromkeys(score_cols, "{:.3f}"), na_rep="—")
	st.dataframe(
	styled,

[JosephMarinier]

If we apply this suggestion, we can also remove this since the index column is pinned by default.

Suggested change

"#": st.column_config.NumberColumn("#", width=50, pinned=True),

[JosephMarinier]

Streamlit now has a st.space() function that can be used instead of st.markdown("<div style='padding-top:28px'></div>", unsafe_allow_html=True). Also, we can use st.container(horizontal=True) to that items flow naturally, instead of forcing arbitrary columns with st.columns(). Lastly, with a few tweaks, we can center the toggle to the selectbox:

Suggested change

	ctrl_col, swap_col = st.columns([4, 1])
	with ctrl_col:
	selected_heatmap_metric = st.selectbox(
	"Metric",
	available_heatmap_metrics,
	format_func=_format_metric_name,
	key="heatmap_metric",
	)
	with swap_col:
	st.markdown("<div style='padding-top:28px'></div>", unsafe_allow_html=True)
	swap_axes = st.toggle("Swap axes", key="heatmap_swap_axes")
	with st.container(horizontal=True, vertical_alignment="center", gap="medium"):
	selected_heatmap_metric = st.selectbox(
	"Metric",
	available_heatmap_metrics,
	format_func=_format_metric_name,
	key="heatmap_metric",
	)
	with st.container(width="content", gap=None):
	st.space(28) # Height of the selectbox's label.
	swap_axes = st.toggle("Swap axes", key="heatmap_swap_axes")