Updated with latest versions

pyproject.toml

@@ -1,12 +1,12 @@
 [project]
 name = "persona-ui"
-version = "0.1.1"
+version = "0.2.0"
 description = "Streamlit UI for persona-vectors"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "persona-vectors>=0.3.2",
-    "persona-data>=0.2.3",
+    "persona-vectors>=0.4.0",
+    "persona-data>=0.2.5",
     "streamlit>=1.44.0",
     "plotly>=6.6.0",
     "python-dotenv>=1.2.2",
@@ -15,7 +15,7 @@ dependencies = [
 
 # Local development:
 [tool.uv.sources]
-persona-vectors = { path = "../persona-vectors", editable = true }
+# persona-vectors = { path = "../persona-vectors", editable = true }
 # persona-data = { path = "../persona-data", editable = true }
 
 # [build-system]

tabs/compare.py

@@ -1,9 +1,14 @@
+from itertools import combinations
+
 import streamlit as st
 import torch
 from persona_data.environment import get_artifacts_dir
 from persona_data.prompts import BASELINE_PERSONA_ID
-from persona_vectors.analysis import LayeredSamples, load_persona_mean_samples
-from persona_vectors.artifacts import SUPPORTED_VARIANTS, ActivationStore
+from persona_vectors.analysis import load_persona_mean_samples
+from persona_vectors.artifacts import (
+    PERSONA_VARIANTS,
+    ActivationStore,
+)
 from persona_vectors.artifacts import list_layers as list_available_layers
 from persona_vectors.artifacts import list_personas as list_available_personas
 from persona_vectors.artifacts import load_mean_activations, load_persona_names
@@ -37,7 +42,6 @@ _list_layers_cached = st.cache_data(show_spinner=False)(list_available_layers)
 _LAST_COSINE_PERSONAS_KEY = "compare:last_personas:cosine"
 _LAST_PROJECTION_PERSONAS_KEY = "compare:last_personas:projection"
 _LAST_MASK_STRATEGY_KEY = "compare:last_mask_strategy"
-_COMPARABLE_VARIANTS = tuple(v for v in SUPPORTED_VARIANTS if v != "baseline")
 
 
 def _select_artifact_personas(
@@ -155,7 +159,7 @@ def _render_cosine_similarity(
     store: ActivationStore,
     mask_strategy: MaskStrategy,
 ) -> None:
-    if len(_COMPARABLE_VARIANTS) < 2:
+    if len(PERSONA_VARIANTS) < 2:
         st.info("Need at least two non-baseline variants for cosine comparison.")
         return
 
@@ -163,7 +167,7 @@ def _render_cosine_similarity(
     with col1:
         variant_a = st.selectbox(
             "Variant A",
-            options=_COMPARABLE_VARIANTS,
+            options=PERSONA_VARIANTS,
             index=0,
             format_func=prompt_variant_label,
             key=widget_key("load", "variant_a"),
@@ -171,8 +175,8 @@ def _render_cosine_similarity(
     with col2:
         variant_b = st.selectbox(
             "Variant B",
-            options=_COMPARABLE_VARIANTS,
-            index=min(1, len(_COMPARABLE_VARIANTS) - 1),
+            options=PERSONA_VARIANTS,
+            index=min(1, len(PERSONA_VARIANTS) - 1),
             format_func=prompt_variant_label,
             key=widget_key("load", "variant_b"),
         )
@@ -207,6 +211,13 @@ def _render_cosine_similarity(
         variant_a,
         variant_b,
     )
+    pairs_filename = _filename(
+        "compare",
+        "cosine_pairs",
+        store.model_name,
+        mask_strategy.value,
+        "_".join(PERSONA_VARIANTS),
+    )
 
     if st.button("Compare vectors", type="primary"):
         traces, loaded_names, errors = load_mean_activations(
@@ -242,13 +253,57 @@ def _render_cosine_similarity(
             title=f"{prompt_variant_label(variant_a)} vs {prompt_variant_label(variant_b)}",
             show=False,
         )
-        st.session_state[cosine_fig_key] = (fig, len(traces))
+
+        pair_traces = []
+        pair_errors: list[str] = []
+        for left, right in combinations(PERSONA_VARIANTS, 2):
+            pair_data, _, pair_load_errors = load_mean_activations(
+                store.root_dir,
+                store.model_name,
+                persona_ids,
+                left,
+                right,
+                mask_strategy=mask_strategy,
+            )
+            pair_errors.extend(pair_load_errors)
+            if not pair_data:
+                continue
+            pair_traces.append(
+                (
+                    f"{prompt_variant_label(left)} vs {prompt_variant_label(right)}",
+                    torch.stack([short for _, short, _ in pair_data]).mean(dim=0),
+                    torch.stack([long for _, _, long in pair_data]).mean(dim=0),
+                )
+            )
+
+        if pair_errors:
+            for err in pair_errors:
+                st.warning(f"Skipped pair trace: `{err}`")
+        pair_fig = (
+            plot_layer_similarity(
+                pair_traces,
+                title="Variant-pair cosine similarity averaged over selected personas",
+                show=False,
+            )
+            if pair_traces
+            else None
+        )
+        st.session_state[cosine_fig_key] = (fig, pair_fig, len(traces), len(pair_traces))
 
     if cosine_fig_key in st.session_state:
-        fig, n_traces = st.session_state[cosine_fig_key]
+        fig, pair_fig, n_traces, n_pair_traces = st.session_state[cosine_fig_key]
         st.plotly_chart(fig, width="stretch")
-        _render_save_buttons([fig], [filename], "cosine")
+        figs = [fig]
+        filenames = [filename]
+        if pair_fig is not None:
+            st.subheader("Variant pairs")
+            st.plotly_chart(pair_fig, width="stretch")
+            figs.append(pair_fig)
+            filenames.append(pairs_filename)
+        _render_save_buttons(figs, filenames, "cosine")
         st.success(f"Loaded {n_traces} personas for cosine comparison.")
+        if pair_fig is not None:
+            st.caption(f"Generated {n_pair_traces} averaged variant-pair trace(s).")
 
 
 def _select_single_variant_samples(
@@ -258,9 +313,9 @@ def _select_single_variant_samples(
 ) -> tuple[str, list[str], str, list[int]] | None:
     variant = st.selectbox(
         "Variant",
-        options=_COMPARABLE_VARIANTS,
-        index=_COMPARABLE_VARIANTS.index("biography")
-        if "biography" in _COMPARABLE_VARIANTS
+        options=PERSONA_VARIANTS,
+        index=PERSONA_VARIANTS.index("biography")
+        if "biography" in PERSONA_VARIANTS
         else 0,
         format_func=prompt_variant_label,
         key=widget_key("load", "variant", scope),
@@ -316,7 +371,7 @@ def _baseline_available(
     return BASELINE_PERSONA_ID in list_available_personas(
         store.root_dir,
         store.model_name,
-        ["baseline"],
+        [BASELINE_PERSONA_ID],
         mask_strategy=mask_strategy,
         warn_missing=False,
     )
@@ -336,30 +391,11 @@ def _render_baseline_reference_toggle(
         help=(
             "Adds the single saved baseline artifact as one reference sample."
             if available
-            else "Run extraction for the baseline variant first."
+            else "Run Assistant baseline extraction first."
         ),
     )
 
 
-def _append_baseline_reference(
-    store: ActivationStore,
-    mask_strategy: MaskStrategy,
-    samples: LayeredSamples,
-) -> LayeredSamples:
-    baseline_samples = load_persona_mean_samples(
-        store.root_dir,
-        store.model_name,
-        "baseline",
-        mask_strategy=mask_strategy,
-        persona_ids=[BASELINE_PERSONA_ID],
-    )
-    return LayeredSamples(
-        vectors=torch.cat([samples.vectors, baseline_samples.vectors], dim=0),
-        labels=[*samples.labels, *baseline_samples.labels],
-        hover_text=[*samples.hover_text, *baseline_samples.hover_text],
-    )
-
-
 def _render_similarity_matrix(
     store: ActivationStore,
     mask_strategy: MaskStrategy,
@@ -386,7 +422,7 @@ def _render_similarity_matrix(
         variant,
         "persona_mean",
         persona_key,
-        "baseline" if include_baseline else "no_baseline",
+        BASELINE_PERSONA_ID if include_baseline else "no_baseline",
     )
     filename = _filename(
         "compare",
@@ -396,7 +432,7 @@ def _render_similarity_matrix(
         variant,
         "persona_mean",
         persona_key,
-        "baseline" if include_baseline else "",
+        BASELINE_PERSONA_ID if include_baseline else "",
     )
 
     if st.button("Generate similarity matrix", type="primary"):
@@ -407,9 +443,8 @@ def _render_similarity_matrix(
                 variant,
                 mask_strategy=mask_strategy,
                 persona_ids=persona_ids,
+                include_baseline=include_baseline,
             )
-            if include_baseline:
-                samples = _append_baseline_reference(store, mask_strategy, samples)
             matrix_fig = build_layered_figure(
                 samples,
                 "similarity",
@@ -479,7 +514,7 @@ def _render_embedding_analysis(
         variant,
         "persona_mean",
         persona_key,
-        "baseline" if include_baseline else "no_baseline",
+        BASELINE_PERSONA_ID if include_baseline else "no_baseline",
     )
     filename = _filename(
         "compare",
@@ -489,7 +524,7 @@ def _render_embedding_analysis(
         variant,
         "persona_mean",
         persona_key,
-        "baseline" if include_baseline else "",
+        BASELINE_PERSONA_ID if include_baseline else "",
     )
 
     if st.button(f"Generate {analysis_mode} projection", type="primary"):
@@ -500,9 +535,8 @@ def _render_embedding_analysis(
                 variant,
                 mask_strategy=mask_strategy,
                 persona_ids=persona_ids,
+                include_baseline=include_baseline,
             )
-            if include_baseline:
-                samples = _append_baseline_reference(store, mask_strategy, samples)
             fig = build_layered_figure(
                 samples,
                 figure_kind,

tabs/extract.py

@@ -5,10 +5,10 @@ import streamlit as st
 from persona_data.prompts import (
     BASELINE_PERSONA_ID,
     BASELINE_PERSONA_NAME,
-    system_prompt_for_variant,
+    format_roleplay_prompt,
 )
 from persona_data.synth_persona import PersonaData, QAPair
-from persona_vectors.artifacts import SUPPORTED_VARIANTS
+from persona_vectors.artifacts import PERSONA_VARIANTS
 from persona_vectors.extraction import (
     MaskStrategy,
     PreparedInput,
@@ -28,6 +28,7 @@ from utils.runtime import cached_model
 # Cross-model / remote-switch persistence — same pattern as compare.py.
 # Written on every render so selections survive model or NDIF toggles.
 _LAST_VARIANTS_KEY = "extract:last_variants"
+_LAST_BASELINE_KEY = "extract:last_include_baseline"
 _LAST_PERSONA_IDS_KEY = "extract:last_persona_ids"
 _LAST_QA_TYPE_KEY = "extract:last_qa_type"
 _LAST_DIFFICULTY_KEY = "extract:last_difficulty"
@@ -37,29 +38,24 @@ _LAST_MASK_STRATEGY_KEY = "extract:last_mask_strategy"
 _QA_TYPE_OPTIONS = ["all", "explicit", "implicit"]
 
 
-def _baseline_persona() -> PersonaData:
-    return PersonaData(
-        id=BASELINE_PERSONA_ID,
-        persona={"first_name": BASELINE_PERSONA_NAME, "last_name": ""},
-        templated_view=BASELINE_PERSONA_NAME,
-        biography_view=BASELINE_PERSONA_NAME,
-    )
-
-
 def _build_run_plan(
     selected_variants: list[str],
     runs: list[tuple[PersonaData, list[QAPair]]],
-) -> list[tuple[PersonaData, list[QAPair], str]]:
-    regular_variants = [v for v in selected_variants if v != "baseline"]
-    run_plan = [
-        (persona, qa_pairs, variant)
-        for persona, qa_pairs in runs
-        for variant in regular_variants
-    ]
-    if "baseline" in selected_variants:
-        _, baseline_qa_pairs = runs[0]
-        run_plan.append((_baseline_persona(), baseline_qa_pairs, "baseline"))
-    return run_plan
+) -> list[tuple[PersonaData | None, list[QAPair], str]]:
+    """Expand selected variants × personas into one (persona, qa, variant) per call.
+
+    The baseline variant is run once across the first persona's QA pairs and
+    has no associated persona.
+    """
+    plan: list[tuple[PersonaData | None, list[QAPair], str]] = []
+    for variant in selected_variants:
+        if variant == BASELINE_PERSONA_ID:
+            _, qa_pairs = runs[0]
+            plan.append((None, qa_pairs, variant))
+        else:
+            for persona, qa_pairs in runs:
+                plan.append((persona, qa_pairs, variant))
+    return plan
 
 
 def _extract_widget_key(
@@ -165,25 +161,41 @@ def render_extract_tab(remote: bool, model_name: str, dataset_source: str) -> No
     if dataset_source == "Local JSONL upload":
         _render_local_dataset_uploads()
 
-    last_variants = st.session_state.get(_LAST_VARIANTS_KEY, list(SUPPORTED_VARIANTS))
-    default_variants = [v for v in last_variants if v in SUPPORTED_VARIANTS] or list(
-        SUPPORTED_VARIANTS
+    last_variants = st.session_state.get(
+        _LAST_VARIANTS_KEY, [*PERSONA_VARIANTS, BASELINE_PERSONA_ID]
     )
-    selected_variants = st.multiselect(
-        "Prompt variants",
-        options=SUPPORTED_VARIANTS,
-        default=default_variants,
+    default_persona_variants = [
+        v for v in last_variants if v in PERSONA_VARIANTS
+    ] or list(PERSONA_VARIANTS)
+    selected_persona_variants = st.multiselect(
+        "Persona variants",
+        options=PERSONA_VARIANTS,
+        default=default_persona_variants,
         format_func=prompt_variant_label,
-        key=_extract_widget_key(model_name, remote, dataset_source, "prompt_variants"),
+        key=_extract_widget_key(model_name, remote, dataset_source, "persona_variants"),
+        help="Extract these variants for each selected persona.",
+    )
+    include_baseline_default = st.session_state.get(
+        _LAST_BASELINE_KEY, BASELINE_PERSONA_ID in last_variants
     )
+    include_baseline = st.checkbox(
+        "Extract Assistant baseline",
+        value=include_baseline_default,
+        key=_extract_widget_key(model_name, remote, dataset_source, "baseline"),
+        help=(
+            "Extracts the persona-less Assistant prompt once using the first "
+            "selected persona's QA set."
+        ),
+    )
+    selected_variants = [
+        *selected_persona_variants,
+        *([BASELINE_PERSONA_ID] if include_baseline else []),
+    ]
     st.session_state[_LAST_VARIANTS_KEY] = selected_variants
+    st.session_state[_LAST_BASELINE_KEY] = include_baseline
     if not selected_variants:
-        st.info("Select at least one prompt variant.")
+        st.info("Select at least one persona variant or enable the baseline.")
         return
-    if "baseline" in selected_variants:
-        st.caption(
-            "Baseline uses the persona-less Assistant prompt and is saved once as an Assistant reference. It uses the first selected persona's QA set for extraction."
-        )
 
     try:
         dataset, dataset_status = load_dataset(
@@ -333,24 +345,30 @@ def render_extract_tab(remote: bool, model_name: str, dataset_source: str) -> No
     with preview_col:
         preview_clicked = st.button("Preview tokens", use_container_width=True)
 
+    run_plan = _build_run_plan(selected_variants, runs)
+
+    def _row_label(persona: PersonaData | None, variant: str) -> str:
+        name = persona.name if persona is not None else BASELINE_PERSONA_NAME
+        return f"{name} · {prompt_variant_label(variant)}"
+
     if preview_clicked:
         with st.spinner("Loading tokenizer..."):
             model = cached_model(model_name=model_name, remote=remote)
         st.markdown(_TOKEN_LEGEND, unsafe_allow_html=True)
-        for persona, qa_pairs, variant in _build_run_plan(selected_variants, runs):
-            system_prompt = system_prompt_for_variant(persona, variant)
+        for persona, qa_pairs, variant in run_plan:
+            system_prompt = (
+                format_roleplay_prompt()
+                if persona is None
+                else format_roleplay_prompt(getattr(persona, f"{variant}_view"))
+            )
             prepared = prepare_inputs_for_strategy(
                 tokenizer=model.tokenizer,
                 system_prompt=system_prompt,
                 qa_pairs=qa_pairs[:max_questions],
                 mask_strategy=mask_strategy,
             )
-            display_name = (
-                BASELINE_PERSONA_NAME if variant == "baseline" else persona.name
-            )
-            st.caption(f"{display_name} · {prompt_variant_label(variant)}")
-            shown = prepared[:_MAX_PREVIEW_SAMPLES]
-            for i, p in enumerate(shown):
+            st.caption(_row_label(persona, variant))
+            for i, p in enumerate(prepared[:_MAX_PREVIEW_SAMPLES]):
                 question = (
                     p.question if len(p.question) <= 60 else p.question[:57] + "..."
                 )
@@ -383,31 +401,25 @@ def render_extract_tab(remote: bool, model_name: str, dataset_source: str) -> No
         model = cached_model(model_name=model_name, remote=remote)
 
     try:
-        run_plan = _build_run_plan(selected_variants, runs)
         total_steps = len(run_plan)
-        step = 0
         results = []
-
-        for persona, qa_pairs, variant in run_plan:
-            display_name = (
-                BASELINE_PERSONA_NAME if variant == "baseline" else persona.name
-            )
+        for step, (persona, qa_pairs, variant) in enumerate(run_plan):
             progress.progress(
                 step / total_steps if total_steps else 1.0,
-                text=f"{display_name} · {prompt_variant_label(variant)} ({step + 1}/{total_steps})",
+                text=f"{_row_label(persona, variant)} ({step + 1}/{total_steps})",
             )
-            variant_results = run_extraction(
-                model=model,
-                model_name=model_name,
-                persona=persona,
-                qa_pairs=qa_pairs[:max_questions],
-                variants=(variant,),
-                mask_strategy=mask_strategy,
-                remote=remote,
-                on_status=_on_ndif_status if remote else None,
+            results.extend(
+                run_extraction(
+                    model=model,
+                    model_name=model_name,
+                    qa_pairs=qa_pairs[:max_questions],
+                    variants=(variant,),
+                    persona=persona,
+                    mask_strategy=mask_strategy,
+                    remote=remote,
+                    on_status=_on_ndif_status if remote else None,
+                )
             )
-            results.extend(variant_results)
-            step += 1
 
         progress.progress(1.0, text="Extraction complete")
     except Exception as exc:

utils/chat.py

@@ -5,11 +5,7 @@ from typing import Literal
 
 import torch
 from nnterp import StandardizedTransformer
-from persona_data.prompts import (
-    _normalize_messages,
-    format_roleplay_prompt,
-    system_prompt_for_variant,
-)
+from persona_data.prompts import _normalize_messages, format_roleplay_prompt
 from persona_data.synth_persona import PersonaData
 
 logger = logging.getLogger(__name__)
@@ -42,7 +38,9 @@ def resolve_system_prompt(
     if mode == "custom":
         return format_roleplay_prompt(mode="conversational")
     if mode in ("templated", "biography"):
-        return system_prompt_for_variant(persona, mode, mode="conversational")
+        return format_roleplay_prompt(
+            getattr(persona, f"{mode}_view"), mode="conversational"
+        )
     raise ValueError(f"Unsupported system prompt mode: {mode}")
 
 

utils/helpers.py

@@ -5,6 +5,7 @@ from persona_data.synth_persona import PersonaData
 # Variant key -> human-readable label mapping
 VARIANT_LABELS = {
     "empty": "None",
+    "baseline": "Baseline",
     "templated": "Template",
     "biography": "Biography",
     "custom": "Custom",

uv.lock

@@ -213,15 +213,16 @@ wheels = [
 
 [[package]]
 name = "choreographer"
-version = "1.2.1"
+version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "logistro" },
+    { name = "platformdirs" },
     { name = "simplejson" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/74/47/64a035c6f764450ea9f902cbeba14c8c70316c2641125510066d8f912bfa/choreographer-1.2.1.tar.gz", hash = "sha256:022afd72b1e9b0bcb950420b134e70055a294c791b6f36cfb47d89745b701b5f", size = 43399, upload-time = "2025-11-09T23:04:44.749Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/17/69/3058cd4f16d6b75c80e8f95e5b713d930526353ce294df9a7887453ba215/choreographer-1.3.0.tar.gz", hash = "sha256:6c44a0e48e9b37977344d40bfa5a9ed88575fe4bc0fd836771bf702bc24d6884", size = 48291, upload-time = "2026-04-28T22:57:45.114Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b7/9f/d73dfb85d7a5b1a56a99adc50f2074029468168c970ff5daeade4ad819e4/choreographer-1.2.1-py3-none-any.whl", hash = "sha256:9af5385effa3c204dbc337abf7ac74fd8908ced326a15645dc31dde75718c77e", size = 49338, upload-time = "2025-11-09T23:04:43.154Z" },
+    { url = "https://files.pythonhosted.org/packages/ba/6c/ff8bf52315064dbeb55cb5067e191120a5b2e58bb648d0d34cf7969dc2c2/choreographer-1.3.0-py3-none-any.whl", hash = "sha256:cea4cb739e4f61625e4b53888a8d3fa1d3bf73948b56753e460ab44da7d8d44f", size = 52622, upload-time = "2026-04-28T22:57:44.015Z" },
 ]
 
 [[package]]
@@ -259,7 +260,7 @@ name = "cuda-bindings"
 version = "13.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "cuda-pathfinder" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/52/c8/b2589d68acf7e3d63e2be330b84bc25712e97ed799affbca7edd7eae25d6/cuda_bindings-13.2.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e865447abfb83d6a98ad5130ed3c70b1fc295ae3eeee39fd07b4ddb0671b6788", size = 5722404, upload-time = "2026-03-11T00:12:44.041Z" },
@@ -290,37 +291,37 @@ wheels = [
 
 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cublas", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 cufft = [
-    { name = "nvidia-cufft", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cufft", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 cufile = [
     { name = "nvidia-cufile", marker = "sys_platform == 'linux'" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 curand = [
-    { name = "nvidia-curand", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-curand", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver", marker = "sys_platform == 'linux'" },
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