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c607869 d39b2dd c607869 f4259c0 d39b2dd db3d901 c607869 f4259c0 c607869 a89a7f1 db3d901 a89a7f1 73d72c1 a89a7f1 77c2d62 c30bbc5 db3d901 e75684b a89a7f1 330d092 0ba2e45 db3d901 ecd19ae a89a7f1 f4259c0 a89a7f1 b279884 f4259c0 a89a7f1 db3d901 c607869 d39b2dd a89a7f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | from __future__ import annotations
import hashlib
import logging
import os
import re
from collections.abc import Iterable
from enum import Enum
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from persona_data.synth_persona import PersonaData
logger = logging.getLogger(__name__)
class DatasetSource(str, Enum):
SYNTH_PERSONA = "HuggingFace: synth-persona"
NEMOTRON_FRANCE = "HuggingFace: nemotron-france"
NEMOTRON_USA = "HuggingFace: nemotron-usa"
LOCAL_UPLOAD = "Local JSONL upload"
DATASET_SOURCES = [s.value for s in DatasetSource]
# Variant key -> human-readable label mapping
VARIANT_LABELS = {
"empty": "None",
"baseline": "Baseline",
"templated": "Template",
"biography": "Biography",
"custom": "Custom",
}
CHAT_PROMPT_MODES = ("empty", "templated", "biography", "custom")
CHAT_PROMPT_MODE_LABELS = [VARIANT_LABELS[key] for key in CHAT_PROMPT_MODES]
CHAT_PROMPT_MODE_LABEL_TO_KEY = {VARIANT_LABELS[key]: key for key in CHAT_PROMPT_MODES}
ANALYSIS_MODES = [
"Cosine similarity",
"Similarity matrix",
"PCA",
"UMAP",
"Isomap",
"Dendrogram",
]
ANALYSIS_HELP_TEXT = {
"Cosine similarity": "Compare layer-wise alignment between variants.",
"Similarity matrix": "Compare centered pairwise similarity between persona vectors by layer, with pair trajectories across layers.",
"PCA": "Project per-persona vectors into a 2D or 3D global view.",
"UMAP": "Project per-persona vectors into a 2D or 3D local-neighborhood view.",
"Isomap": "Project per-persona vectors with graph-geodesic distances to probe manifold-like geometry.",
"Dendrogram": "Hierarchical clustering of persona vectors — shows biography and templated side by side for direct comparison.",
}
NDIF_STATUS_ICONS = {
"RECEIVED": "◉",
"QUEUED": "◎",
"DISPATCHED": "◈",
"RUNNING": "●",
"COMPLETED": "✓",
"ERROR": "✗",
}
def format_ndif_status(
job_id: str,
status_name: str,
description: str,
*,
prefix: str | None = None,
completed_detail: str | None = None,
) -> str:
"""Build the shared one-line NDIF status label used across the UI."""
icon = NDIF_STATUS_ICONS.get(status_name, "•")
detail = (
completed_detail
if completed_detail is not None and status_name == "COMPLETED"
else description
)
label = f"{icon} `{job_id}` **{status_name}** — {detail}"
return f"{prefix}: {label}" if prefix else label
def slugify(value: str) -> str:
"""Convert a string to a filesystem-safe slug."""
return re.sub(r"[^a-z0-9]+", "_", value.lower()).strip("_") or "unknown"
def widget_key(*parts: str) -> str:
"""Generate a namespaced Streamlit widget key from parts."""
return "::".join(parts)
def session_key(*parts: str) -> str:
"""Generate a colon-separated Streamlit session-state key from parts."""
return ":".join(parts)
def env_int(name: str, default: int, *, minimum: int = 1) -> int:
"""Read a bounded integer from the environment."""
try:
return max(minimum, int(os.environ.get(name, str(default))))
except ValueError:
logger.warning("Ignoring invalid integer for %s", name)
return default
def personas_fingerprint(persona_ids: Iterable[str]) -> str:
"""Stable short fingerprint for a set of persona ids.
Used as a discriminator in widget keys and session-state keys. At ~1k
personas, joining ids would produce ~20 KB strings; the sha1 prefix is
fixed-length and keeps tracebacks readable.
"""
joined = "|".join(sorted(persona_ids))
return hashlib.sha1(joined.encode()).hexdigest()[:16]
def prompt_variant_label(variant: str) -> str:
"""Return a human-friendly prompt-variant label."""
return VARIANT_LABELS.get(variant, variant.title())
def persona_label(persona: PersonaData) -> str:
"""Format a persona for selection widgets."""
return f"{persona.name} ({persona.id})"
|