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"""Logic for the **View Examples** tab – dropdown population + example renderer."""
from __future__ import annotations
from typing import Any, List, Tuple, Optional
import gradio as gr
import ast
from .state import app_state
from .utils import (
get_unique_values_for_dropdowns,
get_example_data,
format_examples_display,
search_clusters_by_text,
)
__all__: List[str] = [
"get_dropdown_choices",
"update_example_dropdowns",
"view_examples",
"get_filter_options",
"update_filter_dropdowns",
]
# ---------------------------------------------------------------------------
# Dropdown helpers
# ---------------------------------------------------------------------------
def get_dropdown_choices(selected_models: Optional[List[str]] = None) -> Tuple[List[str], List[str], List[str]]:
if app_state["clustered_df"] is None:
return [], [], []
choices = get_unique_values_for_dropdowns(app_state["clustered_df"])
prompts = ["All Prompts"] + choices["prompts"]
# If a sidebar selection is provided, filter models to that subset (ignoring the pseudo 'all')
if selected_models:
subset = [m for m in choices["models"] if m in [sm for sm in selected_models if sm != "all"]]
models = ["All Models"] + (subset if subset else choices["models"]) # fallback to all available if subset empty
else:
models = ["All Models"] + choices["models"]
properties = ["All Clusters"] + choices["properties"]
return prompts, models, properties
def update_example_dropdowns(selected_models: Optional[List[str]] = None) -> Tuple[Any, Any, Any]:
prompts, models, properties = get_dropdown_choices(selected_models)
# If exactly one concrete model selected in sidebar, preselect it; else default to All Models
preselect_model = "All Models"
if selected_models:
concrete = [m for m in selected_models if m != "all"]
if len(concrete) == 1 and concrete[0] in models:
preselect_model = concrete[0]
return (
gr.update(choices=prompts, value="All Prompts" if prompts else None),
gr.update(choices=models, value=(preselect_model if models else None)),
gr.update(choices=properties, value="All Clusters" if properties else None),
)
# ---------------------------------------------------------------------------
# Example viewer
# ---------------------------------------------------------------------------
def view_examples(
selected_prompt: str,
selected_model: str,
selected_property: str,
max_examples: int = 5,
use_accordion: bool = True,
pretty_print_dicts: bool = True,
search_term: str = "",
show_unexpected_behavior: bool = False,
selected_models_sidebar: Optional[List[str]] = None,
selected_tags_sidebar: Optional[List[str]] = None,
) -> str:
if app_state["clustered_df"] is None:
return (
"<p style='color: #e74c3c; padding: 20px;'>❌ Please load data first "
"using the 'Load Data' tab</p>"
)
# Apply search filter first if search term is provided
df = app_state["clustered_df"]
# Apply sidebar-selected model filter if provided (ignoring pseudo 'all') before dropdown filters
if selected_models_sidebar:
concrete = [m for m in selected_models_sidebar if m != "all"]
if concrete:
df = df[df["model"].isin(concrete)]
if df.empty:
return "<p style='color: #e74c3c; padding: 20px;'>❌ No examples for the selected model subset.</p>"
if search_term and isinstance(search_term, str) and search_term.strip():
df = search_clusters_by_text(df, search_term.strip(), 'all')
if df.empty:
return f"<p style='color: #e74c3c; padding: 20px;'>❌ No clusters found matching '{search_term}'</p>"
# Optional tags filter (sidebar): include rows whose first meta value is in selected tags
if selected_tags_sidebar and len(selected_tags_sidebar) > 0 and 'meta' in df.columns:
def _parse_meta(obj: Any) -> Any:
if isinstance(obj, str):
try:
return ast.literal_eval(obj)
except Exception:
return obj
return obj
def _first_val(obj: Any) -> Any:
if obj is None:
return None
obj = _parse_meta(obj)
if isinstance(obj, dict):
for _, v in obj.items():
return v
return None
if isinstance(obj, (list, tuple)):
return obj[0] if len(obj) > 0 else None
return obj
parsed_meta = df['meta'].apply(_parse_meta)
non_null_parsed = [m for m in parsed_meta.tolist() if m is not None]
all_empty_dicts = (
len(non_null_parsed) > 0 and all(isinstance(m, dict) and len(m) == 0 for m in non_null_parsed)
)
if not all_empty_dicts:
allowed = set(map(str, selected_tags_sidebar))
df = df[df['meta'].apply(_first_val).astype(str).isin(allowed)]
if df.empty:
return "<p style='color: #e74c3c; padding: 20px;'>❌ No examples found for selected tags</p>"
examples = get_example_data(
df,
selected_prompt if selected_prompt != "All Prompts" else None,
selected_model if selected_model != "All Models" else None,
selected_property if selected_property != "All Clusters" else None,
max_examples,
show_unexpected_behavior=show_unexpected_behavior,
randomize=(
(selected_prompt == "All Prompts") and
(selected_model == "All Models") and
(selected_property == "All Clusters") and
(not search_term or not str(search_term).strip())
),
)
return format_examples_display(
examples,
selected_prompt,
selected_model,
selected_property,
use_accordion=use_accordion,
pretty_print_dicts=pretty_print_dicts,
)
# ---------------------------------------------------------------------------
# Filter dropdown helpers for frequency comparison
# ---------------------------------------------------------------------------
def get_filter_options() -> Tuple[List[str], List[str]]:
if not app_state["model_stats"]:
return ["All Models"], ["All Metrics"]
available_models = ["All Models"] + list(app_state["model_stats"].keys())
quality_metrics = set()
for model_data in app_state["model_stats"].values():
clusters = model_data.get("fine", []) + model_data.get("coarse", [])
for cluster in clusters:
quality_score = cluster.get("quality_score", {})
if isinstance(quality_score, dict):
quality_metrics.update(quality_score.keys())
available_metrics = ["All Metrics"] + sorted(list(quality_metrics))
return available_models, available_metrics
def update_filter_dropdowns() -> Tuple[Any, Any]:
models, metrics = get_filter_options()
return (
gr.update(choices=models, value="All Models" if models else None),
gr.update(choices=metrics, value="All Metrics" if metrics else None),
) |