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"""Logic for the **View Examples** tab – dropdown population + example renderer."""
from __future__ import annotations
from typing import Any, List, Tuple
import gradio as gr
from .state import app_state
from .utils import (
get_unique_values_for_dropdowns,
get_example_data,
format_examples_display,
search_clusters_only,
)
__all__: List[str] = [
"get_dropdown_choices",
"update_example_dropdowns",
"view_examples",
"get_filter_options",
"update_filter_dropdowns",
]
# ---------------------------------------------------------------------------
# Dropdown helpers
# ---------------------------------------------------------------------------
def get_dropdown_choices() -> 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"]
models = ["All Models"] + choices["models"]
properties = ["All Clusters"] + choices["properties"]
return prompts, models, properties
def update_example_dropdowns() -> Tuple[Any, Any, Any]:
prompts, models, properties = get_dropdown_choices()
return (
gr.update(choices=prompts, value="All Prompts" if prompts else None),
gr.update(choices=models, value="All Models" 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,
) -> 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"]
if search_term and isinstance(search_term, str) and search_term.strip():
df = search_clusters_only(df, search_term.strip(), 'fine') # Default to fine clusters
if df.empty:
return f"<p style='color: #e74c3c; padding: 20px;'>❌ No clusters found matching '{search_term}'</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),
) |