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import html as html_lib
import pandas as pd
import re, json
from config import (
CUSTOM_CSS,
AG_GRID_HEAD,
DISPLAY_COLUMNS
)
def build_sortable_table(df: pd.DataFrame, table_id: str, title: str) -> str:
if df is None or df.empty:
return f"""
<div class="mb-4">
<h4 class="text-primary">{html_lib.escape(title)}</h4>
<p>No data</p>
</div>
"""
def format_display_df(df: pd.DataFrame) -> pd.DataFrame:
out = df.copy()
def truncate_cell(x):
if isinstance(x, (list, dict)):
x = json.dumps(x, default=str)
x = "" if x is None else str(x)
return html_lib.escape(x, quote=False)
if "evidence" in out.columns:
out["evidence"] = out["evidence"].apply(
lambda x: json.dumps(x, indent=2, default=str) if isinstance(x, (list, dict)) else x
).apply(truncate_cell)
if "a_to_b_mapping" in out.columns and not out["a_to_b_mapping"].astype(str).eq("").all():
out["a_to_b_mapping"] = out["a_to_b_mapping"].astype(str).apply(truncate_cell)
return out
def _cell_html(value, max_len: int = 120) -> str:
if value is None:
text = ""
elif isinstance(value, float) and pd.isna(value):
text = ""
elif isinstance(value, (list, dict)):
text = json.dumps(value, ensure_ascii=False, default=str)
else:
text = str(value)
escaped_full = html_lib.escape(text)
if len(text) <= max_len:
return escaped_full
escaped_short = html_lib.escape(text[:max_len] + "…")
return (
f'<span class="expandable-cell" '
f'data-full-value="{escaped_full}" '
f'title="Click to expand">'
f"{escaped_short}"
f"</span>"
)
def get_display_df(df: pd.DataFrame, feature_type: str) -> pd.DataFrame:
cols = DISPLAY_COLUMNS.get(feature_type, DISPLAY_COLUMNS["default"])
cols = [c for c in cols if c in df.columns]
return df[cols].copy()
def col_to_class(col: str) -> str:
col = str(col).strip().lower()
col = re.sub(r"[^a-z0-9]+", "-", col) # replace non-alphanumeric with -
return f"col-{col}"
def build_sortable_table(df: pd.DataFrame, table_id: str, title: str) -> str:
if df is None or df.empty:
return f"""
<div class="mb-4">
<h4 class="text-primary">{html_lib.escape(title)}</h4>
<p>No data</p>
</div>
"""
df = df.round(3)
headers = []
for idx, col in enumerate(df.columns):
col_class = col_to_class(col)
headers.append(
f'<th class="{col_class}" onclick="sortHtmlTable(\'{table_id}\', {idx})">'
f'{html_lib.escape(str(col))}<span class="sort-indicator"></span>'
f'</th>'
)
body_rows = []
for _, row in df.iterrows():
cells = []
for col in df.columns:
cells.append(f"<td>{_cell_html(row[col])}</td>")
body_rows.append("<tr>" + "".join(cells) + "</tr>")
return f"""
<div class="mb-4">
<h4 class="text-primary">{html_lib.escape(title)}</h4>
<div class="table-wrap">
<table id="{table_id}" class="sortable-table table table-striped table-bordered table-hover table-sm"
data-sort-col="" data-sort-dir="asc">
<thead>
<tr>{''.join(headers)}</tr>
</thead>
<tbody>
{''.join(body_rows)}
</tbody>
</table>
</div>
</div>
"""
def render_generated_tables(analysis_df: pd.DataFrame, valid_df: pd.DataFrame, invalid_df: pd.DataFrame) -> tuple[str, str, str]:
analysis_html = (
build_sortable_table(format_display_df(analysis_df), "property-relation-table", "Analyze property (all features)")
if analysis_df is not None and not analysis_df.empty
else "<p>No Analysis data</p>"
)
valid_html = (
build_sortable_table(format_display_df(valid_df), "generated-table", "Generated Data")
if valid_df is not None and not valid_df.empty
else "<p>No generated data</p>"
)
invalid_html = (
build_sortable_table(format_display_df(invalid_df), "invalid-table", "Invalid Data")
if invalid_df is not None and not invalid_df.empty
else "<p>No invalid rows</p>"
)
return analysis_html, valid_html, invalid_html, ""
def render_tables(dfs):
if not isinstance(dfs, dict) or not dfs:
return "<p>No grouped tables to display.</p>"
html_parts = []
for i, (feature_type, df) in enumerate(dfs.items(), start=1):
display_df = format_display_df(get_display_df(df, feature_type))
table_id = f"feature_table_{i}"
html_parts.append(build_sortable_table(display_df, table_id, str(feature_type)))
return "".join(html_parts) |