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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) |