""" Time Series Visualizer + AI Chart Interpreter ============================================= Main Gradio application. Run with: python app.py """ from __future__ import annotations import hashlib import io from pathlib import Path from dotenv import load_dotenv load_dotenv() import matplotlib matplotlib.use("Agg") import numpy as np import pandas as pd import gradio as gr from src.ui_theme import ( MiamiTheme, get_miami_css, get_miami_mpl_style, get_palette_colors, render_palette_preview, ) from src.cleaning import ( detect_delimiter, suggest_date_columns, suggest_numeric_columns, clean_dataframe, detect_frequency, add_time_features, detect_long_format, pivot_long_to_wide, CleaningReport, FrequencyInfo, ) from src.diagnostics import ( compute_summary_stats, compute_acf_pacf, compute_decomposition, compute_yoy_change, compute_multi_series_summary, ) from src.plotting import ( fig_to_png_bytes, plot_line_with_markers, plot_line_colored_markers, plot_seasonal, plot_seasonal_subseries, plot_acf_pacf, plot_decomposition, plot_rolling_overlay, plot_yoy_change, plot_lag, plot_panel, plot_spaghetti, ) from src.ai_interpretation import ( check_api_key_available, interpret_chart, render_interpretation_markdown, ) from src.querychat_helpers import ( check_querychat_available, create_querychat, get_filtered_pandas_df, ) # --------------------------------------------------------------------------- # Constants # --------------------------------------------------------------------------- _DATA_DIR = Path(__file__).parent / "data" _DEMO_FILES = { "Ohio Unemployment Rate (single, monthly)": _DATA_DIR / "demo_ohio_unemployment.csv", "Manufacturing Employment by State (wide, monthly)": _DATA_DIR / "demo_manufacturing_wide.csv", "Manufacturing Employment by State (long, monthly)": _DATA_DIR / "demo_manufacturing_long.csv", } _DEMO_CHOICES = ["(none)"] + list(_DEMO_FILES.keys()) _CHART_TYPES = [ "Line with Markers", "Line \u2013 Colored Markers", "Seasonal Plot", "Seasonal Sub-series", "ACF / PACF", "Decomposition", "Rolling Mean Overlay", "Year-over-Year Change", "Lag Plot", ] _PALETTE_NAMES = ["Set2", "Dark2", "Set1", "Paired", "Pastel1", "Pastel2", "Accent"] _STYLE_DICT = get_miami_mpl_style() _MODE_SINGLE = "Single Series" _MODE_PANEL = "Compare Few (Panel)" _MODE_SPAG = "Compare Many (Spaghetti)" _DATE_HINT_TOKENS = ("date", "time", "year", "month", "day", "period") # --------------------------------------------------------------------------- # State helpers # --------------------------------------------------------------------------- def _make_empty_state() -> dict: return { "raw_df_original": None, "cleaned_df": None, "cleaning_report": None, "freq_info": None, "date_col": None, "y_cols": None, "setup_applied": False, "single_png": None, "panel_png": None, "spag_png": None, "qc": None, "mode_choices": [_MODE_SINGLE], "recommended_mode": _MODE_SINGLE, } # --------------------------------------------------------------------------- # Formatting helpers # --------------------------------------------------------------------------- def _format_cleaning_report_md(report: CleaningReport) -> str: lines = [ "| Metric | Value |", "|:--|:--|", f"| **Rows before** | {report.rows_before:,} |", f"| **Rows after** | {report.rows_after:,} |", f"| **Duplicates found** | {report.duplicates_found:,} |", ] if report.missing_before: lines += ["", "**Missing values:**", "| Column | Before | After |", "|:--|:--|:--|"] for col in report.missing_before: lines.append(f"| {col} | {report.missing_before[col]} | {report.missing_after.get(col, 0)} |") if report.parsing_warnings: lines += ["", "**Warnings:**"] for w in report.parsing_warnings: lines.append(f"- {w}") return "\n".join(lines) def _fmt(val, fmt_str): return fmt_str.format(val) if pd.notna(val) else "N/A" def _format_summary_stats_md(stats) -> str: lines = [ "| Statistic | Value |", "|:--|:--|", f"| **Count** | {stats.count:,} |", f"| **Missing** | {stats.missing_count} ({stats.missing_pct:.1f}%) |", f"| **Mean** | {stats.mean_val:,.2f} |", f"| **Std Dev** | {stats.std_val:,.2f} |", f"| **Min** | {stats.min_val:,.2f} |", f"| **25th %ile** | {stats.p25:,.2f} |", f"| **Median** | {stats.median_val:,.2f} |", f"| **75th %ile** | {stats.p75:,.2f} |", f"| **Max** | {stats.max_val:,.2f} |", f"| **Trend slope** | {_fmt(stats.trend_slope, '{:,.4f}')} |", f"| **Trend p-value** | {_fmt(stats.trend_pvalue, '{:.4f}')} |", f"| **ADF statistic** | {_fmt(stats.adf_statistic, '{:.4f}')} |", f"| **ADF p-value** | {_fmt(stats.adf_pvalue, '{:.4f}')} |", "", f"*Date range: {stats.date_start.date()} to {stats.date_end.date()} ({stats.date_span_days:,} days)*", ] return "\n".join(lines) def _format_multi_summary_md(summary_df: pd.DataFrame) -> str: lines = [ "| Variable | Count | Mean | Std | Min | Max | Trend Slope | ADF p |", "|:--|--:|--:|--:|--:|--:|--:|--:|", ] for _, row in summary_df.iterrows(): adf = f"{row['adf_pvalue']:.4f}" if pd.notna(row['adf_pvalue']) else "N/A" slope = f"{row['trend_slope']:,.4f}" if pd.notna(row['trend_slope']) else "N/A" lines.append( f"| {row['variable']} | {row['count']:,} | {row['mean']:,.2f} | " f"{row['std']:,.2f} | {row['min']:,.2f} | {row['max']:,.2f} | " f"{slope} | {adf} |" ) return "\n".join(lines) # --------------------------------------------------------------------------- # UX helpers # --------------------------------------------------------------------------- def _preview_df(df: pd.DataFrame, n: int = 10) -> pd.DataFrame: return df.head(n).copy() def _format_sidebar_status_md(df: pd.DataFrame | None, date_col: str | None = None, data_format: str | None = None, y_count: int | None = None, freq_label: str | None = None, cleaned_rows: int | None = None) -> str: if df is None: return "*No data loaded yet.*" row_count = cleaned_rows if cleaned_rows is not None else len(df) col_count = len(df.columns) parts = [ "### Dataset Status", f"- Rows: **{row_count:,}**", f"- Columns: **{col_count}**", ] if date_col: parts.append(f"- Date column: **{date_col}**") if data_format: parts.append(f"- Structure: **{data_format}**") if y_count is not None: parts.append(f"- Value series selected: **{y_count}**") if freq_label: parts.append(f"- Frequency: **{freq_label}**") return "\n".join(parts) def _format_raw_profile_md(df: pd.DataFrame, date_col: str, data_format: str, y_cols: list[str]) -> str: numeric_cols = int(df.select_dtypes(include=[np.number]).shape[1]) object_cols = int(df.select_dtypes(include=["object"]).shape[1]) return "\n".join([ "### Dataset Profile", "| Metric | Value |", "|:--|:--|", f"| Rows | {len(df):,} |", f"| Columns | {len(df.columns)} |", f"| Suggested date column | {date_col} |", f"| Detected structure | {data_format} |", f"| Numeric columns | {numeric_cols} |", f"| Text columns | {object_cols} |", f"| Value series selected | {len(y_cols)} |", ]) def _get_mode_config(y_count: int) -> tuple[list[str], str, str]: if y_count <= 1: return ( [_MODE_SINGLE], _MODE_SINGLE, "Single series detected. Multi-series comparison modes are hidden.", ) if y_count <= 8: return ( [_MODE_SINGLE, _MODE_PANEL], _MODE_PANEL, "Best fit: compare a few series in panel view. Spaghetti is hidden to reduce clutter.", ) return ( [_MODE_SINGLE, _MODE_PANEL, _MODE_SPAG], _MODE_SPAG, "Many series detected. Spaghetti is the recommended default.", ) def _chart_availability(df_plot: pd.DataFrame, date_col: str, y_col: str, freq_info: FrequencyInfo | None) -> dict[str, str]: blocked: dict[str, str] = {} if y_col not in df_plot.columns: return {name: "Value column not found in active data." for name in _CHART_TYPES} n_obs = int(df_plot[y_col].dropna().shape[0]) if n_obs <= 1: return {name: "Need at least 2 non-missing observations." for name in _CHART_TYPES} date_series = pd.to_datetime(df_plot[date_col], errors="coerce").dropna() span_days = int((date_series.max() - date_series.min()).days) if len(date_series) >= 2 else 0 has_time_features = "month" in df_plot.columns freq_label = freq_info.label if freq_info else "Unknown" period_map = {"Monthly": 12, "Quarterly": 4, "Weekly": 52, "Daily": 365} period = period_map.get(freq_label) if not has_time_features: blocked["Line – Colored Markers"] = "Calendar features unavailable." blocked["Seasonal Plot"] = "Calendar features unavailable." blocked["Seasonal Sub-series"] = "Calendar features unavailable." if has_time_features and n_obs < 12: blocked["Seasonal Plot"] = "Need at least 12 observations." blocked["Seasonal Sub-series"] = "Need at least 12 observations." if n_obs < 8: blocked["ACF / PACF"] = "Need at least 8 observations." if period is None: blocked["Decomposition"] = "Requires Daily/Weekly/Monthly/Quarterly frequency." elif n_obs < max(8, period * 2): blocked["Decomposition"] = f"Need at least {max(8, period * 2)} observations." if span_days < 365: blocked["Year-over-Year Change"] = "Need at least one year of coverage." if n_obs < 3: blocked["Lag Plot"] = "Need at least 3 observations." return blocked def _available_chart_choices(df_plot: pd.DataFrame, date_col: str, y_col: str, freq_info: FrequencyInfo | None) -> tuple[list[str], str]: blocked = _chart_availability(df_plot, date_col, y_col, freq_info) available = [name for name in _CHART_TYPES if name not in blocked] if not available: available = ["Line with Markers"] notes = ["**Chart availability (auto-gated):**"] if blocked: for chart_name, reason in blocked.items(): notes.append(f"- {chart_name}: {reason}") else: notes.append("- All chart types are available.") return available, "\n".join(notes) def _mode_visibility(mode: str) -> tuple[bool, bool, bool]: return ( mode == _MODE_SINGLE, mode == _MODE_PANEL, mode == _MODE_SPAG, ) def _choose_default_date_col(df: pd.DataFrame) -> str | None: cols = list(df.columns) if not cols: return None for col in cols: if pd.api.types.is_datetime64_any_dtype(df[col]): return col for col in cols: name = str(col).lower() if any(tok in name for tok in _DATE_HINT_TOKENS): return col suggestions = suggest_date_columns(df) if suggestions: return suggestions[0] return cols[0] def _derive_setup_options(df: pd.DataFrame, date_col: str | None, data_format: str, group_col: str | None = None, value_col: str | None = None, current_y: list[str] | None = None) -> dict: all_cols = list(df.columns) resolved_date = date_col if date_col in all_cols else (all_cols[0] if all_cols else None) if not resolved_date: return { "resolved_date": None, "string_cols": [], "value_options": [], "group_default": None, "value_default": None, "available_y": [], "default_y": [], } other_cols = [c for c in all_cols if c != resolved_date] string_cols = [ c for c in other_cols if df[c].dtype == object or pd.api.types.is_string_dtype(df[c]) ] numeric_suggest = suggest_numeric_columns(df) group_default = ( group_col if group_col and group_col in string_cols else (string_cols[0] if string_cols else None) ) value_options = [c for c in numeric_suggest if c != resolved_date and c != group_default] value_default = ( value_col if value_col and value_col in value_options else (value_options[0] if value_options else None) ) if data_format == "Long" and group_default and value_default: try: effective = pivot_long_to_wide(df, resolved_date, group_default, value_default) available_y = [c for c in effective.columns if c != resolved_date] except Exception: available_y = value_options.copy() else: available_y = value_options.copy() kept = [c for c in (current_y or []) if c in available_y] default_y = kept if kept else available_y[:4] return { "resolved_date": resolved_date, "string_cols": string_cols, "value_options": value_options, "group_default": group_default, "value_default": value_default, "available_y": available_y, "default_y": default_y, } # --------------------------------------------------------------------------- # Data helpers # --------------------------------------------------------------------------- def _read_file_to_df(file_path: str) -> tuple[pd.DataFrame, str]: with open(file_path, "rb") as f: raw = f.read() delim = detect_delimiter(raw) text = raw.decode("utf-8", errors="replace") df = pd.read_csv(io.StringIO(text), sep=delim) return df, delim def _apply_date_filter(df, date_col, mode, n_years, custom_start, custom_end): if mode == "Last N years" and n_years: cutoff = df[date_col].max() - pd.DateOffset(years=int(n_years)) df = df[df[date_col] >= cutoff] elif mode == "Custom": try: if custom_start and str(custom_start).strip(): df = df[df[date_col] >= pd.to_datetime(custom_start)] except (ValueError, TypeError): pass try: if custom_end and str(custom_end).strip(): df = df[df[date_col] <= pd.to_datetime(custom_end)] except (ValueError, TypeError): pass return df def _generate_single_chart(df_plot, date_col, active_y, chart_type, palette_colors, color_by, period_label, window_size, lag_val, decomp_model, freq_info): """Generate a single chart figure. Returns ``(fig, error_msg)``.""" try: if chart_type == "Line with Markers": return plot_line_with_markers( df_plot, date_col, active_y, title=f"{active_y} over Time", style_dict=_STYLE_DICT, palette_colors=palette_colors, ), None elif "Colored Markers" in chart_type and color_by: return plot_line_colored_markers( df_plot, date_col, active_y, color_by=color_by, palette_colors=palette_colors, title=f"{active_y} colored by {color_by}", style_dict=_STYLE_DICT, ), None elif chart_type == "Seasonal Plot": return plot_seasonal( df_plot, date_col, active_y, period=period_label or "month", palette_name_colors=palette_colors, title=f"Seasonal Plot - {active_y}", style_dict=_STYLE_DICT, ), None elif chart_type == "Seasonal Sub-series": return plot_seasonal_subseries( df_plot, date_col, active_y, period=period_label or "month", title=f"Seasonal Sub-series - {active_y}", style_dict=_STYLE_DICT, palette_colors=palette_colors, ), None elif chart_type == "ACF / PACF": series = df_plot[active_y].dropna() acf_vals, acf_ci, pacf_vals, pacf_ci = compute_acf_pacf(series) return plot_acf_pacf( acf_vals, acf_ci, pacf_vals, pacf_ci, title=f"ACF / PACF - {active_y}", style_dict=_STYLE_DICT, ), None elif chart_type == "Decomposition": period_int = None if freq_info: period_int = {"Monthly": 12, "Quarterly": 4, "Weekly": 52, "Daily": 365}.get(freq_info.label) result = compute_decomposition( df_plot, date_col, active_y, model=decomp_model or "additive", period=period_int, ) return plot_decomposition( result, title=f"Decomposition - {active_y} ({decomp_model})", style_dict=_STYLE_DICT, ), None elif chart_type == "Rolling Mean Overlay": w = int(window_size) if window_size else 12 return plot_rolling_overlay( df_plot, date_col, active_y, window=w, title=f"Rolling {w}-pt Mean - {active_y}", style_dict=_STYLE_DICT, palette_colors=palette_colors, ), None elif chart_type == "Year-over-Year Change": yoy_result = compute_yoy_change(df_plot, date_col, active_y) yoy_df = pd.DataFrame({ "date": yoy_result[date_col], "abs_change": yoy_result["yoy_abs_change"], "pct_change": yoy_result["yoy_pct_change"], }).dropna() return plot_yoy_change( df_plot, date_col, active_y, yoy_df, title=f"Year-over-Year Change - {active_y}", style_dict=_STYLE_DICT, ), None elif chart_type == "Lag Plot": lag = int(lag_val) if lag_val else 1 return plot_lag( df_plot[active_y], lag=lag, title=f"Lag-{lag} Plot - {active_y}", style_dict=_STYLE_DICT, ), None except Exception as exc: return None, str(exc) return None, "Unknown chart type" # --------------------------------------------------------------------------- # HTML fragments # --------------------------------------------------------------------------- _DEVELOPER_CARD = """
Fadel M. Megahed
Raymond E. Glos Professor, Farmer School of Business
Miami University
Email LinkedIn Website GitHub
""" _WELCOME_MD = """ # Time Series Visualizer *ISA 444 \u00b7 Miami University \u00b7 Farmer School of Business* --- ### Guided Workflow
1
Load Data
Upload your CSV or load a demo dataset from the sidebar.
2
Understand
Review auto-detected structure and inspect a raw-data preview.
3
Prepare
Set date/value columns and apply cleaning options in the main canvas.
4
Visualize
Only relevant visualization modes are shown based on series count.
5
Interpret
Generate AI interpretation when your chart is ready.
--- ### Features | | | |:--|:--| | **Smart Import** | Auto-detect delimiters, dates, and numeric formats | | **9+ Chart Types** | Line, seasonal, ACF/PACF, decomposition, lag, and more | | **Multi-Series** | Panel (small multiples) and spaghetti plots | | **AI Insights** | GPT vision analyzes your charts and returns structured interpretation | | **QueryChat** | Natural-language data filtering powered by DuckDB | ### Good to Know **Privacy** \u2014 All data processing happens in-memory. No data is stored on disk. Only chart images (never raw data) are sent to OpenAI when you click *Interpret Chart with AI*. **Demo Datasets** \u2014 Three built-in FRED datasets are available in the sidebar: Ohio Unemployment Rate (single series), Manufacturing Employment for five states in wide format, and the same data in long/stacked format. """ # --------------------------------------------------------------------------- # Event handlers # --------------------------------------------------------------------------- def _process_new_data(df: pd.DataFrame, delim: str | None = None): """Shared logic for file upload and demo select. Returns a tuple of values matching ``_DATA_LOAD_OUTPUTS``. """ state = _make_empty_state() state["raw_df_original"] = df all_cols = list(df.columns) default_date = _choose_default_date_col(df) infer_date = default_date if default_date in all_cols else (all_cols[0] if all_cols else None) if infer_date is None: return ( state, gr.Column(visible=False), gr.Dropdown(), gr.Radio(), gr.Column(visible=False), gr.Dropdown(), gr.Dropdown(), gr.CheckboxGroup(choices=[], value=[]), "", _format_sidebar_status_md(None), "", pd.DataFrame(), gr.Dropdown(), "", gr.Column(visible=True), gr.Column(visible=False), gr.Radio(choices=[_MODE_SINGLE], value=_MODE_SINGLE), "", gr.Dropdown(choices=_CHART_TYPES, value=_CHART_TYPES[0]), "", gr.Column(visible=False), gr.Column(visible=False), gr.Column(visible=False), pd.DataFrame(), ) is_long, _, _ = detect_long_format(df, infer_date) fmt = "Long" if is_long else "Wide" setup_opts = _derive_setup_options( df, date_col=infer_date, data_format=fmt, group_col=None, value_col=None, current_y=None, ) resolved_date = setup_opts["resolved_date"] group_default = setup_opts["group_default"] value_default = setup_opts["value_default"] available_y = setup_opts["available_y"] default_y = setup_opts["default_y"] delim_text = f"Detected delimiter: `{repr(delim)}`" if delim else "" profile_md = _format_raw_profile_md(df, resolved_date, fmt, default_y) status_md = _format_sidebar_status_md( df=df, date_col=resolved_date, data_format=fmt, y_count=len(default_y), ) return ( state, # app_state gr.Column(visible=True), # setup_col gr.Dropdown(choices=all_cols, value=resolved_date), # date_col_dd gr.Radio(value=fmt), # format_radio gr.Column(visible=is_long), # long_col gr.Dropdown(choices=setup_opts["string_cols"], value=group_default), # group_col_dd gr.Dropdown(choices=setup_opts["value_options"], value=value_default), # value_col_dd gr.CheckboxGroup(choices=available_y, value=default_y), # y_cols_cbg delim_text, # delim_md status_md, # status_md profile_md, # raw_profile_md _preview_df(df), # raw_preview_df gr.Dropdown(choices=all_cols, value=resolved_date), # cast_col_dd "", # cast_status_md gr.Column(visible=False), # welcome_col gr.Column(visible=False), # analysis_col gr.Radio(choices=[_MODE_SINGLE], value=_MODE_SINGLE), # viz_mode_radio "Apply setup to unlock visualization modes.", # mode_hint_md gr.Dropdown(choices=_CHART_TYPES, value=_CHART_TYPES[0]), # single_chart_dd "*Apply setup to tailor chart options to your data.*", # single_gate_md gr.Column(visible=False), # single_mode_col gr.Column(visible=False), # panel_mode_col gr.Column(visible=False), # spag_mode_col pd.DataFrame(), # cleaned_preview_df ) def on_file_upload(file_obj, state): if file_obj is None: empty = _make_empty_state() return ( empty, gr.Column(visible=False), gr.Dropdown(), gr.Radio(), gr.Column(visible=False), gr.Dropdown(), gr.Dropdown(), gr.CheckboxGroup(choices=[], value=[]), "", "*No data loaded yet.*", "", pd.DataFrame(), gr.Dropdown(), "", gr.Column(visible=True), gr.Column(visible=False), gr.Radio(choices=[_MODE_SINGLE], value=_MODE_SINGLE), "", gr.Dropdown(choices=_CHART_TYPES, value=_CHART_TYPES[0]), "", gr.Column(visible=False), gr.Column(visible=False), gr.Column(visible=False), pd.DataFrame(), ) path = file_obj if isinstance(file_obj, str) else str(file_obj) df, delim = _read_file_to_df(path) return _process_new_data(df, delim) def on_demo_select(choice, state): if choice == "(none)" or choice is None: return ( state, gr.Column(), gr.Dropdown(), gr.Radio(), gr.Column(), gr.Dropdown(), gr.Dropdown(), gr.CheckboxGroup(), "", gr.Markdown(), gr.Markdown(), gr.Dataframe(), gr.Dropdown(), gr.Markdown(), gr.Column(), gr.Column(), gr.Radio(), gr.Markdown(), gr.Dropdown(), gr.Markdown(), gr.Column(), gr.Column(), gr.Column(), gr.Dataframe(), ) demo_path = _DEMO_FILES[choice] df = pd.read_csv(demo_path) return _process_new_data(df, None) def on_format_change(fmt): return gr.Column(visible=(fmt == "Long")) def on_setup_inputs_change(date_col, data_format, group_col, value_col, current_y, state): raw_df = state.get("raw_df_original") if raw_df is None: return ( gr.Column(visible=(data_format == "Long")), gr.Dropdown(), gr.Dropdown(), gr.CheckboxGroup(), "", _format_sidebar_status_md(None), ) opts = _derive_setup_options( raw_df, date_col=date_col, data_format=data_format, group_col=group_col, value_col=value_col, current_y=list(current_y) if current_y else [], ) resolved_date = opts["resolved_date"] profile_md = _format_raw_profile_md(raw_df, resolved_date, data_format, opts["default_y"]) status_md = _format_sidebar_status_md( raw_df, date_col=resolved_date, data_format=data_format, y_count=len(opts["default_y"]), ) return ( gr.Column(visible=(data_format == "Long")), gr.Dropdown(choices=opts["string_cols"], value=opts["group_default"]), gr.Dropdown(choices=opts["value_options"], value=opts["value_default"]), gr.CheckboxGroup(choices=opts["available_y"], value=opts["default_y"]), profile_md, status_md, ) def on_long_cols_change(date_col, group_col, value_col, state): raw_df = state.get("raw_df_original") if raw_df is None or not group_col or not value_col: return gr.CheckboxGroup() try: effective = pivot_long_to_wide(raw_df, date_col, group_col, value_col) available = [c for c in effective.columns if c != date_col] return gr.CheckboxGroup(choices=available, value=available[:4]) except Exception: return gr.CheckboxGroup(choices=[], value=[]) def on_y_selection_change(date_col, data_format, y_cols, state): raw_df = state.get("raw_df_original") if raw_df is None: return "", _format_sidebar_status_md(None) all_cols = list(raw_df.columns) resolved_date = date_col if date_col in all_cols else (all_cols[0] if all_cols else "") y_list = list(y_cols) if y_cols else [] profile_md = _format_raw_profile_md(raw_df, resolved_date, data_format, y_list) status_md = _format_sidebar_status_md( raw_df, date_col=resolved_date, data_format=data_format, y_count=len(y_list), ) return profile_md, status_md def on_cast_apply(state, cast_col, cast_type, date_col, data_format, group_col, value_col, y_cols): raw_df = state.get("raw_df_original") if raw_df is None or not cast_col or cast_col not in raw_df.columns: return ( state, gr.Dataframe(), "", _format_sidebar_status_md(None), gr.Dropdown(), gr.Column(visible=(data_format == "Long")), gr.Dropdown(), gr.Dropdown(), gr.CheckboxGroup(), gr.Dropdown(), "*Select a valid column to cast.*", ) updated = raw_df.copy() try: if cast_type == "Numeric (coerce)": updated[cast_col] = pd.to_numeric(updated[cast_col], errors="coerce") elif cast_type == "Datetime (coerce)": updated[cast_col] = pd.to_datetime(updated[cast_col], errors="coerce") else: updated[cast_col] = updated[cast_col].astype(str) except Exception as exc: return ( state, gr.Dataframe(value=_preview_df(raw_df)), "", _format_sidebar_status_md(raw_df, date_col=date_col, data_format=data_format), gr.Dropdown(choices=list(raw_df.columns), value=date_col), gr.Column(visible=(data_format == "Long")), gr.Dropdown(), gr.Dropdown(), gr.CheckboxGroup(), gr.Dropdown(choices=list(raw_df.columns), value=cast_col), f"*Type cast failed: {exc}*", ) state["raw_df_original"] = updated all_cols = list(updated.columns) next_date = date_col if date_col in all_cols else _choose_default_date_col(updated) opts = _derive_setup_options( updated, date_col=next_date, data_format=data_format, group_col=group_col, value_col=value_col, current_y=list(y_cols) if y_cols else [], ) profile_md = _format_raw_profile_md(updated, opts["resolved_date"], data_format, opts["default_y"]) status_md = _format_sidebar_status_md( updated, date_col=opts["resolved_date"], data_format=data_format, y_count=len(opts["default_y"]), ) return ( state, gr.Dataframe(value=_preview_df(updated)), profile_md, status_md, gr.Dropdown(choices=all_cols, value=opts["resolved_date"]), gr.Column(visible=(data_format == "Long")), gr.Dropdown(choices=opts["string_cols"], value=opts["group_default"]), gr.Dropdown(choices=opts["value_options"], value=opts["value_default"]), gr.CheckboxGroup(choices=opts["available_y"], value=opts["default_y"]), gr.Dropdown(choices=all_cols, value=cast_col), f"*Applied cast: `{cast_col}` -> {cast_type}*", ) def on_apply_setup(state, date_col, data_format, group_col, value_col, y_cols, dup_action, missing_action, freq_override): def _error_return(message: str): return ( state, # 0 app_state gr.Column(visible=False), # 1 welcome_col gr.Column(visible=True), # 2 analysis_col message, # 3 quality_md "", # 4 freq_info_md gr.Dropdown(), # 5 single_y_dd gr.Dropdown(), # 6 color_by_dd None, # 7 single_plot "", # 8 single_stats_md "", # 9 single_interp_md gr.CheckboxGroup(), # 10 panel_cols_cbg None, # 11 panel_plot "", # 12 panel_summary_md "", # 13 panel_interp_md gr.CheckboxGroup(), # 14 spag_cols_cbg gr.Dropdown(), # 15 spag_highlight_dd None, # 16 spag_plot "", # 17 spag_summary_md "", # 18 spag_interp_md gr.Radio(choices=[_MODE_SINGLE], value=_MODE_SINGLE), # 19 viz_mode_radio "Load data and apply setup.", # 20 mode_hint_md gr.Dropdown(choices=_CHART_TYPES, value=_CHART_TYPES[0]), # 21 single_chart_dd "", # 22 single_gate_md gr.Column(visible=False), # 23 single_mode_col gr.Column(visible=False), # 24 panel_mode_col gr.Column(visible=False), # 25 spag_mode_col pd.DataFrame(), # 26 cleaned_preview_df _format_sidebar_status_md(None), # 27 status_md ) if not y_cols: return _error_return("*Select at least one value column.*") raw_df = state.get("raw_df_original") if raw_df is None: return _error_return("*No data loaded.*") # Pivot if long format if data_format == "Long" and group_col and value_col: effective_df = pivot_long_to_wide(raw_df, date_col, group_col, value_col) else: effective_df = raw_df # Clean cleaned, report = clean_dataframe( effective_df, date_col, list(y_cols), dup_action=dup_action, missing_action=missing_action, ) freq = detect_frequency(cleaned, date_col) cleaned = add_time_features(cleaned, date_col) if freq_override and freq_override.strip(): freq = FrequencyInfo( label=freq_override.strip(), median_delta=freq.median_delta, is_regular=freq.is_regular, ) state["cleaned_df"] = cleaned state["cleaning_report"] = report state["freq_info"] = freq state["date_col"] = date_col state["y_cols"] = list(y_cols) state["setup_applied"] = True state["single_png"] = None state["panel_png"] = None state["spag_png"] = None # Create QueryChat instance if available if check_querychat_available(): try: state["qc"] = create_querychat( cleaned, name="uploaded_data", date_col=date_col, y_cols=list(y_cols), freq_label=freq.label, ) except Exception: state["qc"] = None else: state["qc"] = None quality_md = _format_cleaning_report_md(report) freq_text = f"Frequency: **{freq.label}** ({'regular' if freq.is_regular else 'irregular'})" # Color-by choices color_by_choices = [] if "month" in cleaned.columns: color_by_choices = ["month", "quarter", "year", "day_of_week"] y_list = list(y_cols) panel_default = y_list[:4] if len(y_list) >= 2 else y_list highlight_choices = ["(none)"] + y_list mode_choices, recommended_mode, mode_hint = _get_mode_config(len(y_list)) single_visible, panel_visible, spag_visible = _mode_visibility(recommended_mode) state["mode_choices"] = mode_choices state["recommended_mode"] = recommended_mode chart_choices, chart_gate_md = _available_chart_choices( cleaned, date_col, y_list[0], freq ) status_md = _format_sidebar_status_md( df=cleaned, date_col=date_col, data_format=data_format, y_count=len(y_list), freq_label=freq.label, cleaned_rows=report.rows_after, ) return ( state, # 0 app_state gr.Column(visible=False), # 1 welcome_col gr.Column(visible=True), # 2 analysis_col quality_md, # 3 quality_md freq_text, # 4 freq_info_md gr.Dropdown(choices=y_list, value=y_list[0]), # 5 single_y_dd gr.Dropdown(choices=color_by_choices, value=color_by_choices[0] if color_by_choices else None),# 6 color_by_dd None, # 7 single_plot "", # 8 single_stats_md "", # 9 single_interp_md gr.CheckboxGroup(choices=y_list, value=panel_default), # 10 panel_cols_cbg None, # 11 panel_plot "", # 12 panel_summary_md "", # 13 panel_interp_md gr.CheckboxGroup(choices=y_list, value=y_list), # 14 spag_cols_cbg gr.Dropdown(choices=highlight_choices, value="(none)"), # 15 spag_highlight_dd None, # 16 spag_plot "", # 17 spag_summary_md "", # 18 spag_interp_md gr.Radio(choices=mode_choices, value=recommended_mode), # 19 viz_mode_radio mode_hint, # 20 mode_hint_md gr.Dropdown(choices=chart_choices, value=chart_choices[0]), # 21 single_chart_dd chart_gate_md, # 22 single_gate_md gr.Column(visible=single_visible), # 23 single_mode_col gr.Column(visible=panel_visible), # 24 panel_mode_col gr.Column(visible=spag_visible), # 25 spag_mode_col _preview_df(cleaned), # 26 cleaned_preview_df status_md, # 27 status_md ) # ---- Visibility toggles ---- def on_dr_mode_change(mode): return ( gr.Column(visible=(mode == "Last N years")), gr.Column(visible=(mode == "Custom")), ) def on_viz_mode_change(mode): single_visible, panel_visible, spag_visible = _mode_visibility(mode) return ( gr.Column(visible=single_visible), gr.Column(visible=panel_visible), gr.Column(visible=spag_visible), ) def on_single_y_change(state, y_col, current_chart): cleaned_df = state.get("cleaned_df") date_col = state.get("date_col") freq_info = state.get("freq_info") if cleaned_df is None or not y_col or not date_col: return gr.Dropdown(choices=_CHART_TYPES, value=_CHART_TYPES[0]), "" chart_choices, chart_gate_md = _available_chart_choices(cleaned_df, date_col, y_col, freq_info) next_chart = current_chart if current_chart in chart_choices else chart_choices[0] return gr.Dropdown(choices=chart_choices, value=next_chart), chart_gate_md def on_chart_type_change(chart_type): return ( gr.Column(visible=("Colored Markers" in chart_type)), gr.Column(visible=(chart_type in ("Seasonal Plot", "Seasonal Sub-series"))), gr.Column(visible=(chart_type == "Rolling Mean Overlay")), gr.Column(visible=(chart_type == "Lag Plot")), gr.Column(visible=(chart_type == "Decomposition")), ) def on_palette_change(pal_name): colors = get_palette_colors(pal_name, 8) return render_palette_preview(colors) # ---- Single series ---- def on_single_update(state, y_col, dr_mode, dr_n, dr_start, dr_end, chart_type, palette_name, color_by, period, window, lag, decomp_model): cleaned_df = state.get("cleaned_df") date_col = state.get("date_col") freq_info = state.get("freq_info") if cleaned_df is None or not y_col: return state, None, "*No data. Apply setup first.*" palette_colors = get_palette_colors(palette_name, 12) df_plot = _apply_date_filter(cleaned_df.copy(), date_col, dr_mode, dr_n, dr_start, dr_end) if df_plot.empty: return state, None, "*No data in selected range.*" blocked = _chart_availability(df_plot, date_col, y_col, freq_info) if chart_type in blocked: return state, None, f"*{chart_type} unavailable: {blocked[chart_type]}*" fig, err = _generate_single_chart( df_plot, date_col, y_col, chart_type, palette_colors, color_by, period, window, lag, decomp_model, freq_info, ) if err: return state, None, f"**Chart error:** {err}" # Summary stats stats = compute_summary_stats(df_plot, date_col, y_col) stats_md = _format_summary_stats_md(stats) # Store PNG for AI interpretation state["single_png"] = fig_to_png_bytes(fig) if fig else None return state, fig, stats_md def on_single_interpret(state): png = state.get("single_png") if not png: return "*Generate a chart first, then click Interpret.*" if not check_api_key_available(): return "*Set `OPENAI_API_KEY` to enable AI interpretation.*" freq_info = state.get("freq_info") metadata = { "chart_type": "single series", "frequency_label": freq_info.label if freq_info else "Unknown", "y_column": state.get("y_cols", [""])[0], } interp = interpret_chart(png, metadata) return render_interpretation_markdown(interp) # ---- Panel ---- def on_panel_update(state, panel_cols, panel_chart, shared_y, palette_name): cleaned_df = state.get("cleaned_df") date_col = state.get("date_col") if cleaned_df is None or not panel_cols or len(panel_cols) < 2: return state, None, "*Select 2+ columns and apply setup first.*" palette_colors = get_palette_colors(palette_name, len(panel_cols)) try: fig = plot_panel( cleaned_df, date_col, list(panel_cols), chart_type=panel_chart, shared_y=shared_y, title="Panel Comparison", style_dict=_STYLE_DICT, palette_colors=palette_colors, ) summary_df = compute_multi_series_summary(cleaned_df, date_col, list(panel_cols)) summary_md = _format_multi_summary_md(summary_df) state["panel_png"] = fig_to_png_bytes(fig) return state, fig, summary_md except Exception as exc: return state, None, f"**Panel chart error:** {exc}" def on_panel_interpret(state): png = state.get("panel_png") if not png: return "*Generate a panel chart first, then click Interpret.*" if not check_api_key_available(): return "*Set `OPENAI_API_KEY` to enable AI interpretation.*" freq_info = state.get("freq_info") metadata = { "chart_type": "panel (small multiples)", "frequency_label": freq_info.label if freq_info else "Unknown", "y_column": ", ".join(state.get("y_cols", [])), } interp = interpret_chart(png, metadata) return render_interpretation_markdown(interp) # ---- Spaghetti ---- def on_spag_update(state, spag_cols, alpha, topn, highlight, show_median, palette_name): cleaned_df = state.get("cleaned_df") date_col = state.get("date_col") if cleaned_df is None or not spag_cols or len(spag_cols) < 2: return state, None, "*Select 2+ columns and apply setup first.*" highlight_col = highlight if highlight and highlight != "(none)" else None top_n = int(topn) if topn and int(topn) > 0 else None palette_colors = get_palette_colors(palette_name, len(spag_cols)) try: fig = plot_spaghetti( cleaned_df, date_col, list(spag_cols), alpha=float(alpha), highlight_col=highlight_col, top_n=top_n, show_median_band=bool(show_median), title="Spaghetti Plot", style_dict=_STYLE_DICT, palette_colors=palette_colors, ) summary_df = compute_multi_series_summary(cleaned_df, date_col, list(spag_cols)) summary_md = _format_multi_summary_md(summary_df) state["spag_png"] = fig_to_png_bytes(fig) return state, fig, summary_md except Exception as exc: return state, None, f"**Spaghetti chart error:** {exc}" def on_spag_interpret(state): png = state.get("spag_png") if not png: return "*Generate a spaghetti chart first, then click Interpret.*" if not check_api_key_available(): return "*Set `OPENAI_API_KEY` to enable AI interpretation.*" freq_info = state.get("freq_info") metadata = { "chart_type": "spaghetti (overlay)", "frequency_label": freq_info.label if freq_info else "Unknown", "y_column": ", ".join(state.get("y_cols", [])), } interp = interpret_chart(png, metadata) return render_interpretation_markdown(interp) def on_auto_generate(state, viz_mode, single_y, dr_mode, dr_n, dr_start, dr_end, single_chart, single_pal, color_by, period, window, lag, decomp_model, panel_cols, panel_chart, panel_shared, panel_pal, spag_cols, spag_alpha, spag_topn, spag_highlight, spag_median, spag_pal): if viz_mode == _MODE_PANEL: next_state, fig, summary_md = on_panel_update( state, panel_cols, panel_chart, panel_shared, panel_pal ) return next_state, None, "", fig, summary_md, None, "" if viz_mode == _MODE_SPAG: next_state, fig, summary_md = on_spag_update( state, spag_cols, spag_alpha, spag_topn, spag_highlight, spag_median, spag_pal ) return next_state, None, "", None, "", fig, summary_md next_state, fig, stats_md = on_single_update( state, single_y, dr_mode, dr_n, dr_start, dr_end, single_chart, single_pal, color_by, period, window, lag, decomp_model ) return next_state, fig, stats_md, None, "", None, "" # --------------------------------------------------------------------------- # Build the Gradio app # --------------------------------------------------------------------------- with gr.Blocks( title="Time Series Visualizer", ) as demo: app_state = gr.State(_make_empty_state()) # =================================================================== # Sidebar # =================================================================== with gr.Sidebar(): gr.HTML( '
' 'Time Series Visualizer
' 'ISA 444 · Miami University' '
' ) gr.Markdown("### Data Input") file_upload = gr.File( label="Upload a CSV file", file_types=[".csv", ".tsv", ".txt"], type="filepath", ) demo_dd = gr.Dropdown( label="Or load a demo dataset", choices=_DEMO_CHOICES, value="(none)", ) reset_btn = gr.Button("Reset all", variant="secondary", size="sm") delim_md = gr.Markdown("") status_md = gr.Markdown("*No data loaded yet.*") with gr.Accordion("About", open=False): gr.Markdown("**Vibe-Coded By**") gr.HTML(_DEVELOPER_CARD) gr.Markdown("v0.2.0 · Last updated Feb 2026", elem_classes=["caption"]) gr.Markdown("---") gr.Markdown("### QueryChat") if check_querychat_available(): gr.Markdown( "QueryChat natural-language filtering is available. " "Use the chat below to filter your dataset." ) else: gr.Markdown( "*Set `OPENAI_API_KEY` and install `querychat[gradio]` " "to enable natural-language data filtering.*" ) # =================================================================== # Welcome screen # =================================================================== with gr.Column(visible=True) as welcome_col: gr.Markdown(_WELCOME_MD) # =================================================================== # Setup panel (hidden until data loaded) # =================================================================== with gr.Column(visible=False) as setup_col: gr.Markdown("## Step 1. Understand Data") gr.Markdown("*Check inferred structure and preview your raw file before cleaning.*") raw_profile_md = gr.Markdown("") raw_preview_df = gr.Dataframe( label="Raw data preview (first 10 rows)", interactive=False, wrap=True, ) gr.Markdown("## Step 2. Prepare Data") gr.Markdown("*If the date guess is wrong, change the date column - value choices update automatically.*") with gr.Row(): with gr.Column(scale=1, min_width=300): gr.Markdown("### Structure") date_col_dd = gr.Dropdown(label="Date column", choices=[]) format_radio = gr.Radio( label="Data format", choices=["Wide", "Long"], value="Wide", ) with gr.Column(visible=False) as long_col: group_col_dd = gr.Dropdown(label="Group column", choices=[]) value_col_dd = gr.Dropdown(label="Value column", choices=[]) y_cols_cbg = gr.CheckboxGroup(label="Value column(s)", choices=[]) with gr.Column(scale=1, min_width=300): gr.Markdown("### Cleaning") dup_dd = gr.Dropdown( label="Duplicate dates", choices=["keep_last", "keep_first", "drop_all"], value="keep_last", ) missing_dd = gr.Dropdown( label="Missing values", choices=["interpolate", "ffill", "drop"], value="interpolate", ) freq_tb = gr.Textbox( label="Override frequency label (optional)", placeholder="e.g. Daily, Weekly, Monthly", ) apply_btn = gr.Button("Apply setup", variant="primary") freq_info_md = gr.Markdown("") with gr.Accordion("Type Casting (optional)", open=False): gr.Markdown("*Use this when a column is read with the wrong dtype.*") cast_col_dd = gr.Dropdown(label="Column", choices=[]) cast_type_dd = gr.Dropdown( label="Cast to", choices=["Numeric (coerce)", "Datetime (coerce)", "String"], value="Numeric (coerce)", ) cast_apply_btn = gr.Button("Apply cast", variant="secondary", size="sm") cast_status_md = gr.Markdown("") # =================================================================== # Analysis panel (hidden until setup applied) # =================================================================== with gr.Column(visible=False) as analysis_col: gr.Markdown("## Step 3. Visualize") mode_hint_md = gr.Markdown("") viz_mode_radio = gr.Radio( label="Visualization mode", choices=[_MODE_SINGLE], value=_MODE_SINGLE, ) with gr.Accordion("Cleaned Data Preview", open=False): cleaned_preview_df = gr.Dataframe( label="Cleaned data preview (first 10 rows)", interactive=False, wrap=True, ) with gr.Accordion("Data Quality Report", open=False): quality_md = gr.Markdown("") with gr.Column(visible=False) as single_mode_col: with gr.Row(): with gr.Column(scale=1, min_width=280): single_y_dd = gr.Dropdown(label="Value column", choices=[]) dr_mode_radio = gr.Radio( label="Date range", choices=["All", "Last N years", "Custom"], value="All", ) with gr.Column(visible=False) as dr_n_col: dr_n_slider = gr.Slider( label="Years", minimum=1, maximum=20, value=5, step=1, ) with gr.Column(visible=False) as dr_custom_col: dr_start_tb = gr.Textbox(label="Start date", placeholder="YYYY-MM-DD") dr_end_tb = gr.Textbox(label="End date", placeholder="YYYY-MM-DD") single_chart_dd = gr.Dropdown( label="Chart type", choices=_CHART_TYPES, value=_CHART_TYPES[0], ) single_gate_md = gr.Markdown("") single_pal_dd = gr.Dropdown( label="Color palette", choices=_PALETTE_NAMES, value=_PALETTE_NAMES[0], ) single_swatch = gr.Plot(label="Palette preview", show_label=False) with gr.Column(visible=False) as color_by_col: color_by_dd = gr.Dropdown( label="Color by", choices=["month", "quarter", "year", "day_of_week"], ) with gr.Column(visible=False) as period_col: period_dd = gr.Dropdown( label="Period", choices=["month", "quarter"], value="month", ) with gr.Column(visible=False) as window_col: window_slider = gr.Slider( label="Window", minimum=2, maximum=52, value=12, step=1, ) with gr.Column(visible=False) as lag_col: lag_slider = gr.Slider( label="Lag", minimum=1, maximum=52, value=1, step=1, ) with gr.Column(visible=False) as decomp_col: decomp_dd = gr.Dropdown( label="Model", choices=["additive", "multiplicative"], value="additive", ) single_update_btn = gr.Button("Update chart", variant="primary") with gr.Column(scale=3): single_plot = gr.Plot(label="Chart") with gr.Accordion("Summary Statistics", open=False): single_stats_md = gr.Markdown("") with gr.Accordion("AI Chart Interpretation", open=False): gr.Markdown( "*The chart image (PNG) is sent to OpenAI for " "interpretation. Do not include sensitive data.*" ) single_interp_btn = gr.Button( "Interpret Chart with AI", variant="secondary", ) single_interp_md = gr.Markdown("") with gr.Column(visible=False) as panel_mode_col: with gr.Row(): with gr.Column(scale=1, min_width=280): panel_cols_cbg = gr.CheckboxGroup( label="Columns to plot", choices=[], ) panel_chart_dd = gr.Dropdown( label="Chart type", choices=["line", "bar"], value="line", ) panel_shared_cb = gr.Checkbox( label="Shared Y axis", value=True, ) panel_pal_dd = gr.Dropdown( label="Color palette", choices=_PALETTE_NAMES, value=_PALETTE_NAMES[0], ) panel_update_btn = gr.Button("Update chart", variant="primary") with gr.Column(scale=3): panel_plot = gr.Plot(label="Panel Chart") with gr.Accordion("Per-series Summary", open=False): panel_summary_md = gr.Markdown("") with gr.Accordion("AI Chart Interpretation", open=False): gr.Markdown( "*The chart image (PNG) is sent to OpenAI for " "interpretation. Do not include sensitive data.*" ) panel_interp_btn = gr.Button( "Interpret Chart with AI", variant="secondary", ) panel_interp_md = gr.Markdown("") with gr.Column(visible=False) as spag_mode_col: with gr.Row(): with gr.Column(scale=1, min_width=280): spag_cols_cbg = gr.CheckboxGroup( label="Columns to include", choices=[], ) spag_alpha_slider = gr.Slider( label="Alpha (opacity)", minimum=0.05, maximum=1.0, value=0.15, step=0.05, ) spag_topn_num = gr.Number( label="Highlight top N (0 = none)", value=0, minimum=0, precision=0, ) spag_highlight_dd = gr.Dropdown( label="Highlight series", choices=["(none)"], value="(none)", ) spag_median_cb = gr.Checkbox( label="Show Median + IQR band", value=False, ) spag_pal_dd = gr.Dropdown( label="Color palette", choices=_PALETTE_NAMES, value=_PALETTE_NAMES[0], ) spag_update_btn = gr.Button("Update chart", variant="primary") with gr.Column(scale=3): spag_plot = gr.Plot(label="Spaghetti Chart") with gr.Accordion("Per-series Summary", open=False): spag_summary_md = gr.Markdown("") with gr.Accordion("AI Chart Interpretation", open=False): gr.Markdown( "*The chart image (PNG) is sent to OpenAI for " "interpretation. Do not include sensitive data.*" ) spag_interp_btn = gr.Button( "Interpret Chart with AI", variant="secondary", ) spag_interp_md = gr.Markdown("") # =================================================================== # Event wiring # =================================================================== _DATA_LOAD_OUTPUTS = [ app_state, setup_col, date_col_dd, format_radio, long_col, group_col_dd, value_col_dd, y_cols_cbg, delim_md, status_md, raw_profile_md, raw_preview_df, cast_col_dd, cast_status_md, welcome_col, analysis_col, viz_mode_radio, mode_hint_md, single_chart_dd, single_gate_md, single_mode_col, panel_mode_col, spag_mode_col, cleaned_preview_df, ] file_upload.change( on_file_upload, inputs=[file_upload, app_state], outputs=_DATA_LOAD_OUTPUTS, ) demo_dd.change( on_demo_select, inputs=[demo_dd, app_state], outputs=_DATA_LOAD_OUTPUTS, ) # Reset via page reload reset_btn.click(fn=None, js="() => { window.location.reload(); }") date_col_dd.change( on_setup_inputs_change, inputs=[date_col_dd, format_radio, group_col_dd, value_col_dd, y_cols_cbg, app_state], outputs=[long_col, group_col_dd, value_col_dd, y_cols_cbg, raw_profile_md, status_md], ) format_radio.change( on_setup_inputs_change, inputs=[date_col_dd, format_radio, group_col_dd, value_col_dd, y_cols_cbg, app_state], outputs=[long_col, group_col_dd, value_col_dd, y_cols_cbg, raw_profile_md, status_md], ) # Long-format column changes update y_cols for _comp in [group_col_dd, value_col_dd]: _comp.change( on_long_cols_change, inputs=[date_col_dd, group_col_dd, value_col_dd, app_state], outputs=[y_cols_cbg], ) y_cols_cbg.change( on_y_selection_change, inputs=[date_col_dd, format_radio, y_cols_cbg, app_state], outputs=[raw_profile_md, status_md], ) cast_apply_btn.click( on_cast_apply, inputs=[app_state, cast_col_dd, cast_type_dd, date_col_dd, format_radio, group_col_dd, value_col_dd, y_cols_cbg], outputs=[ app_state, raw_preview_df, raw_profile_md, status_md, date_col_dd, long_col, group_col_dd, value_col_dd, y_cols_cbg, cast_col_dd, cast_status_md, ], ) # Apply setup _APPLY_OUTPUTS = [ app_state, # 0 welcome_col, # 1 analysis_col, # 2 quality_md, # 3 freq_info_md, # 4 # Single single_y_dd, # 5 color_by_dd, # 6 single_plot, # 7 single_stats_md, # 8 single_interp_md, # 9 # Panel panel_cols_cbg, # 10 panel_plot, # 11 panel_summary_md, # 12 panel_interp_md, # 13 # Spaghetti spag_cols_cbg, # 14 spag_highlight_dd, # 15 spag_plot, # 16 spag_summary_md, # 17 spag_interp_md, # 18 viz_mode_radio, # 19 mode_hint_md, # 20 single_chart_dd, # 21 single_gate_md, # 22 single_mode_col, # 23 panel_mode_col, # 24 spag_mode_col, # 25 cleaned_preview_df, # 26 status_md, # 27 ] apply_btn.click( on_apply_setup, inputs=[ app_state, date_col_dd, format_radio, group_col_dd, value_col_dd, y_cols_cbg, dup_dd, missing_dd, freq_tb, ], outputs=_APPLY_OUTPUTS, ).then( on_auto_generate, inputs=[ app_state, viz_mode_radio, single_y_dd, dr_mode_radio, dr_n_slider, dr_start_tb, dr_end_tb, single_chart_dd, single_pal_dd, color_by_dd, period_dd, window_slider, lag_slider, decomp_dd, panel_cols_cbg, panel_chart_dd, panel_shared_cb, panel_pal_dd, spag_cols_cbg, spag_alpha_slider, spag_topn_num, spag_highlight_dd, spag_median_cb, spag_pal_dd, ], outputs=[app_state, single_plot, single_stats_md, panel_plot, panel_summary_md, spag_plot, spag_summary_md], ) # Date range mode visibility dr_mode_radio.change( on_dr_mode_change, inputs=[dr_mode_radio], outputs=[dr_n_col, dr_custom_col], ) viz_mode_radio.change( on_viz_mode_change, inputs=[viz_mode_radio], outputs=[single_mode_col, panel_mode_col, spag_mode_col], ) single_y_dd.change( on_single_y_change, inputs=[app_state, single_y_dd, single_chart_dd], outputs=[single_chart_dd, single_gate_md], ) # Chart type conditional controls single_chart_dd.change( on_chart_type_change, inputs=[single_chart_dd], outputs=[color_by_col, period_col, window_col, lag_col, decomp_col], ) # Palette swatch preview single_pal_dd.change(on_palette_change, [single_pal_dd], [single_swatch]) # Initialise swatch on load demo.load(on_palette_change, [single_pal_dd], [single_swatch]) # ---- Single series chart + stats ---- single_update_btn.click( on_single_update, inputs=[ app_state, single_y_dd, dr_mode_radio, dr_n_slider, dr_start_tb, dr_end_tb, single_chart_dd, single_pal_dd, color_by_dd, period_dd, window_slider, lag_slider, decomp_dd, ], outputs=[app_state, single_plot, single_stats_md], ) single_interp_btn.click( on_single_interpret, inputs=[app_state], outputs=[single_interp_md], ) # ---- Panel chart + stats ---- panel_update_btn.click( on_panel_update, inputs=[app_state, panel_cols_cbg, panel_chart_dd, panel_shared_cb, panel_pal_dd], outputs=[app_state, panel_plot, panel_summary_md], ) panel_interp_btn.click( on_panel_interpret, inputs=[app_state], outputs=[panel_interp_md], ) # ---- Spaghetti chart + stats ---- spag_update_btn.click( on_spag_update, inputs=[ app_state, spag_cols_cbg, spag_alpha_slider, spag_topn_num, spag_highlight_dd, spag_median_cb, spag_pal_dd, ], outputs=[app_state, spag_plot, spag_summary_md], ) spag_interp_btn.click( on_spag_interpret, inputs=[app_state], outputs=[spag_interp_md], ) # --------------------------------------------------------------------------- # Launch # --------------------------------------------------------------------------- if __name__ == "__main__": demo.launch( server_name="0.0.0.0", server_port=7860, theme=MiamiTheme(), css=get_miami_css(), )