python_dash_TARS.py

"""
© Benjamin R. Berton 2025 Polytechnique Montreal
"""
import dash
from dash import html, dcc, dash_table, Input, Output, State, callback_context
import plotly.graph_objects as go
import pandas as pd
import os
import base64
import io

app = dash.Dash(__name__, suppress_callback_exceptions=True, external_stylesheets=['assets/styles.css'])
server = app.server

# Navigation menu component
def create_navbar(current_pathname="/"):
    return html.Div([
        html.Div([
            dcc.Link("Interdependence Analysis Table", href="/", className="nav-link" + (" active" if current_pathname == "/" else "")),
            dcc.Link("Assumptions", href="/assumptions", className="nav-link" + (" active" if current_pathname == "/assumptions" else ""))
        ], style={"textAlign": "left", "marginBottom": "20px"}),
    ])

DATA_FILE = "./V6/IA_V6.csv"
if os.path.exists(DATA_FILE):
    df = pd.read_csv(DATA_FILE)

color_options = ["red", "yellow", "green", "orange"]

# Updated dropdown options for all categorical columns
dropdowns = {
    # Original color-based columns
    "Human*": {
        'options': [{'label': c.capitalize(), 'value': c} for c in color_options]
    },
    "TARS": {
        'options': [{'label': c.capitalize(), 'value': c} for c in color_options]
    },
    "TARS*": {
        'options': [{'label': c.capitalize(), 'value': c} for c in color_options]
    },
    "Human": {
        'options': [{'label': c.capitalize(), 'value': c} for c in color_options]
    },
    # New categorical columns
    "Category": {
        'options': [
            {'label': 'NORM', 'value': 'NORM'},
            {'label': 'ABNORM', 'value': 'ABNORM'},
            {'label': 'EMER', 'value': 'EMER'}
        ]
    },
    "Type": {
        'options': [
            {'label': 'SOP', 'value': 'SOP'},
            {'label': 'Checklist', 'value': 'Checklist'},
            {'label': 'Memory Item', 'value': 'Memory Item'}
        ]
    },
    "Constraint Type": {
        'options': [
            {'label': 'Soft-time', 'value': 'Soft-time'},
            {'label': 'Hard-time', 'value': 'Hard-time'}
        ]
    },
    "Execution Type": {
        'options': [
            {'label': 'One-off', 'value': 'One-off'},
            {'label': 'Continuous', 'value': 'Continuous'},
            {'label': 'Cyclic', 'value': 'Cyclic'},
            {'label': 'Monitoring', 'value': 'Monitoring'}
        ]
    }
}
def style_table(df):
    styles = []
    # Only apply color styling to the color-based columns
    color_columns = ["Human*", "TARS", "TARS*", "Human"]
    
    for i, row in df.iterrows():
        for col in color_columns:
            if col in df.columns:
                value = row[col]
                if isinstance(value, str) and value.strip().lower() in ["red", "yellow", "green", "orange"]:
                    color = value.strip().lower()
                    styles.append({
                        'if': {'row_index': i, 'column_id': col},
                        'backgroundColor': color,
                        'color': color,  # Text color matches background (invisible text)
                        'textAlign': 'center',
                        'fontWeight': 'bold'
                    })
    return styles

# No need to assign Row since it already exists in the CSV
# df = df.assign(Row=lambda x: x.index + 1)

# Updated editable columns to include new fields
editable_columns = [
    "Procedure", "Category", "Type", "Task Object", "Value", 
    "Human*", "TARS", "TARS*", "Human", 
    "Observability", "Predictability", "Directability", 
    "Information Requirement", "Constraint Type", "Time constraint (in s)", "Execution Type"
]

table = dash_table.DataTable(
    id='responsibility-table',
    columns=[
        {"name": col, "id": col, "editable": col in editable_columns}
        for col in df.columns
    ],
    data=df.to_dict("records"),  # Remove the .assign() since Row already exists
    editable=True,
    row_deletable=True,
    dropdown=dropdowns,
    style_data_conditional=style_table(df),
    style_cell={'textAlign': 'left', 'padding': '5px', 'whiteSpace': 'normal'},
    style_cell_conditional=[
        {
            'if': {'column_id': 'Human*'},
            'borderLeft': '3px solid black'
        },
        {
            'if': {'column_id': 'Human'},
            'borderRight': '3px solid black'
        },
        {
            'if': {'column_id': 'TARS'},
            'borderRight': '2px solid black'
        }
    ],
    style_header={'fontWeight': 'bold', 'backgroundColor': '#f0f0f0'},
    style_table={'overflowX': 'auto', 'border': '1px solid lightgrey'},
    active_cell={"row": 0, "column_id":"Objective", "column":1}
)

columns=[
    {"name": "Row", "id": "Row", "editable": False},
    {"name": "Procedure", "id": "Procedure", "editable": True},
    {"name": "Category", "id": "Category", "editable": True, "presentation": "dropdown"},
    {"name": "Type", "id": "Type", "editable": True, "presentation": "dropdown"},
    {"name": "Task Object", "id": "Task Object", "editable": True},
    {"name": "Value", "id": "Value", "editable": True},
    {"name": "Human*", "id": "Human*", "editable": True, "presentation": "dropdown"},
    {"name": "TARS", "id": "TARS", "editable": True, "presentation": "dropdown"},
    {"name": "TARS*", "id": "TARS*", "editable": True, "presentation": "dropdown"},
    {"name": "Human", "id": "Human", "editable": True, "presentation": "dropdown"},
    {"name": "Observability", "id": "Observability", "editable": True},
    {"name": "Predictability", "id": "Predictability", "editable": True},
    {"name": "Directability", "id": "Directability", "editable": True},
    {"name": "Information Requirement", "id": "Information Requirement", "editable": True},
    {"name": "Constraint Type", "id": "Constraint Type", "editable": True, "presentation": "dropdown"},
    {"name": "Time constraint (in s)", "id": "Time constraint (in s)", "editable": True, "type": "numeric"},
    {"name": "Execution Type", "id": "Execution Type", "editable": True, "presentation": "dropdown"},
]

def wrap_text(text, max_width=30):
    import textwrap
    if not isinstance(text, str):
        return ""
    return '<br>'.join(textwrap.wrap(text, width=max_width))


def get_chosen_agent_with_override(row, highlight_track, category_overrides, agent_colors):
    """
    Determine the chosen agent for highlighting, considering category overrides.
    
    Args:
        row: DataFrame row containing task data
        highlight_track: The base highlight strategy
        category_overrides: Dict mapping category names to "HUMAN" or "TARS"
        agent_colors: Dict with "HUMAN*" and "TARS*" color values
    
    Returns:
        The chosen agent ("HUMAN*" or "TARS*") or None
    """
    category = row.get("Category", "")
    
    # Check if there's an override for this category
    if category in category_overrides:
        override = category_overrides[category]
        if override == "TARS":
            # Use TARS* if available (not red)
            if agent_colors["TARS*"] in ["green", "yellow", "orange"]:
                return "TARS*"
            # Fall back to HUMAN* if TARS* not available
            elif agent_colors["HUMAN*"] in ["green", "yellow", "orange"]:
                return "HUMAN*"
        elif override == "HUMAN":
            # Use HUMAN* if available (not red)
            if agent_colors["HUMAN*"] in ["green", "yellow", "orange"]:
                return "HUMAN*"
            # Fall back to TARS* if HUMAN* not available
            elif agent_colors["TARS*"] in ["green", "yellow", "orange"]:
                return "TARS*"
        return None
    
    # No override - use base strategy
    if highlight_track in ["human_baseline", "human_full_support"]:
        # Human performer only
        if agent_colors["HUMAN*"] in ["green", "yellow", "orange"]:
            return "HUMAN*"
    elif highlight_track in ["agent_whenever_possible", "agent_whenever_possible_full_support"]:
        # TARS* preferred when available
        if agent_colors["TARS*"] in ["green", "yellow", "orange"]:
            return "TARS*"
        elif agent_colors["HUMAN*"] in ["green", "yellow", "orange"]:
            return "HUMAN*"
    elif highlight_track == "most_reliable":
        # Most reliable (green > yellow preference, HUMAN* first)
        if agent_colors["HUMAN*"] == "green":
            return "HUMAN*"
        elif agent_colors["TARS*"] == "green":
            return "TARS*"
        elif agent_colors["HUMAN*"] == "yellow":
            return "HUMAN*"
        elif agent_colors["TARS*"] == "yellow":
            return "TARS*"
    
    return None


def build_performers_only_figure_base(df, procedure):
    """Build base figure for performers-only view (single procedure) without highlighting."""
    agents = ["HUMAN*", "TARS*"]
    VALID_COLORS = {"red", "yellow", "green", "orange", "black", "grey"}
    
    proc_df = df[df["Procedure"] == procedure].reset_index(drop=True)
    proc_df["task_idx"] = proc_df.index
    tasks = proc_df["Task Object"].tolist()
    height = 400 + len(tasks) * 60
    
    dots = []
    dashed_arrows = []
    black_to_black_arrows = []
    
    for idx, row in proc_df.iterrows():
        task_idx = row["task_idx"]
        black_points = []
        
        # HUMAN* performer
        human_performer_val = row["Human*"].strip().lower() if isinstance(row["Human*"], str) else ""
        if human_performer_val in VALID_COLORS and human_performer_val != "red":
            dots.append({"task": task_idx, "agent": "HUMAN*", "shape": "circle", "color": human_performer_val, "role": "performer"})
            black_points.append("HUMAN*")
        
        # TARS* performer
        tars_performer_val = row["TARS*"].strip().lower() if isinstance(row["TARS*"], str) else ""
        if tars_performer_val in VALID_COLORS and tars_performer_val != "red":
            dots.append({"task": task_idx, "agent": "TARS*", "shape": "circle", "color": tars_performer_val, "role": "performer"})
            black_points.append("TARS*")
        
        # TARS supporter (square in TARS* column)
        tars_supporter_val = row["TARS"].strip().lower() if isinstance(row["TARS"], str) and row["TARS"] else ""
        if tars_supporter_val in VALID_COLORS and tars_supporter_val != "red":
            dots.append({"task": task_idx, "agent": "TARS*", "shape": "square", "color": tars_supporter_val, "role": "supporter"})
        
        # HUMAN supporter (square in HUMAN* column)
        human_supporter_val = row["Human"].strip().lower() if isinstance(row["Human"], str) and row["Human"] else ""
        if human_supporter_val in VALID_COLORS and human_supporter_val != "red":
            dots.append({"task": task_idx, "agent": "HUMAN*", "shape": "square", "color": human_supporter_val, "role": "supporter"})
        
        # Dashed arrows for support relationships (going BETWEEN columns)
        # TARS supporter helps HUMAN* performer: arrow from TARS* to HUMAN*
        if "HUMAN*" in black_points and tars_supporter_val in VALID_COLORS and tars_supporter_val != "red":
            dashed_arrows.append({"start_agent": "TARS*", "end_agent": "HUMAN*", "task": task_idx, "is_support": True})
        
        # HUMAN supporter helps TARS* performer: arrow from HUMAN* to TARS*
        if "TARS*" in black_points and human_supporter_val in VALID_COLORS and human_supporter_val != "red":
            dashed_arrows.append({"start_agent": "HUMAN*", "end_agent": "TARS*", "task": task_idx, "is_support": True})
    
    # Black-to-black arrows between consecutive tasks
    for i in range(len(proc_df) - 1):
        current_performers = [d["agent"] for d in dots if d["task"] == i and d["shape"] == "circle"]
        next_performers = [d["agent"] for d in dots if d["task"] == i + 1 and d["shape"] == "circle"]
        for curr in current_performers:
            for nxt in next_performers:
                black_to_black_arrows.append({
                    "start_task": i, "start_agent": curr,
                    "end_task": i + 1, "end_agent": nxt
                })
    
    # Build figure
    agent_pos = {agent: i for i, agent in enumerate(agents)}
    fig = go.Figure()
    
    for dot in dots:
        row = proc_df.iloc[dot["task"]]
        hover_text = f"<b>Task:</b> {wrap_text(row['Task Object'])}<br><b>Agent:</b> {dot['agent']}<br><b>Role:</b> {dot['role']}"
        
        if dot["shape"] == "circle":
            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]], y=[dot["task"]], mode="markers",
                marker=dict(size=30, color=dot["color"]), showlegend=False,
                hoverinfo="text", hovertext=hover_text
            ))
        else:  # square - add black border for visibility
            # Draw black border square first
            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]], y=[dot["task"]], mode="markers",
                marker=dict(size=18, color="black", symbol="square"),
                showlegend=False, hoverinfo="skip"
            ))
            # Draw colored square on top
            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]], y=[dot["task"]], mode="markers",
                marker=dict(size=14, color=dot["color"], symbol="square"),
                showlegend=False, hoverinfo="text", hovertext=hover_text
            ))
    
    # Add dashed arrows (shapes) - stored for later highlighting
    # Group by task to apply vertical offset when there are two arrows at same task
    dashed_arrow_info = []
    arrows_by_task = {}
    for arrow in dashed_arrows:
        task = arrow["task"]
        if task not in arrows_by_task:
            arrows_by_task[task] = []
        arrows_by_task[task].append(arrow)
    
    for task, task_arrows in arrows_by_task.items():
        if len(task_arrows) == 2:
            offsets = [-0.08, 0.08]  # Vertical offset for distinguishability
        else:
            offsets = [0] * len(task_arrows)
        
        for arrow, offset in zip(task_arrows, offsets):
            fig.add_shape(
                type="line",
                x0=agent_pos[arrow["start_agent"]], y0=arrow["task"] + offset,
                x1=agent_pos[arrow["end_agent"]], y1=arrow["task"] + offset,
                line=dict(color="black", width=2, dash="dot")
            )
            dashed_arrow_info.append({"task": arrow["task"], "end_agent": arrow["end_agent"], "offset": offset})
    
    # Add black-to-black arrows (annotations) - stored for later highlighting
    for arrow in black_to_black_arrows:
        fig.add_annotation(
            x=agent_pos[arrow["end_agent"]], y=arrow["end_task"],
            ax=agent_pos[arrow["start_agent"]], ay=arrow["start_task"],
            xref="x", yref="y", axref="x", ayref="y",
            showarrow=True, arrowhead=3, arrowsize=1, arrowwidth=2, arrowcolor="black", opacity=0.9,
            standoff=18, startstandoff=18
        )
    
    fig.update_layout(
        title=f"Workflow for {procedure} - Performers Only",
        xaxis=dict(tickvals=list(agent_pos.values()), ticktext=list(agent_pos.keys()), title="Agent", showgrid=True, gridcolor='lightgrey', range=[-0.5, 1.5]),
        yaxis=dict(tickvals=list(range(len(tasks))), ticktext=[wrap_text(t) for t in tasks], title="Task", autorange="reversed", showgrid=True, gridcolor='lightgrey'),
        margin=dict(l=250, r=50, t=50, b=50), height=height, plot_bgcolor='white', paper_bgcolor='white'
    )
    
    arrow_info = {"dashed_arrows": dashed_arrow_info, "black_to_black_arrows": black_to_black_arrows}
    return fig, arrow_info


def build_performers_only_combined_figure_base(df):
    """Build base figure for performers-only combined view without highlighting."""
    agents = ["HUMAN*", "TARS*"]
    VALID_COLORS = {"red", "yellow", "green", "orange", "black", "grey"}
    
    df = df.reset_index(drop=True)
    df["task_idx"] = df.index
    tasks = df["Task Object"].tolist()
    height = len(tasks) * 100
    
    dots = []
    dashed_arrows = []
    black_to_black_arrows = []
    
    for idx, row in df.iterrows():
        task_idx = row["task_idx"]
        black_points = []
        
        human_performer_val = row["Human*"].strip().lower() if isinstance(row["Human*"], str) else ""
        if human_performer_val in VALID_COLORS and human_performer_val != "red":
            dots.append({"task": task_idx, "agent": "HUMAN*", "shape": "circle", "color": human_performer_val, "role": "performer"})
            black_points.append("HUMAN*")
        
        tars_performer_val = row["TARS*"].strip().lower() if isinstance(row["TARS*"], str) else ""
        if tars_performer_val in VALID_COLORS and tars_performer_val != "red":
            dots.append({"task": task_idx, "agent": "TARS*", "shape": "circle", "color": tars_performer_val, "role": "performer"})
            black_points.append("TARS*")
        
        tars_supporter_val = row["TARS"].strip().lower() if isinstance(row["TARS"], str) and row["TARS"] else ""
        if tars_supporter_val in VALID_COLORS and tars_supporter_val != "red":
            dots.append({"task": task_idx, "agent": "TARS*", "shape": "square", "color": tars_supporter_val, "role": "supporter"})
        
        human_supporter_val = row["Human"].strip().lower() if isinstance(row["Human"], str) and row["Human"] else ""
        if human_supporter_val in VALID_COLORS and human_supporter_val != "red":
            dots.append({"task": task_idx, "agent": "HUMAN*", "shape": "square", "color": human_supporter_val, "role": "supporter"})
        
        # Dashed arrows for support relationships (going BETWEEN columns)
        # TARS supporter helps HUMAN* performer: arrow from TARS* to HUMAN*
        if "HUMAN*" in black_points and tars_supporter_val in VALID_COLORS and tars_supporter_val != "red":
            dashed_arrows.append({"start_agent": "TARS*", "end_agent": "HUMAN*", "task": task_idx, "is_support": True})
        
        # HUMAN supporter helps TARS* performer: arrow from HUMAN* to TARS*
        if "TARS*" in black_points and human_supporter_val in VALID_COLORS and human_supporter_val != "red":
            dashed_arrows.append({"start_agent": "HUMAN*", "end_agent": "TARS*", "task": task_idx, "is_support": True})
    
    for i in range(len(df) - 1):
        current_performers = [d["agent"] for d in dots if d["task"] == i and d["shape"] == "circle"]
        next_performers = [d["agent"] for d in dots if d["task"] == i + 1 and d["shape"] == "circle"]
        for curr in current_performers:
            for nxt in next_performers:
                black_to_black_arrows.append({"start_task": i, "start_agent": curr, "end_task": i + 1, "end_agent": nxt})
    
    agent_pos = {agent: i for i, agent in enumerate(agents)}
    fig = go.Figure()
    
    for dot in dots:
        row = df.iloc[dot["task"]]
        hover_text = f"<b>Task:</b> {wrap_text(row['Task Object'])}<br><b>Agent:</b> {dot['agent']}<br><b>Role:</b> {dot['role']}"
        
        if dot["shape"] == "circle":
            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]], y=[dot["task"]], mode="markers",
                marker=dict(size=40, color=dot["color"]), showlegend=False,
                hoverinfo="text", hovertext=hover_text
            ))
        else:  # square - add black border for visibility
            # Draw black border square first
            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]], y=[dot["task"]], mode="markers",
                marker=dict(size=20, color="black", symbol="square"),
                showlegend=False, hoverinfo="skip"
            ))
            # Draw colored square on top
            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]], y=[dot["task"]], mode="markers",
                marker=dict(size=16, color=dot["color"], symbol="square"),
                showlegend=False, hoverinfo="text", hovertext=hover_text
            ))
    
    # Group dashed arrows by task for vertical offset when overlapping
    dashed_arrow_info = []
    arrows_by_task = {}
    for arrow in dashed_arrows:
        task = arrow["task"]
        if task not in arrows_by_task:
            arrows_by_task[task] = []
        arrows_by_task[task].append(arrow)
    
    for task, task_arrows in arrows_by_task.items():
        if len(task_arrows) == 2:
            offsets = [-0.08, 0.08]  # Vertical offset for distinguishability
        else:
            offsets = [0] * len(task_arrows)
        
        for arrow, offset in zip(task_arrows, offsets):
            fig.add_shape(
                type="line",
                x0=agent_pos[arrow["start_agent"]], y0=arrow["task"] + offset,
                x1=agent_pos[arrow["end_agent"]], y1=arrow["task"] + offset,
                line=dict(color="black", width=2, dash="dot")
            )
            dashed_arrow_info.append({"task": arrow["task"], "end_agent": arrow["end_agent"], "offset": offset})
    
    for arrow in black_to_black_arrows:
        fig.add_annotation(
            x=agent_pos[arrow["end_agent"]], y=arrow["end_task"],
            ax=agent_pos[arrow["start_agent"]], ay=arrow["start_task"],
            xref="x", yref="y", axref="x", ayref="y",
            showarrow=True, arrowhead=3, arrowsize=1, arrowwidth=2, arrowcolor="black", opacity=0.9, standoff=15
        )
    
    fig.update_layout(
        title="Combined Workflow Graph - Performers Only (All Procedures)",
        xaxis=dict(tickvals=list(agent_pos.values()), ticktext=list(agent_pos.keys()), title="Agent", showgrid=True, gridcolor='lightgrey', range=[-0.5, 1.5]),
        yaxis=dict(tickvals=list(df["task_idx"]), ticktext=[wrap_text(f"{p} | {t}") for p, t in zip(df["Procedure"], df["Task Object"])], title="Procedure | Task", autorange="reversed", showgrid=True, gridcolor='lightgrey'),
        margin=dict(l=250, r=50, t=50, b=50), height=height, plot_bgcolor='white', paper_bgcolor='white'
    )
    
    arrow_info = {"dashed_arrows": dashed_arrow_info, "black_to_black_arrows": black_to_black_arrows}
    return fig, arrow_info


def build_interdependence_figure_base(df, procedure):
    """Build base figure for full interdependence view (single procedure) without highlighting."""
    agents = ["HUMAN*", "TARS", "TARS*", "HUMAN"]
    VALID_COLORS = {"red", "yellow", "green", "orange", "black", "grey"}
    
    proc_df = df[df["Procedure"] == procedure].reset_index(drop=True)
    proc_df["task_idx"] = proc_df.index
    tasks = proc_df["Task Object"].tolist()
    height = 400 + len(tasks) * 60
    
    dots = []
    dashed_arrows = []
    black_to_black_arrows = []
    
    for idx, row in proc_df.iterrows():
        task_idx = row["task_idx"]
        black_points = []
        
        for col in ["Human*", "TARS*"]:
            val = row[col].strip().lower() if isinstance(row[col], str) else ""
            if val in VALID_COLORS:
                dots.append({"task": task_idx, "agent": col.upper(), "color": val})
                if val != "red":
                    black_points.append(col.upper())
        
        for col in ["Human", "TARS"]:
            val = row[col].strip().lower() if isinstance(row[col], str) else ""
            if val in VALID_COLORS:
                agent = col.upper()
                dots.append({"task": task_idx, "agent": agent, "color": val})
                
                if agent == "TARS" and "HUMAN*" in black_points and row["Human*"].strip().lower() != "red" and row["TARS"].strip().lower() != "red":
                    dashed_arrows.append({"start_agent": "TARS", "end_agent": "HUMAN*", "task": task_idx})
                elif agent == "HUMAN" and "TARS*" in black_points and row["TARS*"].strip().lower() != "red" and row["Human"].strip().lower() != "red":
                    dashed_arrows.append({"start_agent": "HUMAN", "end_agent": "TARS*", "task": task_idx})
    
    for i in range(len(proc_df) - 1):
        current_blacks = [d for d in dots if d["task"] == i and d["agent"] in ["HUMAN*", "TARS*"] and d["color"] != "red"]
        next_blacks = [d for d in dots if d["task"] == i + 1 and d["agent"] in ["HUMAN*", "TARS*"] and d["color"] != "red"]
        for curr in current_blacks:
            for nxt in next_blacks:
                black_to_black_arrows.append({"start_task": i, "start_agent": curr["agent"], "end_task": i + 1, "end_agent": nxt["agent"]})
    
    agent_pos = {agent: i for i, agent in enumerate(agents)}
    fig = go.Figure()
    
    for dot in dots:
        row = proc_df.iloc[dot["task"]]
        hover_text = f"<b>Task:</b> {wrap_text(row['Task Object'])}<br><b>Agent:</b> {dot['agent']}"
        fig.add_trace(go.Scatter(
            x=[agent_pos[dot["agent"]]], y=[dot["task"]], mode="markers",
            marker=dict(size=20, color=dot["color"]), showlegend=False,
            hoverinfo="text", hovertext=hover_text
        ))
    
    dashed_arrow_info = []
    for arrow in dashed_arrows:
        fig.add_shape(
            type="line",
            x0=agent_pos[arrow["start_agent"]], y0=arrow["task"],
            x1=agent_pos[arrow["end_agent"]], y1=arrow["task"],
            line=dict(color="black", width=2, dash="dot")
        )
        dashed_arrow_info.append({"task": arrow["task"], "end_agent": arrow["end_agent"]})
    
    for arrow in black_to_black_arrows:
        fig.add_annotation(
            x=agent_pos[arrow["end_agent"]], y=arrow["end_task"],
            ax=agent_pos[arrow["start_agent"]], ay=arrow["start_task"],
            xref="x", yref="y", axref="x", ayref="y",
            showarrow=True, arrowhead=3, arrowsize=1, arrowwidth=2, arrowcolor="black", opacity=0.9
        )
    
    fig.update_layout(
        title=f"Workflow for {procedure}",
        xaxis=dict(tickvals=list(agent_pos.values()), ticktext=list(agent_pos.keys()), title="Agent", showgrid=True, gridcolor='lightgrey'),
        yaxis=dict(tickvals=list(range(len(tasks))), ticktext=[wrap_text(t) for t in tasks], title="Task", autorange="reversed", showgrid=True, gridcolor='lightgrey'),
        margin=dict(l=250, r=50, t=50, b=50), height=height, plot_bgcolor='white', paper_bgcolor='white'
    )
    
    arrow_info = {"dashed_arrows": dashed_arrow_info, "black_to_black_arrows": black_to_black_arrows}
    return fig, arrow_info


def build_combined_interdependence_figure_base(df):
    """Build base figure for full interdependence combined view without highlighting."""
    agents = ["HUMAN*", "TARS", "TARS*", "HUMAN"]
    VALID_COLORS = {"red", "yellow", "green", "orange", "black", "grey"}
    
    df = df.reset_index(drop=True)
    df["task_idx"] = df.index
    tasks = df["Task Object"].tolist()
    height = len(tasks) * 90
    
    dots = []
    dashed_arrows = []
    black_to_black_arrows = []
    
    for idx, row in df.iterrows():
        task_idx = row["task_idx"]
        black_points = []
        
        for col in ["Human*", "TARS*"]:
            val = row[col].strip().lower() if isinstance(row[col], str) else ""
            if val in VALID_COLORS:
                dots.append({"task": task_idx, "agent": col.upper(), "color": val})
                if val != "red":
                    black_points.append(col.upper())
        
        for col in ["Human", "TARS"]:
            val = row[col].strip().lower() if isinstance(row[col], str) else ""
            if val in VALID_COLORS:
                agent = col.upper()
                dots.append({"task": task_idx, "agent": agent, "color": val})
                
                if agent == "TARS" and "HUMAN*" in black_points and row["Human*"].strip().lower() != "red" and row["TARS"].strip().lower() != "red":
                    dashed_arrows.append({"start_agent": "TARS", "end_agent": "HUMAN*", "task": task_idx})
                elif agent == "HUMAN" and "TARS*" in black_points and row["TARS*"].strip().lower() != "red" and row["Human"].strip().lower() != "red":
                    dashed_arrows.append({"start_agent": "HUMAN", "end_agent": "TARS*", "task": task_idx})
    
    for i in range(len(df) - 1):
        current_blacks = [d for d in dots if d["task"] == i and d["agent"] in ["HUMAN*", "TARS*"] and d["color"] != "red"]
        next_blacks = [d for d in dots if d["task"] == i + 1 and d["agent"] in ["HUMAN*", "TARS*"] and d["color"] != "red"]
        for curr in current_blacks:
            for nxt in next_blacks:
                black_to_black_arrows.append({"start_task": i, "start_agent": curr["agent"], "end_task": i + 1, "end_agent": nxt["agent"]})
    
    agent_pos = {agent: i for i, agent in enumerate(agents)}
    fig = go.Figure()
    
    for dot in dots:
        row = df.iloc[dot["task"]]
        hover_text = f"<b>Task:</b> {wrap_text(row['Task Object'])}<br><b>Agent:</b> {dot['agent']}"
        fig.add_trace(go.Scatter(
            x=[agent_pos[dot["agent"]]], y=[dot["task"]], mode="markers",
            marker=dict(size=20, color=dot["color"]), showlegend=False,
            hoverinfo="text", hovertext=hover_text
        ))
    
    dashed_arrow_info = []
    for arrow in dashed_arrows:
        fig.add_shape(
            type="line",
            x0=agent_pos[arrow["start_agent"]], y0=arrow["task"],
            x1=agent_pos[arrow["end_agent"]], y1=arrow["task"],
            line=dict(color="black", width=2, dash="dot")
        )
        dashed_arrow_info.append({"task": arrow["task"], "end_agent": arrow["end_agent"]})
    
    for arrow in black_to_black_arrows:
        fig.add_annotation(
            x=agent_pos[arrow["end_agent"]], y=arrow["end_task"],
            ax=agent_pos[arrow["start_agent"]], ay=arrow["start_task"],
            xref="x", yref="y", axref="x", ayref="y",
            showarrow=True, arrowhead=3, arrowsize=1, arrowwidth=2, arrowcolor="black", opacity=0.9
        )
    
    fig.update_layout(
        title="Combined Workflow Graph (All Procedures)",
        xaxis=dict(tickvals=list(agent_pos.values()), ticktext=list(agent_pos.keys()), title="Agent", showgrid=True, gridcolor='lightgrey'),
        yaxis=dict(tickvals=list(df["task_idx"]), ticktext=[wrap_text(f"{p} | {t}") for p, t in zip(df["Procedure"], df["Task Object"])], title="Procedure | Task", autorange="reversed", showgrid=True, gridcolor='lightgrey'),
        margin=dict(l=250, r=50, t=50, b=50), height=height, plot_bgcolor='white', paper_bgcolor='white'
    )
    
    arrow_info = {"dashed_arrows": dashed_arrow_info, "black_to_black_arrows": black_to_black_arrows}
    return fig, arrow_info


def build_performers_only_figures(df, highlight_track=None, category_overrides=None):
    """Build workflow graphs showing only HUMAN* and TARS* performers (no supporters)"""
    if category_overrides is None:
        category_overrides = {}
    
    agents = ["HUMAN*", "TARS*"]
    VALID_COLORS = {"red", "yellow", "green", "orange", "black", "grey"}

    figures = {}

    for procedure, proc_df in df.groupby("Procedure"):
        tasks = proc_df["Task Object"].tolist()
        height = 300 + len(tasks) * 100
        dots = []
        grey_to_black_arrows = []
        black_to_black_arrows = []
        highlight_track_list = []  # Unified track considering overrides
        dashed_arrows_to_highlight = []

        proc_df = proc_df.reset_index(drop=True)
        proc_df["task_idx"] = proc_df.index

        # Step 1: Add points for performers only
        for idx, row in proc_df.iterrows():
            task_idx = row["task_idx"]
            black_points = []

            agent_colors = {
                "HUMAN*": row["Human*"].strip().lower() if isinstance(row["Human*"], str) else "",
                "TARS*": row["TARS*"].strip().lower() if isinstance(row["TARS*"], str) else "",
            }

            # Determine chosen agent using the unified helper function
            chosen_agent = get_chosen_agent_with_override(row, highlight_track, category_overrides, agent_colors)
            if chosen_agent:
                highlight_track_list.append((task_idx, chosen_agent))

            # Add dots for each agent column showing their own capabilities
            # HUMAN* column: HUMAN* performer (circle)
            # TARS* column: TARS* performer (circle) + TARS supporter (square)
            
            # HUMAN* performer in HUMAN* column
            human_performer_val = row["Human*"].strip().lower() if isinstance(row["Human*"], str) else ""
            if human_performer_val in VALID_COLORS and human_performer_val != "red":
                dots.append({
                    "task": task_idx,
                    "agent": "HUMAN*",
                    "shape": "circle",
                    "color": human_performer_val,
                    "role": "performer"
                })
                black_points.append("HUMAN*")
            
            # TARS* performer in TARS* column
            tars_performer_val = row["TARS*"].strip().lower() if isinstance(row["TARS*"], str) else ""
            if tars_performer_val in VALID_COLORS and tars_performer_val != "red":
                dots.append({
                    "task": task_idx,
                    "agent": "TARS*",
                    "shape": "circle",
                    "color": tars_performer_val,
                    "role": "performer"
                })
                black_points.append("TARS*")
            
            # TARS supporter in TARS* column (as square)
            tars_supporter_val = row["TARS"].strip().lower() if isinstance(row["TARS"], str) and row["TARS"] else ""
            if tars_supporter_val in VALID_COLORS and tars_supporter_val != "red":
                dots.append({
                    "task": task_idx,
                    "agent": "TARS*",
                    "shape": "square",
                    "color": tars_supporter_val,
                    "role": "supporter"
                })
            
            # HUMAN supporter in HUMAN* column (as square)
            human_supporter_val = row["Human"].strip().lower() if isinstance(row["Human"], str) and row["Human"] else ""
            if human_supporter_val in VALID_COLORS and human_supporter_val != "red":
                dots.append({
                    "task": task_idx,
                    "agent": "HUMAN*",
                    "shape": "square",
                    "color": human_supporter_val,
                    "role": "supporter"
                })

            # Add dashed arrows from supporters to performers (even though supporters aren't shown)
            # TARS -> HUMAN*
            tars_val = row["TARS"].strip().lower() if isinstance(row["TARS"], str) else ""
            human_val = row["Human"].strip().lower() if isinstance(row["Human"], str) else ""
            
            if ("HUMAN*" in black_points and 
                row["Human*"].strip().lower() != "red" and 
                tars_val != "" and tars_val != "red"):
                dashed_arrow = {
                    "start_agent": "TARS*",  # TARS supporter helps HUMAN* performer
                    "end_agent": "HUMAN*",
                    "task": task_idx,
                    "is_support": True  # Flag to indicate this is a support relationship
                }
                grey_to_black_arrows.append(dashed_arrow)
                # Highlight support arrow if this task's performer is HUMAN* in the highlight track
                # and we're using a "full support" mode
                if highlight_track in ["human_full_support", "agent_whenever_possible_full_support", "most_reliable"]:
                    if (task_idx, "HUMAN*") in highlight_track_list:
                        dashed_arrows_to_highlight.append(dashed_arrow)

            # HUMAN -> TARS*
            if ("TARS*" in black_points and 
                row["TARS*"].strip().lower() != "red" and 
                human_val != "" and human_val != "red"):
                dashed_arrow = {
                    "start_agent": "HUMAN*",  # HUMAN supporter helps TARS* performer
                    "end_agent": "TARS*",
                    "task": task_idx,
                    "is_support": True
                }
                grey_to_black_arrows.append(dashed_arrow)
                # Highlight support arrow if this task's performer is TARS* in the highlight track
                # and we're using a "full support" mode
                if highlight_track in ["human_full_support", "agent_whenever_possible_full_support", "most_reliable"]:
                    if (task_idx, "TARS*") in highlight_track_list:
                        dashed_arrows_to_highlight.append(dashed_arrow)

        # Step 2: Black-to-black transitions between performers (circles only)
        for i in range(len(tasks) - 1):
            current_blacks = [d for d in dots if d["task"] == i and d["agent"] in ["HUMAN*", "TARS*"] and d["shape"] == "circle"]
            next_blacks = [d for d in dots if d["task"] == i + 1 and d["agent"] in ["HUMAN*", "TARS*"] and d["shape"] == "circle"]
            for curr in current_blacks:
                for nxt in next_blacks:
                    black_to_black_arrows.append({
                        "start_task": i,
                        "start_agent": curr["agent"],
                        "end_task": i + 1,
                        "end_agent": nxt["agent"]
                    })

        # Step 3: Create figure
        agent_pos = {agent: i for i, agent in enumerate(agents)}
        fig = go.Figure()

        for dot in dots:
            row = proc_df.iloc[dot["task"]]
            hover_text = (
                f"<b>Task:</b> {wrap_text(row['Task Object'])}<br>"
                f"<b>Agent:</b> {dot['agent']}<br>"
                f"<b>Role:</b> {dot['role'].capitalize()}<br><br>"
                f"<b>Observability:</b><br>{wrap_text(row.get('Observability', ''))}<br><br>"
                f"<b>Predictability:</b><br>{wrap_text(row.get('Predictability', ''))}<br><br>"
                f"<b>Directability:</b><br>{wrap_text(row.get('Directability', ''))}"
            )

            if dot["shape"] == "circle":
                # Draw performer circle
                fig.add_trace(go.Scatter(
                    x=[agent_pos[dot["agent"]]],
                    y=[dot["task"]],
                    mode="markers",
                    marker=dict(size=30, color=dot["color"]),
                    showlegend=False,
                    hoverinfo="text",
                    hovertext=hover_text
                ))
            else:  # square
                # Draw black border square
                fig.add_trace(go.Scatter(
                    x=[agent_pos[dot["agent"]]],
                    y=[dot["task"]],
                    mode="markers",
                    marker=dict(
                        size=20,
                        color="black",
                        symbol="square"
                    ),
                    showlegend=False,
                    hoverinfo="skip"
                ))
                # Draw colored square on top
                fig.add_trace(go.Scatter(
                    x=[agent_pos[dot["agent"]]],
                    y=[dot["task"]],
                    mode="markers",
                    marker=dict(
                        size=17,
                        color=dot["color"],
                        symbol="square"
                    ),
                    showlegend=False,
                    hoverinfo="text",
                    hovertext=hover_text
                ))

        # Add dashed horizontal lines for support relationships
        # Group arrows by task to detect overlaps
        arrows_by_task = {}
        for arrow in grey_to_black_arrows:
            if arrow.get("is_support"):
                task = arrow["task"]
                if task not in arrows_by_task:
                    arrows_by_task[task] = []
                arrows_by_task[task].append(arrow)
        
        for task, task_arrows in arrows_by_task.items():
            # If there are 2 arrows at the same task, offset them vertically
            if len(task_arrows) == 2:
                offsets = [-0.08, 0.08]  # Small vertical offset
            else:
                offsets = [0] * len(task_arrows)
            
            for arrow, offset in zip(task_arrows, offsets):
                is_highlighted = len(highlight_track_list) > 0 and arrow in dashed_arrows_to_highlight
                # Draw dashed line from start_agent to end_agent with offset
                fig.add_shape(
                    type="line",
                    x0=agent_pos[arrow["start_agent"]],
                    y0=arrow["task"] + offset,
                    x1=agent_pos[arrow["end_agent"]],
                    y1=arrow["task"] + offset,
                    line=dict(
                        color="crimson" if is_highlighted else "black",
                        width=4 if is_highlighted else 2,
                        dash="dot"
                    )
                )

        for arrow in black_to_black_arrows:
            # Use the unified highlight_track_list for all strategies including category overrides
            is_highlighted = (arrow["start_task"], arrow["start_agent"]) in highlight_track_list and \
                             (arrow["end_task"], arrow["end_agent"]) in highlight_track_list

            fig.add_annotation(
                x=agent_pos[arrow["end_agent"]],
                y=arrow["end_task"],
                ax=agent_pos[arrow["start_agent"]],
                ay=arrow["start_task"],
                xref="x", yref="y", axref="x", ayref="y",
                showarrow=True,
                arrowhead=3,
                arrowsize=1,
                arrowwidth=4 if is_highlighted else 2,
                arrowcolor="crimson" if is_highlighted else "black",
                opacity=0.9,
                standoff=18,  # Stop arrow at end circle edge (circle radius=15, add margin)
                startstandoff=18  # Start arrow from start circle edge
            )

        fig.update_layout(
            title=f"Workflow for {procedure} (Performers Only)",
            xaxis=dict(
                tickvals=list(agent_pos.values()),
                ticktext=list(agent_pos.keys()),
                title="Agent",
                showgrid=True,
                gridcolor='lightgrey',
                range=[-0.5, 1.5]  # Constrain x-axis to keep agents closer
            ),
            yaxis=dict(
                tickvals=list(range(len(tasks))),
                ticktext=[wrap_text(task) for task in tasks],
                title="Task",
                autorange="reversed",
                showgrid=True,
                gridcolor='lightgrey'
            ),
            height=height,
            margin=dict(l=200, r=50, t=50, b=50),
            plot_bgcolor='white',
            paper_bgcolor='white',
        )
        figures[procedure] = fig

    return figures


def build_performers_only_combined_figure(df, highlight_track=None, category_overrides=None):
    """Build combined workflow graph showing only HUMAN* and TARS* performers (no supporters)"""
    if category_overrides is None:
        category_overrides = {}
    
    agents = ["HUMAN*", "TARS*"]
    VALID_COLORS = {"red", "yellow", "green", "orange", "black", "grey"}
    tasks = df["Task Object"].tolist()
    height = 600 + len(tasks) * 100
    dots = []
    grey_to_black_arrows = []
    black_to_black_arrows = []

    df = df.reset_index(drop=True)
    df["task_idx"] = df.index

    highlight_track_list = []  # Unified track considering overrides
    dashed_arrows_to_highlight = []

    for idx, row in df.iterrows():
        task_idx = row["task_idx"]
        black_points = []

        agent_colors = {
            "HUMAN*": row["Human*"].strip().lower() if isinstance(row["Human*"], str) else "",
            "TARS*": row["TARS*"].strip().lower() if isinstance(row["TARS*"], str) else "",
        }

        # Determine chosen agent using the unified helper function
        chosen_agent = get_chosen_agent_with_override(row, highlight_track, category_overrides, agent_colors)
        if chosen_agent:
            highlight_track_list.append((task_idx, chosen_agent))

        # Add dots for each agent column showing their own capabilities
        # HUMAN* column: HUMAN* performer (circle)
        # TARS* column: TARS* performer (circle) + TARS supporter (square)
        
        # HUMAN* performer in HUMAN* column
        human_performer_val = row["Human*"].strip().lower() if isinstance(row["Human*"], str) else ""
        if human_performer_val in VALID_COLORS and human_performer_val != "red":
            dots.append({
                "task": task_idx,
                "agent": "HUMAN*",
                "shape": "circle",
                "color": human_performer_val,
                "role": "performer"
            })
            black_points.append("HUMAN*")
        
        # TARS* performer in TARS* column
        tars_performer_val = row["TARS*"].strip().lower() if isinstance(row["TARS*"], str) else ""
        if tars_performer_val in VALID_COLORS and tars_performer_val != "red":
            dots.append({
                "task": task_idx,
                "agent": "TARS*",
                "shape": "circle",
                "color": tars_performer_val,
                "role": "performer"
            })
            black_points.append("TARS*")
        
        # TARS supporter in TARS* column (as square)
        tars_supporter_val = row["TARS"].strip().lower() if isinstance(row["TARS"], str) and row["TARS"] else ""
        if tars_supporter_val in VALID_COLORS and tars_supporter_val != "red":
            dots.append({
                "task": task_idx,
                "agent": "TARS*",
                "shape": "square",
                "color": tars_supporter_val,
                "role": "supporter"
            })
        
        # HUMAN supporter in HUMAN* column (as square)
        human_supporter_val = row["Human"].strip().lower() if isinstance(row["Human"], str) and row["Human"] else ""
        if human_supporter_val in VALID_COLORS and human_supporter_val != "red":
            dots.append({
                "task": task_idx,
                "agent": "HUMAN*",
                "shape": "square",
                "color": human_supporter_val,
                "role": "supporter"
            })

        # Add dashed arrows from supporters to performers (even though supporters aren't shown)
        # TARS -> HUMAN*
        tars_val = row["TARS"].strip().lower() if isinstance(row["TARS"], str) else ""
        human_val = row["Human"].strip().lower() if isinstance(row["Human"], str) else ""
        
        if ("HUMAN*" in black_points and 
            row["Human*"].strip().lower() != "red" and 
            tars_val != "" and tars_val != "red"):
            dashed_arrow = {
                "start_agent": "TARS*",  # TARS supporter helps HUMAN* performer
                "end_agent": "HUMAN*",
                "task": task_idx,
                "is_support": True
            }
            grey_to_black_arrows.append(dashed_arrow)
            # Highlight support arrow if this task's performer is HUMAN* in the highlight track
            if highlight_track in ["human_full_support", "agent_whenever_possible_full_support", "most_reliable"]:
                if (task_idx, "HUMAN*") in highlight_track_list:
                    dashed_arrows_to_highlight.append(dashed_arrow)

        # HUMAN -> TARS*
        if ("TARS*" in black_points and 
            row["TARS*"].strip().lower() != "red" and 
            human_val != "" and human_val != "red"):
            dashed_arrow = {
                "start_agent": "HUMAN*",  # HUMAN supporter helps TARS* performer
                "end_agent": "TARS*",
                "task": task_idx,
                "is_support": True
            }
            grey_to_black_arrows.append(dashed_arrow)
            # Highlight support arrow if this task's performer is TARS* in the highlight track
            if highlight_track in ["human_full_support", "agent_whenever_possible_full_support", "most_reliable"]:
                if (task_idx, "TARS*") in highlight_track_list:
                    dashed_arrows_to_highlight.append(dashed_arrow)

    # Black-to-black transitions between performers (circles only)
    for i in range(len(df) - 1):
        current_blacks = [d for d in dots if d["task"] == i and d["agent"] in ["HUMAN*", "TARS*"] and d["shape"] == "circle"]
        next_blacks = [d for d in dots if d["task"] == i + 1 and d["agent"] in ["HUMAN*", "TARS*"] and d["shape"] == "circle"]
        for curr in current_blacks:
            for nxt in next_blacks:
                black_to_black_arrows.append({
                    "start_task": i,
                    "start_agent": curr["agent"],
                    "end_task": i + 1,
                    "end_agent": nxt["agent"]
                })

    agent_pos = {agent: i for i, agent in enumerate(agents)}
    fig = go.Figure()

    for dot in dots:
        row = df.iloc[dot["task"]]
        hover_text = (
            f"<b>Task:</b> {wrap_text(row['Task Object'])}<br>"
            f"<b>Agent:</b> {dot['agent']}<br>"
            f"<b>Role:</b> {dot['role'].capitalize()}<br><br>"
            f"<b>Observability:</b><br>{wrap_text(row.get('Observability', ''))}<br><br>"
            f"<b>Predictability:</b><br>{wrap_text(row.get('Predictability', ''))}<br><br>"
            f"<b>Directability:</b><br>{wrap_text(row.get('Directability', ''))}"
        )

        if dot["shape"] == "circle":
            # Draw performer circle
            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]],
                y=[dot["task"]],
                mode="markers",
                marker=dict(size=40, color=dot["color"]),
                showlegend=False,
                hoverinfo="text",
                hovertext=hover_text
            ))
        else:  # square
            # Draw black border square
            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]],
                y=[dot["task"]],
                mode="markers",
                marker=dict(
                    size=20,
                    color="black",
                    symbol="square"
                ),
                showlegend=False,
                hoverinfo="skip"
            ))
            # Draw colored square on top
            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]],
                y=[dot["task"]],
                mode="markers",
                marker=dict(
                    size=17,
                    color=dot["color"],
                    symbol="square"
                ),
                showlegend=False,
                hoverinfo="text",
                hovertext=hover_text
            ))

    # Add dashed horizontal lines for support relationships
    # Group arrows by task to detect overlaps
    arrows_by_task = {}
    for arrow in grey_to_black_arrows:
        if arrow.get("is_support"):
            task = arrow["task"]
            if task not in arrows_by_task:
                arrows_by_task[task] = []
            arrows_by_task[task].append(arrow)
    
    for task, task_arrows in arrows_by_task.items():
        # If there are 2 arrows at the same task, offset them vertically
        if len(task_arrows) == 2:
            offsets = [-0.08, 0.08]  # Small vertical offset
        else:
            offsets = [0] * len(task_arrows)
        
        for arrow, offset in zip(task_arrows, offsets):
            is_highlighted = len(highlight_track_list) > 0 and arrow in dashed_arrows_to_highlight
            # Draw dashed line from start_agent to end_agent with offset
            fig.add_shape(
                type="line",
                x0=agent_pos[arrow["start_agent"]],
                y0=arrow["task"] + offset,
                x1=agent_pos[arrow["end_agent"]],
                y1=arrow["task"] + offset,
                line=dict(
                    color="crimson" if is_highlighted else "black",
                    width=4 if is_highlighted else 2,
                    dash="dot"
                )
            )

    for arrow in black_to_black_arrows:
        # Use the unified highlight_track_list for all strategies including category overrides
        is_highlighted = (arrow["start_task"], arrow["start_agent"]) in highlight_track_list and \
                         (arrow["end_task"], arrow["end_agent"]) in highlight_track_list

        fig.add_annotation(
            x=agent_pos[arrow["end_agent"]],
            y=arrow["end_task"],
            ax=agent_pos[arrow["start_agent"]],
            ay=arrow["start_task"],
            xref="x", yref="y", axref="x", ayref="y",
            showarrow=True,
            arrowhead=3,
            arrowsize=1,
            arrowwidth=4 if is_highlighted else 2,
            arrowcolor="crimson" if is_highlighted else "black",
            opacity=0.9,
            standoff=15  # Stop arrow at circle edge
        )

    fig.update_layout(
        title="Combined Workflow Graph - Performers Only (All Procedures)",
        xaxis=dict(
            tickvals=list(agent_pos.values()),
            ticktext=list(agent_pos.keys()),
            title="Agent",
            showgrid=True,
            gridcolor='lightgrey',
            range=[-0.5, 1.5]  # Constrain x-axis to keep agents closer
        ),
        yaxis=dict(
            tickvals=list(df["task_idx"]),
            ticktext=[wrap_text(f"{p} | {t}") for p, t in zip(df["Procedure"], df["Task Object"])],
            title="Procedure | Task",
            autorange="reversed",
            showgrid=True,
            gridcolor='lightgrey'
        ),
        margin=dict(l=250, r=50, t=50, b=50),
        height=height,
        plot_bgcolor='white',
        paper_bgcolor='white',
    )
    return fig


def build_interdependence_figures(df, highlight_track=None, category_overrides=None):
    if category_overrides is None:
        category_overrides = {}
    
    agents = ["HUMAN*", "TARS", "TARS*", "HUMAN"]
    VALID_COLORS = {"red", "yellow", "green", "orange", "black", "grey"}

    figures = {}

    for procedure, proc_df in df.groupby("Procedure"):
        tasks = proc_df["Task Object"].tolist()
        height = 300 + len(tasks) * 100
        dots = []
        grey_to_black_arrows = []
        black_to_black_arrows = []
        highlight_track_list = []  # Unified track considering overrides
        dashed_arrows_to_highlight = []

        proc_df = proc_df.reset_index(drop=True)
        proc_df["task_idx"] = proc_df.index

        # Step 1: Add points
        for idx, row in proc_df.iterrows():
            task_idx = row["task_idx"]
            black_points = []

            agent_colors = {
                "HUMAN*": row["Human*"].strip().lower() if isinstance(row["Human*"], str) else "",
                "TARS*": row["TARS*"].strip().lower() if isinstance(row["TARS*"], str) else "",
            }

            # Determine chosen agent using the unified helper function
            chosen_agent = get_chosen_agent_with_override(row, highlight_track, category_overrides, agent_colors)
            if chosen_agent:
                highlight_track_list.append((task_idx, chosen_agent))

            for col in ["Human*", "TARS*"]:
                val = row[col].strip().lower() if isinstance(row[col], str) else ""
                if val in VALID_COLORS:
                    dots.append({"task": task_idx, "agent": col.upper(), "color": val})
                    if val != "red":
                        black_points.append(col.upper())

            for col in ["Human", "TARS"]:
                val = row[col].strip().lower() if isinstance(row[col], str) else ""
                if val in VALID_COLORS:
                    agent = col.upper()
                    dots.append({"task": task_idx, "agent": agent, "color": val})

                    if (agent == "TARS"
                        and "HUMAN*" in black_points
                        and row["Human*"].strip().lower() != "red"
                        and row["TARS"].strip().lower() != "red"):
                        dashed_arrow = {
                            "start_agent": "TARS",
                            "end_agent": "HUMAN*",
                            "task": task_idx
                        }
                        grey_to_black_arrows.append(dashed_arrow)
                        # Highlight support arrow if this task's performer is HUMAN* in the highlight track
                        if highlight_track in ["human_full_support", "agent_whenever_possible_full_support", "most_reliable"]:
                            if (task_idx, "HUMAN*") in highlight_track_list:
                                dashed_arrows_to_highlight.append(dashed_arrow)

                    elif (agent == "HUMAN"
                          and "TARS*" in black_points
                          and row["TARS*"].strip().lower() != "red"
                          and row["Human"].strip().lower() != "red"):
                        dashed_arrow = {
                            "start_agent": "HUMAN",
                            "end_agent": "TARS*",
                            "task": task_idx
                        }
                        grey_to_black_arrows.append(dashed_arrow)
                        # Highlight support arrow if this task's performer is TARS* in the highlight track
                        if highlight_track in ["human_full_support", "agent_whenever_possible_full_support", "most_reliable"]:
                            if (task_idx, "TARS*") in highlight_track_list:
                                dashed_arrows_to_highlight.append(dashed_arrow)

        # Step 2: Black-to-black transitions
        for i in range(len(tasks) - 1):
            current_blacks = [d for d in dots if d["task"] == i and d["agent"] in ["HUMAN*", "TARS*"] and d["color"] != "red"]
            next_blacks = [d for d in dots if d["task"] == i + 1 and d["agent"] in ["HUMAN*", "TARS*"] and d["color"] != "red"]
            for curr in current_blacks:
                for nxt in next_blacks:
                    black_to_black_arrows.append({
                        "start_task": i,
                        "start_agent": curr["agent"],
                        "end_task": i + 1,
                        "end_agent": nxt["agent"]
                    })

        # Step 3: Create figure
        agent_pos = {agent: i for i, agent in enumerate(agents)}
        fig = go.Figure()

        for dot in dots:
            row = proc_df.iloc[dot["task"]]  # get the row by task index
            hover_text = (
                f"<b>Task:</b> {wrap_text(row['Task Object'])}<br>"
                f"<b>Agent:</b> {dot['agent']}<br><br>"
                f"<b>Observability:</b><br>{wrap_text(row.get('Observability', ''))}<br><br>"
                f"<b>Predictability:</b><br>{wrap_text(row.get('Predictability', ''))}<br><br>"
                f"<b>Directability:</b><br>{wrap_text(row.get('Directability', ''))}"
            )


            fig.add_trace(go.Scatter(
                x=[agent_pos[dot["agent"]]],
                y=[dot["task"]],
                mode="markers",
                marker=dict(size=20, color=dot["color"]),
                showlegend=False,
                hoverinfo="text",
                hovertext=hover_text
            ))


        for arrow in grey_to_black_arrows:
            is_highlighted = len(highlight_track_list) > 0 and arrow in dashed_arrows_to_highlight
            fig.add_shape(
                type="line",
                x0=agent_pos[arrow["start_agent"]],
                y0=arrow["task"],
                x1=agent_pos[arrow["end_agent"]],
                y1=arrow["task"],
                line=dict(
                    color="crimson" if is_highlighted else "black",
                    width=4 if is_highlighted else 2,
                    dash="dot"
                )
            )

        for arrow in black_to_black_arrows:
            # Use the unified highlight_track_list for all strategies including category overrides
            is_highlighted = (arrow["start_task"], arrow["start_agent"]) in highlight_track_list and \
                             (arrow["end_task"], arrow["end_agent"]) in highlight_track_list

            fig.add_annotation(
                x=agent_pos[arrow["end_agent"]],
                y=arrow["end_task"],
                ax=agent_pos[arrow["start_agent"]],
                ay=arrow["start_task"],
                xref="x", yref="y", axref="x", ayref="y",
                showarrow=True,
                arrowhead=3,
                arrowsize=1,
                arrowwidth=4 if is_highlighted else 2,
                arrowcolor="crimson" if is_highlighted else "black",
                opacity=0.9
            )

        fig.update_layout(
            title=f"Workflow for {procedure}",
            xaxis=dict(
                tickvals=list(agent_pos.values()),
                ticktext=list(agent_pos.keys()),
                title="Agent",
                showgrid=True,
                gridcolor='lightgrey'
            ),
            yaxis=dict(
                tickvals=list(range(len(tasks))),
                ticktext=[wrap_text(task) for task in tasks],
                title="Task",
                autorange="reversed",
                showgrid=True,
                gridcolor='lightgrey'
            ),
            height=height,
            margin=dict(l=200, r=50, t=50, b=50),
            plot_bgcolor='white',
            paper_bgcolor='white',
        )
        figures[procedure] = fig
    return figures

def build_combined_interdependence_figure(df, highlight_track=None, category_overrides=None):
    if category_overrides is None:
        category_overrides = {}
    
    agents = ["HUMAN*", "TARS", "TARS*", "HUMAN"]
    VALID_COLORS = {"red", "yellow", "green", "orange", "black", "grey"}
    tasks = df["Task Object"].tolist()
    height = 600 + len(tasks) * 100
    dots = []
    grey_to_black_arrows = []
    black_to_black_arrows = []

    df = df.reset_index(drop=True)
    df["task_idx"] = df.index  # Unique index across all tasks

    # Unified track considering category overrides
    highlight_track_list = []
    dashed_arrows_to_highlight = []

    for idx, row in df.iterrows():
        task_idx = row["task_idx"]
        black_points = []

        agent_colors = {
            "HUMAN*": row["Human*"].strip().lower() if isinstance(row["Human*"], str) else "",
            "TARS*": row["TARS*"].strip().lower() if isinstance(row["TARS*"], str) else "",
        }

        # Determine chosen agent using the unified helper function
        chosen_agent = get_chosen_agent_with_override(row, highlight_track, category_overrides, agent_colors)
        if chosen_agent:
            highlight_track_list.append((task_idx, chosen_agent))

        for col in ["Human*", "TARS*"]:
            val = row[col].strip().lower() if isinstance(row[col], str) else ""
            if val in VALID_COLORS:
                dots.append({"task": task_idx, "agent": col.upper(), "color": val})
                if val != "red":
                    black_points.append(col.upper())

        for col in ["Human", "TARS"]:
            val = row[col].strip().lower() if isinstance(row[col], str) else ""
            if val in VALID_COLORS:
                agent = col.upper()
                dots.append({"task": task_idx, "agent": agent, "color": val})

                if (
                    agent == "TARS"
                    and "HUMAN*" in black_points
                    and row["Human*"].strip().lower() != "red"
                    and row["TARS"].strip().lower() != "red"
                ):
                    dashed_arrow = {
                        "start_agent": "TARS",
                        "end_agent": "HUMAN*",
                        "task": task_idx
                    }
                    grey_to_black_arrows.append(dashed_arrow)
                    # Highlight if this task's chosen agent is HUMAN*
                    if (task_idx, "HUMAN*") in highlight_track_list:
                        dashed_arrows_to_highlight.append(dashed_arrow)

                elif (
                    agent == "HUMAN"
                    and "TARS*" in black_points
                    and row["TARS*"].strip().lower() != "red"
                    and row["Human"].strip().lower() != "red"
                ):
                    dashed_arrow = {
                        "start_agent": "HUMAN",
                        "end_agent": "TARS*",
                        "task": task_idx
                    }
                    grey_to_black_arrows.append(dashed_arrow)
                    # Highlight if this task's chosen agent is TARS*
                    if (task_idx, "TARS*") in highlight_track_list:
                        dashed_arrows_to_highlight.append(dashed_arrow)

    for i in range(len(df) - 1):
        current_blacks = [d for d in dots if d["task"] == i and d["agent"] in ["HUMAN*", "TARS*"] and d["color"] != "red"]
        next_blacks = [d for d in dots if d["task"] == i + 1 and d["agent"] in ["HUMAN*", "TARS*"] and d["color"] != "red"]
        for curr in current_blacks:
            for nxt in next_blacks:
                black_to_black_arrows.append({
                    "start_task": i,
                    "start_agent": curr["agent"],
                    "end_task": i + 1,
                    "end_agent": nxt["agent"]
                })

    agent_pos = {agent: i for i, agent in enumerate(agents)}
    fig = go.Figure()

    for dot in dots:
        row = df.iloc[dot["task"]]
        hover_text = (
            f"<b>Task:</b> {wrap_text(row['Task Object'])}<br>"
            f"<b>Agent:</b> {dot['agent']}<br><br>"
            f"<b>Observability:</b><br>{wrap_text(row.get('Observability', ''))}<br><br>"
            f"<b>Predictability:</b><br>{wrap_text(row.get('Predictability', ''))}<br><br>"
            f"<b>Directability:</b><br>{wrap_text(row.get('Directability', ''))}"
        )


        fig.add_trace(go.Scatter(
            x=[agent_pos[dot["agent"]]],
            y=[dot["task"]],
            mode="markers",
            marker=dict(size=20, color=dot["color"]),
            showlegend=False,
            hoverinfo="text",
            hovertext=hover_text
        ))


    for arrow in grey_to_black_arrows:
        is_highlighted = len(highlight_track_list) > 0 and arrow in dashed_arrows_to_highlight
        fig.add_shape(
            type="line",
            x0=agent_pos[arrow["start_agent"]],
            y0=arrow["task"],
            x1=agent_pos[arrow["end_agent"]],
            y1=arrow["task"],
            line=dict(
                color="crimson" if is_highlighted else "black",
                width=4 if is_highlighted else 2,
                dash="dot"
            )
        )

    for arrow in black_to_black_arrows:
        # Use the unified highlight_track_list for all strategies including category overrides
        is_highlighted = (arrow["start_task"], arrow["start_agent"]) in highlight_track_list and \
                         (arrow["end_task"], arrow["end_agent"]) in highlight_track_list

        fig.add_annotation(
            x=agent_pos[arrow["end_agent"]],
            y=arrow["end_task"],
            ax=agent_pos[arrow["start_agent"]],
            ay=arrow["start_task"],
            xref="x", yref="y", axref="x", ayref="y",
            showarrow=True,
            arrowhead=3,
            arrowsize=1,
            arrowwidth=4 if is_highlighted else 2,
            arrowcolor="crimson" if is_highlighted else "black",
            opacity=0.9
        )

    fig.update_layout(
        title="Combined Workflow Graph (All Procedures)",
        xaxis=dict(
            tickvals=list(agent_pos.values()),
            ticktext=list(agent_pos.keys()),
            title="Agent",
            showgrid=True,
            gridcolor='lightgrey'
        ),
        yaxis=dict(
            tickvals=list(df["task_idx"]),
            ticktext=[wrap_text(f"{p} | {t}") for p, t in zip(df["Procedure"], df["Task Object"])],
            title="Procedure | Task",
            autorange="reversed",
            showgrid=True,
            gridcolor='lightgrey'
        ),
        margin=dict(l=250, r=50, t=50, b=50),
        height=height,
        plot_bgcolor='white',
        paper_bgcolor='white',
    )
    return fig


# Page layouts
def interdependence_analysis_page():
    return html.Div([
        # The table goes here
        html.H2("Interdependence Table - Engine Failure at Take Off", style={"textAlign": "center"}),
        html.Div(id="table-wrapper", children=[table]),
        html.Div([
            html.Div([
                # Left-aligned buttons
                html.Div([
                    dcc.Upload(
                        id='upload-data',
                        children=html.Button('Load Table', id='load-button', n_clicks=0),
                        multiple=False,
                        style={
                            'display': 'inline-block',
                            'marginRight': '10px'
                        }
                    ),
                    html.Button("Add Row", id="add-row-button", n_clicks=0),
                    html.Button("Copy Cell Down", id="copy-down-button", n_clicks=0),
                ], style={"display": "flex", "gap": "10px"}),

                # Right-aligned Save button
                html.Div([
                    html.Button("Save Table", disabled=False, id="save-button", n_clicks=0)
                ], style={"marginLeft": "auto"})  # pushes this div to the right
            ], style={"display": "flex", "width": "100%"}),
            html.Div(id="save-confirmation", style={"marginTop": "10px", "fontStyle": "italic"})
        ]),

        html.H2("Workflow graph", style={"textAlign": "center"}),

        # Dropdown menu to select procedure
        html.Div([
            dcc.Dropdown(
                id="procedure-dropdown",
                options=[{"label": proc, "value": proc} for proc in df["Procedure"].unique()],
                value=df["Procedure"].unique()[0],
                clearable=True,
                style={"width": "50%", "margin": "0 auto"}
            )
        ]),

        html.Div([
            dcc.RadioItems(
                id="view-selector",
                options=[
                    {"label": "Full View (All Agents)", "value": "full"},
                    {"label": "Performers Only (HUMAN* & TARS*)", "value": "performers"}
                ],
                value="full",
                labelStyle={'display': 'inline-block', 'margin-right': '20px'},
                style={"textAlign": "center", "marginTop": "20px"}
            )
        ]),

        dcc.RadioItems(
        id="highlight-selector",
        options=[
            {"label": "No highlight", "value": "none"},
            {"label": "Human-performer-only no support", "value": "human_baseline"},
            {"label": "Human-performer-only full support", "value": "human_full_support"},
            {"label": "Agent-performer-whenever-possible no support", "value": "agent_whenever_possible"},
            {"label": "Agent-performer-whenever-possible full support", "value": "agent_whenever_possible_full_support"},
            {"label": "Most reliable path", "value": "most_reliable"}
        ],
        value="none",
        labelStyle={'display': 'inline-block', 'margin-right': '20px'},
        style={"textAlign": "center", "marginTop": "20px"}
        ),

        # Category overrides section
        html.Div([
            html.H4("Category Overrides", style={"textAlign": "center", "marginTop": "30px", "marginBottom": "10px"}),
            html.P("Override performer assignment for specific task categories:", 
                   style={"textAlign": "center", "color": "#666", "fontSize": "14px"}),
            html.Div(id="category-overrides-container", style={
                "display": "flex",
                "flexWrap": "wrap",
                "justifyContent": "center",
                "gap": "20px",
                "marginTop": "15px",
                "marginBottom": "20px",
                "padding": "15px",
                "backgroundColor": "#f9f9f9",
                "borderRadius": "8px"
            })
        ]),

        # Automation Proportion Summary Box
        html.Div([
            html.Div([
                html.Span("Automation Proportion: ", style={"fontSize": "16px", "fontWeight": "bold"}),
                html.Span(id="ap-summary-value", children="--", style={"fontSize": "20px", "fontWeight": "bold", "color": "#2196F3"}),
            ], style={"display": "inline-block", "marginRight": "20px"}),
            html.A("See methodology details below ↓", href="#automation-proportion-section", 
                   style={"fontSize": "12px", "color": "#666", "textDecoration": "underline"})
        ], style={
            "textAlign": "center",
            "padding": "15px",
            "backgroundColor": "#e3f2fd",
            "borderRadius": "8px",
            "marginBottom": "20px",
            "border": "1px solid #90caf9"
        }),

        # Hidden store for category override values
        dcc.Store(id="category-overrides-store", data={}),
        
        # Cache for base figure (without highlighting) to avoid rebuilding
        dcc.Store(id="base-figure-store", data=None),
        dcc.Store(id="arrow-indices-store", data=None),

        # Graph
        dcc.Graph(id="interdependence-graph", config={
            "displayModeBar": False
        }),

        # Bar chart for most reliable path color counts
        html.Div([
            dcc.Graph(id="bar-chart-whole-scenario"),
            dcc.Graph(id="most-reliable-bar-chart"),
            dcc.Graph(id="spo_baseline-bar-chart"),
            dcc.Graph(id="allocation-type-bar-chart"),
            dcc.Graph(id="agent-autonomy-bar-chart")
        ]),

        # Automation Proportion Section
        html.Div(id="automation-proportion-section", children=[
            html.H2("Automation Proportion", style={"textAlign": "center", "marginTop": "40px"}),
            
            # Citation
            html.Div([
                html.P([
                    html.B("Reference: "),
                    "Liu, Y., & Kaber, D. B. (2025). Models of automation proportion in human-in-the-loop systems and operator situation awareness responses. ",
                    html.I("Ergonomics"),
                    ", 1–14. ",
                    html.A("https://doi.org/10.1080/00140139.2025.2608273", 
                           href="https://doi.org/10.1080/00140139.2025.2608273", 
                           target="_blank")
                ], style={"fontSize": "14px", "color": "#555", "marginBottom": "20px"})
            ], style={"backgroundColor": "#f5f5f5", "padding": "15px", "borderRadius": "8px", "marginBottom": "25px"}),
            
            # Explanation
            html.Div([
                html.H4("Methodology", style={"marginBottom": "15px"}),
                dcc.Markdown('''
Let our categories be indexed as $k \\in \\{1, \\ldots, K\\}$ where $K$ is the number of categories.

Let $C_k$ be the set of basic tasks in category $k$, and $T_k = |C_k|$ be the number of tasks in that category.

**Task Weight Definition:**

For each task $t$, we define an "automation-from-human-perspective" weight $w(t)$:

| Task Allocation | Weight |
|-----------------|--------|
| **Manual** (HUMAN* performs alone) | $w(t) = 0$ |
| **Supported** (HUMAN* performs with autonomy support) | $w(t) = 0.5$ |
| **Delegated** (TARS* performs w/ or w/o human support) | $w(t) = 1$ |

**Category Score (equal weight across tasks within category):**

For each category $k$, compute the mean task weight:

$$p_k = \\frac{1}{T_k} \\sum_{t \\in C_k} w(t)$$

This produces $p_k \\in [0, 1]$ for each category.

**Overall Automation Proportion (equal-weight categories):**

$$P = \\frac{1}{K} \\sum_{k=1}^{K} p_k$$

The resulting $P \\in [0, 1]$ represents the overall automation proportion, where:
- $P = 0$ means fully manual operation
- $P = 1$ means fully delegated to automation
                ''', mathjax=True, style={"fontSize": "15px", "lineHeight": "1.8"})
            ], style={"backgroundColor": "white", "padding": "20px", "borderRadius": "8px", "border": "1px solid #ddd", "marginBottom": "25px"}),
            
            # Computation results (dynamic)
            html.Div([
                html.H4("Computation for Current Selection", style={"marginBottom": "15px"}),
                html.Div(id="automation-proportion-results")
            ], style={"backgroundColor": "white", "padding": "20px", "borderRadius": "8px", "border": "1px solid #ddd"})
            
        ], style={"maxWidth": "900px", "margin": "0 auto", "padding": "20px"}),

        # Footer with copyright
        html.Footer(
            "© Benjamin R. Berton 2025 Polytechnique Montreal",
            style={
                "textAlign": "center",
                "marginTop": "40px",
                "padding": "10px 0",
                "color": "#888",
                "fontSize": "14px"
            }
        ),

    ], style={"fontFamily": "'Roboto', 'Helvetica', 'Arial', sans-serif"})


def assumptions_page():
    return html.Div([
        html.H2("Assumptions", style={"textAlign": "center"}),
        html.Div([
            html.H3("Operational Scenario", style={"marginBottom": "20px"}),
            html.Div([
                html.H4("Takeoff with an engine failure caused by a bird strike", style={"marginTop": "30px", "marginBottom": "15px"}),
                html.Ul([
                    html.Li([html.B("Departure Airport"), ": CYUL"]),
                    html.Li([html.B("Arrival Airport"), ": CYOW"]),
                    html.Li([html.B("Time of day"), ": Day (14:00 local time)"]),
                    html.Li([html.B("Weather conditions"), ": CAVOK"]),
                    html.Li([html.B("Wind"), ": Calm, 5 knots, 160°"]),
                    html.Li([html.B("Temperature"), ": 22°C"]),
                    html.Li([html.B("Atmospheric Pressure"), ": 29.92 inHg"]),
                    html.Li([html.B("Departure runway"), ": 24R"]),
                    html.Li([html.B("Arrival Runway"), ": 14"]),
                    html.Li([html.B("Aircraft"), ": Cessna Citation Mustang Very Light Jet"]),
                    html.Li([html.B("Crew"), ": 1 pilot, 2 pax"]),
                    html.Li([html.B("Flight phase"), ": Takeoff"]),
                    html.Li([html.B("Emergency"), ": Engine failure caused by a bird strike"]),
                ], style={"fontSize": "16px", "lineHeight": "1.6"}),
            ], style={"maxWidth": "800px", "margin": "0 auto", "padding": "20px"})
        ]),
        html.Div([
            html.H3("Single Pilot", style={"marginBottom": "20px"}),
            html.Div([
                html.H4("Key Assumptions:", style={"marginTop": "30px", "marginBottom": "15px"}),
                html.Ul([
                    html.Li([html.B("Role"), ": The pilot is the captain and retains final authority and responsibility for decision making throughout the flight."]),
                    html.Li([html.B("Experience"), ": >5,000 flight hours, including >1,000 hours in type."]),
                    html.Li([html.B("Training"), ": Recent training on emergency procedures, including engine failure at takeoff scenarios."]),
                    html.Li([html.B("Health"), ": Good health, well-rested, and fit to fly."]),
                    html.Li([html.B("Support"), ": The pilot has access to TARS for assistance with monitoring and procedural guidance."]),
                ], style={"fontSize": "16px", "lineHeight": "1.6"}),
            ], style={"maxWidth": "800px", "margin": "0 auto", "padding": "20px"})
        ]),
        html.Div([
            html.H3("Autonomous System : TARS", style={"marginBottom": "20px"}),
            html.Div([
                html.H4("Key Assumptions:", style={"marginTop": "30px", "marginBottom": "15px"}),
                html.Ul([
                    html.Li([html.B("Role"), ": TARS assists the pilot on multiple tasks during normal, abnormal and emergency situation according to the briefing. But the pilot retains final authority. TARS is designed to complement human decision-making, not replace it. The pilot must remain engaged and aware of the flight situation at all times."]),
                    html.Li([html.B("Description: "), "TARS is implemented as a reactive software designed to assist the pilot in managing the flight scenario. TARS organizes its behavior into a structured hierarchy of states and substates. Each state encapsulates a specific procedural phase of the scenario, with transitions triggered by defined environmental conditions."]),
                    html.Li([html.B("Sensors"), ": TARS has access to all flight parameters available in the avionics system, including aircraft state and system status. TARS does not have access to environmental parameters outside of the avionics system."]),
                    html.Li([html.B("Actuators"), ": TARS can interact with the pilot through the interface with the pilot, for checklist support. TARS can interact with the airplane through a predefined set of commands, including the FMS and autopilot, but TARS cannot perform physical actions in the cockpit (e.g., moving flight controls, switching levers, pushing buttons)."]),
                    html.Li([html.B("Reliability"), ": TARS is assumed to operate without technical failures during the scenario. TARS is assumed to correctly interpret sensor data and execute commands without errors and under 1ms latency."]),
                    html.Li([html.B("Graphical Interface"), ": The Human-TARS interface is an EFB-like tactile application run on a tablet placed on the left-side of the single pilot in the cockpit that provides access to TARS current state and behavioral intent. The interface allows the pilot to monitor TARS actions, receive alerts, and input commands."]),
                    html.Li([html.B("Voice Interface"), ": TARS can communicate with the pilot using auditory alerts and speech synthesis. The pilot can issue commands to TARS vocally (e.g. cancel action)."]),
                ], style={"fontSize": "16px", "lineHeight": "1.6"}),
            ], style={"maxWidth": "800px", "margin": "0 auto", "padding": "20px"})
        ]),
        
        # Footer with copyright
        html.Footer(
            "© Benjamin R. Berton 2025 Polytechnique Montreal",
            style={
                "textAlign": "center",
                "marginTop": "40px",
                "padding": "10px 0",
                "color": "#888",
                "fontSize": "14px"
            }
        ),
        
    ], style={"fontFamily": "'Roboto', 'Helvetica', 'Arial', sans-serif"})


app.layout = html.Div([
    dcc.Location(id='url', refresh=False),
    html.Div(
        "© Benjamin R. Berton 2025 Polytechnique Montreal",
        style={
            "textAlign": "right",
            "color": "#888",
            "fontSize": "14px",
            "marginBottom": "10px"
        }
    ),
    html.Div(id='navbar'),
    html.Div(id='page-content')
])

# Callback to update navbar based on current pathname
@app.callback(Output('navbar', 'children'), Input('url', 'pathname'))
def update_navbar(pathname):
    return create_navbar(current_pathname=pathname)

# Callback for page routing
@app.callback(Output('page-content', 'children'), Input('url', 'pathname'))
def display_page(pathname):
    if pathname == '/assumptions':
        return assumptions_page()
    else:
        return interdependence_analysis_page()

# Callback to generate category override radio buttons dynamically
@app.callback(
    Output("category-overrides-container", "children"),
    Input("responsibility-table", "data")
)
def generate_category_overrides(data):
    if not data:
        return []
    
    df = pd.DataFrame(data)
    if "Category" not in df.columns:
        return []
    
    # Get unique categories
    categories = sorted(df["Category"].dropna().unique())
    
    # Create radio buttons for each category with mini bar charts
    category_controls = []
    for category in categories:
        # Count assignable tasks per agent for this category
        cat_df = df[df["Category"] == category]
        human_assignable = sum(
            1 for _, row in cat_df.iterrows()
            if isinstance(row.get("Human*"), str) and row["Human*"].strip().lower() in ["green", "yellow", "orange"]
        )
        tars_assignable = sum(
            1 for _, row in cat_df.iterrows()
            if isinstance(row.get("TARS*"), str) and row["TARS*"].strip().lower() in ["green", "yellow", "orange"]
        )
        
        category_controls.append(
            html.Div([
                html.Label(category, style={
                    "fontWeight": "bold",
                    "marginBottom": "5px",
                    "display": "block",
                    "fontSize": "13px"
                }),
                # Clickable bar chart for assignable tasks
                dcc.Graph(
                    id={"type": "category-bar-chart", "category": category},
                    figure=go.Figure(
                        data=[
                            go.Bar(
                                x=[tars_assignable],
                                y=["TARS"],
                                orientation='h',
                                marker_color='grey',
                                text=[tars_assignable],
                                textposition='inside',
                                textfont=dict(color='white', size=10),
                                name="TARS",
                                customdata=["TARS"]
                            ),
                            go.Bar(
                                x=[human_assignable],
                                y=["HUMAN"],
                                orientation='h',
                                marker_color='grey',
                                text=[human_assignable],
                                textposition='inside',
                                textfont=dict(color='white', size=10),
                                name="HUMAN",
                                customdata=["HUMAN"]
                            )
                        ],
                        layout=go.Layout(
                            height=60,
                            width=120,
                            margin=dict(l=45, r=5, t=5, b=5),
                            xaxis=dict(visible=False, range=[0, max(human_assignable, tars_assignable, 1) * 1.1]),
                            yaxis=dict(tickfont=dict(size=9)),
                            paper_bgcolor='rgba(0,0,0,0)',
                            plot_bgcolor='rgba(0,0,0,0)',
                            showlegend=False,
                            barmode='group',
                            autosize=False
                        )
                    ),
                    config={'displayModeBar': False},
                    style={"marginTop": "5px", "height": "60px", "width": "120px", "cursor": "pointer"}
                ),
                # Store the counts for the callback
                dcc.Store(
                    id={"type": "category-counts", "category": category},
                    data={"human": human_assignable, "tars": tars_assignable}
                ),
                # Store the current selection state (hidden radio replacement)
                dcc.Store(
                    id={"type": "category-override", "category": category},
                    data="default"
                )
            ], style={
                "backgroundColor": "white",
                "padding": "10px 15px",
                "borderRadius": "5px",
                "border": "1px solid #ddd",
                "minWidth": "140px"
            })
        )
    
    return category_controls

# Callback to handle bar click and toggle selection
@app.callback(
    Output({"type": "category-override", "category": dash.MATCH}, "data"),
    Output({"type": "category-bar-chart", "category": dash.MATCH}, "figure"),
    Input({"type": "category-bar-chart", "category": dash.MATCH}, "clickData"),
    State({"type": "category-override", "category": dash.MATCH}, "data"),
    State({"type": "category-counts", "category": dash.MATCH}, "data"),
    prevent_initial_call=True
)
def toggle_category_selection(clickData, current_selection, counts):
    if clickData is None or counts is None:
        raise dash.exceptions.PreventUpdate
    
    # Get the clicked bar's label
    clicked_label = clickData["points"][0].get("y", None)
    if clicked_label is None:
        raise dash.exceptions.PreventUpdate
    
    # Toggle logic: if clicking the same selection, deselect (back to default)
    if current_selection == clicked_label:
        new_selection = "default"
    else:
        new_selection = clicked_label
    
    human_count = counts.get("human", 0)
    tars_count = counts.get("tars", 0)
    
    # Determine bar colors based on new selection
    human_color = "dodgerblue" if new_selection == "HUMAN" else "grey"
    tars_color = "dodgerblue" if new_selection == "TARS" else "grey"
    
    fig = go.Figure(
        data=[
            go.Bar(
                x=[tars_count],
                y=["TARS"],
                orientation='h',
                marker_color=tars_color,
                text=[tars_count],
                textposition='inside',
                textfont=dict(color='white', size=10),
                name="TARS",
                customdata=["TARS"]
            ),
            go.Bar(
                x=[human_count],
                y=["HUMAN"],
                orientation='h',
                marker_color=human_color,
                text=[human_count],
                textposition='inside',
                textfont=dict(color='white', size=10),
                name="HUMAN",
                customdata=["HUMAN"]
            )
        ],
        layout=go.Layout(
            height=60,
            width=120,
            margin=dict(l=45, r=5, t=5, b=5),
            xaxis=dict(visible=False, range=[0, max(human_count, tars_count, 1) * 1.1]),
            yaxis=dict(tickfont=dict(size=9)),
            paper_bgcolor='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            showlegend=False,
            barmode='group',
            autosize=False
        )
    )
    
    return new_selection, fig

# Callback to collect all category overrides into a single store
@app.callback(
    Output("category-overrides-store", "data"),
    Input({"type": "category-override", "category": dash.ALL}, "data"),
    State({"type": "category-override", "category": dash.ALL}, "id"),
    prevent_initial_call=True
)
def collect_category_overrides(values, ids):
    if not values or not ids:
        return {}
    
    overrides = {}
    for id_dict, value in zip(ids, values):
        if value != "default":
            overrides[id_dict["category"]] = value
    
    return overrides

@app.callback(
    Output("base-figure-store", "data"),
    Output("arrow-indices-store", "data"),
    Input("procedure-dropdown", "value"),
    Input("view-selector", "value"),
    State("responsibility-table", "data")
)
def build_base_figure(procedure, view_mode, data):
    """Build the base figure structure (without highlighting). Only runs when procedure/view changes."""
    df = pd.DataFrame(data)
    
    if procedure is None:
        # Combined view
        if view_mode == "performers":
            fig, arrow_info = build_performers_only_combined_figure_base(df)
        else:
            fig, arrow_info = build_combined_interdependence_figure_base(df)
    else:
        # Single procedure view
        if view_mode == "performers":
            fig, arrow_info = build_performers_only_figure_base(df, procedure)
        else:
            fig, arrow_info = build_interdependence_figure_base(df, procedure)
    
    return fig.to_dict(), arrow_info


@app.callback(
    Output("interdependence-graph", "figure"),
    Input("base-figure-store", "data"),
    Input("arrow-indices-store", "data"),
    Input("highlight-selector", "value"),
    Input("category-overrides-store", "data"),
    Input("procedure-dropdown", "value"),
    State("responsibility-table", "data")
)
def apply_highlighting(base_fig_dict, arrow_info, highlight_track, category_overrides, procedure, data):
    """Apply highlighting to the cached base figure. Fast operation - just updates colors/widths."""
    if base_fig_dict is None:
        return go.Figure()
    
    fig = go.Figure(base_fig_dict)
    
    if arrow_info is None or highlight_track == "none":
        # Still need to check for category overrides even when highlight_track is "none"
        if not category_overrides:
            return fig
    
    df = pd.DataFrame(data)
    if category_overrides is None:
        category_overrides = {}
    
    # Build the highlight track list based on current settings
    if procedure is not None:
        df_filtered = df[df["Procedure"] == procedure].reset_index(drop=True)
    else:
        df_filtered = df.reset_index(drop=True)
    
    df_filtered["task_idx"] = df_filtered.index
    
    highlight_track_list = []
    for idx, row in df_filtered.iterrows():
        task_idx = row["task_idx"]
        agent_colors = {
            "HUMAN*": row["Human*"].strip().lower() if isinstance(row["Human*"], str) else "",
            "TARS*": row["TARS*"].strip().lower() if isinstance(row["TARS*"], str) else "",
        }
        chosen_agent = get_chosen_agent_with_override(row, highlight_track, category_overrides, agent_colors)
        if chosen_agent:
            highlight_track_list.append((task_idx, chosen_agent))
    
    # Determine if support arrows should be highlighted based on highlight mode
    # "full_support" modes highlight support arrows, "no support" modes don't
    should_highlight_support = highlight_track in ["human_full_support", "agent_whenever_possible_full_support", "most_reliable"]
    
    # Apply highlighting to shapes (dashed arrows - support relationships)
    if "shapes" in fig.layout and fig.layout.shapes:
        shapes = list(fig.layout.shapes)
        for i, shape_info in enumerate(arrow_info.get("dashed_arrows", [])):
            if i < len(shapes):
                task_idx = shape_info["task"]
                end_agent = shape_info["end_agent"]
                
                # Dashed arrows should only be highlighted if:
                # 1. We're in a "full_support" mode AND
                # 2. The performer at this task matches the highlighted path
                is_highlighted = False
                if should_highlight_support and (task_idx, end_agent) in highlight_track_list:
                    is_highlighted = True
                
                shapes[i].line.color = "crimson" if is_highlighted else "black"
                shapes[i].line.width = 4 if is_highlighted else 2
        fig.layout.shapes = shapes
    
    # Apply highlighting to annotations (black-to-black arrows - performer transitions)
    if "annotations" in fig.layout and fig.layout.annotations:
        annotations = list(fig.layout.annotations)
        for i, arrow_data in enumerate(arrow_info.get("black_to_black_arrows", [])):
            if i < len(annotations):
                start_task = arrow_data["start_task"]
                start_agent = arrow_data["start_agent"]
                end_task = arrow_data["end_task"]
                end_agent = arrow_data["end_agent"]
                
                # Check if this arrow is part of the highlighted path
                is_highlighted = (start_task, start_agent) in highlight_track_list and \
                                 (end_task, end_agent) in highlight_track_list
                
                annotations[i].arrowcolor = "crimson" if is_highlighted else "black"
                annotations[i].arrowwidth = 4 if is_highlighted else 2
        fig.layout.annotations = annotations
    
    return fig


@app.callback(
    Output("bar-chart-whole-scenario", "figure"),
    Output("most-reliable-bar-chart", "figure"),
    Output("spo_baseline-bar-chart", "figure"),
    Output("allocation-type-bar-chart", "figure"),
    Output("agent-autonomy-bar-chart", "figure"),
    Input("procedure-dropdown", "value"),
    State("responsibility-table", "data")
)
def update_bar_charts(procedure, data):
    """Update bar charts - only when procedure changes, not on highlight changes."""
    df = pd.DataFrame(data)
    
    if procedure is None:
        df_bar = df
    else:
        df_bar = df[df["Procedure"] == procedure]

# --- Bar Chart for the whole scenario---
    performer_green = 0
    performer_yellow = 0
    performer_orange = 0
    supporter_green = 0
    supporter_yellow = 0
    supporter_orange = 0
    human_performer_green = 0
    human_performer_yellow = 0
    human_performer_orange = 0
    human_supporter_green = 0
    human_supporter_yellow = 0
    human_supporter_orange = 0
    tars_performer_green = 0
    tars_performer_yellow = 0
    tars_performer_orange = 0
    tars_supporter_green = 0
    tars_supporter_yellow = 0
    tars_supporter_orange = 0
    for idx, row in df_bar.iterrows():
        agent_colors = {
            "HUMAN*": str(row.get("Human*", "") or "").strip().lower(),
            "TARS*": str(row.get("TARS*", "") or "").strip().lower(),
        }
        supporter_colors = {
            "HUMAN": str(row.get("Human", "") or "").strip().lower(),
            "TARS": str(row.get("TARS", "") or "").strip().lower(),
        }
        # Find performer (most reliable)
        for agent in ["HUMAN*", "TARS*"]:
            if agent_colors[agent] == "green":
                if agent ==  "HUMAN*":
                    human_performer_green += 1
                if agent == "TARS*":
                    tars_performer_green += 1
                performer_green += 1
            if agent_colors[agent] == "yellow":
                if agent ==  "HUMAN*":
                    human_performer_yellow += 1
                if agent == "TARS*":
                    tars_performer_yellow += 1
                performer_yellow += 1
            if agent_colors[agent] == "orange":
                if agent ==  "HUMAN*":
                    human_performer_orange += 1
                if agent == "TARS*":
                    tars_performer_orange += 1
                performer_orange += 1
        for agent in ["HUMAN", "TARS"]:
            if supporter_colors[agent] == "green":
                if agent ==  "HUMAN":
                    human_supporter_green += 1
                if agent == "TARS":
                    tars_supporter_green += 1
                supporter_green += 1
            if supporter_colors[agent] == "yellow":
                if agent ==  "HUMAN":
                    human_supporter_yellow += 1
                if agent == "TARS":
                    tars_supporter_yellow += 1
                supporter_yellow += 1
            if supporter_colors[agent] == "orange":
                if agent ==  "HUMAN":
                    human_supporter_orange += 1
                if agent == "TARS":
                    tars_supporter_orange += 1
                supporter_orange += 1

    bar_fig_whole_scenario = go.Figure()
    # Stacked bars for performer colors
    bar_fig_whole_scenario.add_trace(go.Bar(
        name="Human Performer",
        x=["Performer Green", "Performer Yellow", "Performer Orange"],
        y=[human_performer_green, human_performer_yellow, human_performer_orange],
        marker_color=["seagreen", "gold", "darkorange"]
    ))
    bar_fig_whole_scenario.add_trace(go.Bar(
        name="TARS Performer",
        x=["Performer Green", "Performer Yellow", "Performer Orange"],
        y=[tars_performer_green, tars_performer_yellow, tars_performer_orange],
        marker_color=["limegreen", "khaki", "orange"]
    ))
    # Stacked bars for supporter colors
    bar_fig_whole_scenario.add_trace(go.Bar(
        name="Human Supporter",
        x=["Supporter Green", "Supporter Yellow", "Supporter Orange"],
        y=[human_supporter_green, human_supporter_yellow, human_supporter_orange],
        marker_color=["seagreen", "gold", "darkorange"]
    ))
    bar_fig_whole_scenario.add_trace(go.Bar(
        name="TARS Supporter",
        x=["Supporter Green", "Supporter Yellow", "Supporter Orange"],
        y=[tars_supporter_green, tars_supporter_yellow, tars_supporter_orange],
        marker_color=["limegreen", "khaki", "orange"]
    ))
    bar_fig_whole_scenario.update_layout(
        title="Performer and Supporter Capacities in Mixed Initiative",
        xaxis_title="Role and Capacity",
        yaxis_title="Number of Tasks",
        barmode='stack',
        bargap=0.3,
        plot_bgcolor='white',
        paper_bgcolor='white',
        showlegend=False
    )

    # --- Bar Chart for Most Reliable Path ---
    performer_green = 0
    performer_yellow = 0
    performer_orange = 0
    supporter_green = 0
    supporter_yellow = 0
    supporter_orange = 0
    human_performer_green = 0
    human_performer_yellow = 0
    human_performer_orange = 0
    human_supporter_green = 0
    human_supporter_yellow = 0
    human_supporter_orange = 0
    tars_performer_green = 0
    tars_performer_yellow = 0
    tars_performer_orange = 0
    tars_supporter_green = 0
    tars_supporter_yellow = 0
    tars_supporter_orange = 0
    for idx, row in df_bar.iterrows():
        agent_colors = {
            "HUMAN*": str(row.get("Human*", "") or "").strip().lower(),
            "TARS*": str(row.get("TARS*", "") or "").strip().lower(),
        }
        supporter_colors = {
            "HUMAN": str(row.get("Human", "") or "").strip().lower(),
            "TARS": str(row.get("TARS", "") or "").strip().lower(),
        }
        # Find performer (most reliable)
        performer = None
        for agent in ["HUMAN*", "TARS*"]:
            if agent_colors[agent] == "green":
                performer = agent
                performer_color = "green"
                break
        if not performer:
            for agent in ["HUMAN*", "TARS*"]:
                if agent_colors[agent] == "yellow":
                    performer = agent
                    performer_color = "yellow"
                    break
        if not performer:
            for agent in ["HUMAN*", "TARS*"]:
                if agent_colors[agent] == "orange":
                    performer = agent
                    performer_color = "orange"
                    break
        # Count performer color
        if performer:
            if performer_color == "green":
                performer_green += 1
                if performer == "HUMAN*":
                    human_performer_green += 1
                else:
                    tars_performer_green += 1
            elif performer_color == "yellow":
                performer_yellow += 1
                if performer == "HUMAN*":
                    human_performer_yellow += 1
                else:
                    tars_performer_yellow += 1
            elif performer_color == "orange":
                performer_orange += 1
                if performer == "HUMAN*":
                    human_performer_orange += 1
                else:
                    tars_performer_orange += 1
            # Now check for supporter (the other agent)
            supporter = "TARS" if performer == "HUMAN*" else "HUMAN"
            supporter_color = supporter_colors[supporter]
            if supporter_color == "green":
                supporter_green += 1
                if supporter == "HUMAN":
                    human_supporter_green += 1
                else:
                    tars_supporter_green += 1
            elif supporter_color == "yellow":
                supporter_yellow += 1
                if supporter == "HUMAN":
                    human_supporter_yellow += 1
                else:
                    tars_supporter_yellow += 1
            elif supporter_color == "orange":
                supporter_orange += 1
                if supporter == "HUMAN":
                    human_supporter_orange += 1
                else:
                    tars_supporter_orange += 1

    bar_fig = go.Figure()
    # Stacked bars for performer colors
    bar_fig.add_trace(go.Bar(
        name="Human Performer",
        x=["Performer Green", "Performer Yellow", "Performer Orange"],
        y=[human_performer_green, human_performer_yellow, human_performer_orange],
        marker_color=["seagreen", "gold", "darkorange"]
    ))
    bar_fig.add_trace(go.Bar(
        name="TARS Performer",
        x=["Performer Green", "Performer Yellow", "Performer Orange"],
        y=[tars_performer_green, tars_performer_yellow, tars_performer_orange],
        marker_color=["limegreen", "khaki", "orange"]
    ))
    # Stacked bars for supporter colors
    bar_fig.add_trace(go.Bar(
        name="Human Supporter",
        x=["Supporter Green", "Supporter Yellow", "Supporter Orange"],
        y=[human_supporter_green, human_supporter_yellow, human_supporter_orange],
        marker_color=["seagreen", "gold", "darkorange"]
    ))
    bar_fig.add_trace(go.Bar(
        name="TARS Supporter",
        x=["Supporter Green", "Supporter Yellow", "Supporter Orange"],
        y=[tars_supporter_green, tars_supporter_yellow, tars_supporter_orange],
        marker_color=["limegreen", "khaki", "orange"]
    ))
    bar_fig.update_layout(
        title="Most Reliable Path: Performer and Supporter Capacities",
        xaxis_title="Role and Capacity",
        yaxis_title="Number of Tasks",
        barmode='stack',
        bargap=0.3,
        plot_bgcolor='white',
        paper_bgcolor='white',
        showlegend=False
    )

    # --- Bar Chart for spo baseline Path ---
    performer_green = 0
    performer_yellow = 0
    performer_orange = 0
    for idx, row in df_bar.iterrows():
        agent_colors = {
            "HUMAN*": str(row.get("Human*", "") or "").strip().lower(),
        }
        performer = "HUMAN*"
        if performer:
            if agent_colors[performer] == "green":
                performer_green += 1
            elif agent_colors[performer] == "yellow":
                performer_yellow += 1
            elif agent_colors[performer] == "orange":
                performer_orange += 1
            elif agent_colors[performer] == "orange":
                performer_orange += 1

    bar_fig_spo = go.Figure()
    # Stacked bars for performer colors
    bar_fig_spo.add_trace(go.Bar(
        name="Performer Green",
        x=["Performer Green"],
        y=[performer_green],
        marker_color="seagreen"
    ))
    bar_fig_spo.add_trace(go.Bar(
        name="Performer Yellow",
        x=["Performer Yellow"],
        y=[performer_yellow],
        marker_color="gold"
    ))
    bar_fig_spo.add_trace(go.Bar(
        name="Performer Orange",
        x=["Performer Orange"],
        y=[performer_orange],
        marker_color="darkorange"
    ))
    bar_fig_spo.update_layout(
        title="Human-only no support Path: Human pilot capacities",
        xaxis_title="Role and Capacity",
        yaxis_title="Number of Tasks",
        bargap=0.3,
        plot_bgcolor='white',
        paper_bgcolor='white',
        showlegend=False
    )

    # --- Allocation Type Analysis ---
    single_allocation_independent = 0
    multiple_allocation_independent = 0
    interdependent = 0

    for idx, row in df_bar.iterrows():
        # Get all performer and supporter values
        human_star = str(row.get("Human*", "") or "").strip().lower()
        tars_star = str(row.get("TARS*", "") or "").strip().lower()
        human = str(row.get("Human", "") or "").strip().lower()
        tars = str(row.get("TARS", "") or "").strip().lower()

        VALID_COLORS = {"red", "yellow", "green", "orange"}
        
        # Count valid performers (not red)
        performers = []
        if human_star in VALID_COLORS and human_star != "red":
            performers.append("Human*")
        if tars_star in VALID_COLORS and tars_star != "red":
            performers.append("TARS*")
        
        # Count valid supporters (not red)
        supporters = []
        if human in VALID_COLORS and human != "red":
            supporters.append("Human")
        if tars in VALID_COLORS and tars != "red":
            supporters.append("TARS")
        
        # Determine task type
        if len(supporters) > 0:
            # Has support available = interdependent
            interdependent += 1
        elif len(performers) == 1:
            # Only one performer, no support = single allocation independent
            single_allocation_independent += 1
        elif len(performers) > 1:
            # Multiple performers, no support = multiple allocation independent
            multiple_allocation_independent += 1
    
    total_tasks = len(df_bar)
    
    # Create horizontal stacked bar chart
    allocation_fig = go.Figure()
    
    allocation_fig.add_trace(go.Bar(
        name='Single Allocation Independent',
        y=['Task Allocation Types'],
        x=[single_allocation_independent],
        orientation='h',
        marker=dict(color='lightcoral'),
        text=[f'Single Allocation Independent: {single_allocation_independent} ({single_allocation_independent/total_tasks*100:.1f}%)' if total_tasks > 0 and single_allocation_independent > 0 else ''],
        textposition='inside',
        textfont=dict(color='black', size=12),
        hovertemplate='Single Allocation Independent<br>Count: %{x}<br>Percentage: ' + (f'{single_allocation_independent/total_tasks*100:.1f}%' if total_tasks > 0 else '0%') + '<extra></extra>'
    ))
    
    allocation_fig.add_trace(go.Bar(
        name='Multiple Allocation Independent',
        y=['Task Allocation Types'],
        x=[multiple_allocation_independent],
        orientation='h',
        marker=dict(color='lightskyblue'),
        text=[f'Multiple Allocation Independent: {multiple_allocation_independent} ({multiple_allocation_independent/total_tasks*100:.1f}%)' if total_tasks > 0 and multiple_allocation_independent > 0 else ''],
        textposition='inside',
        textfont=dict(color='black', size=12),
        hovertemplate='Multiple Allocation Independent<br>Count: %{x}<br>Percentage: ' + (f'{multiple_allocation_independent/total_tasks*100:.1f}%' if total_tasks > 0 else '0%') + '<extra></extra>'
    ))
    
    allocation_fig.add_trace(go.Bar(
        name='Interdependent (Support Available)',
        y=['Task Allocation Types'],
        x=[interdependent],
        orientation='h',
        marker=dict(color='lightgreen'),
        text=[f'Interdependent: {interdependent} ({interdependent/total_tasks*100:.1f}%)' if total_tasks > 0 and interdependent > 0 else ''],
        textposition='inside',
        textfont=dict(color='black', size=12),
        hovertemplate='Interdependent (Support Available)<br>Count: %{x}<br>Percentage: ' + (f'{interdependent/total_tasks*100:.1f}%' if total_tasks > 0 else '0%') + '<extra></extra>'
    ))
    
    allocation_fig.update_layout(
        title="Task Type Distribution",
        xaxis_title="Number of Tasks",
        barmode='stack',
        height=200,
        plot_bgcolor='white',
        paper_bgcolor='white',
        showlegend=False,
        margin=dict(l=50, r=50, t=50, b=50)
    )

    # --- Agent Autonomy Analysis ---
    # Track tasks by agent and autonomy across the entire scenario
    agent_autonomy = {
        "Human*": {"autonomous": 0, "non_autonomous": 0},
        "TARS*": {"autonomous": 0, "non_autonomous": 0}
    }
    
    # Process all tasks in sequence (not grouped by procedure)
    prev_performers = []
    
    for idx, row in df_bar.iterrows():
        # Skip the first task (task 0) as there's no previous task to compare
        if idx == 0:
            # Get current task performers for next iteration
            human_star = str(row.get("Human*", "") or "").strip().lower()
            tars_star = str(row.get("TARS*", "") or "").strip().lower()
            
            VALID_COLORS = {"red", "yellow", "green", "orange"}
            
            current_performers = []
            if human_star in VALID_COLORS and human_star != "red":
                current_performers.append("Human*")
            if tars_star in VALID_COLORS and tars_star != "red":
                current_performers.append("TARS*")
            
            prev_performers = current_performers
            continue
        
        # Get current task performers
        human_star = str(row.get("Human*", "") or "").strip().lower()
        tars_star = str(row.get("TARS*", "") or "").strip().lower()
        
        VALID_COLORS = {"red", "yellow", "green", "orange"}
        
        # Map agents to their colors for this task
        agent_colors = {
            "Human*": human_star,
            "TARS*": tars_star
        }
        
        current_performers = []
        if human_star in VALID_COLORS and human_star != "red":
            current_performers.append("Human*")
        if tars_star in VALID_COLORS and tars_star != "red":
            current_performers.append("TARS*")
        
        # For each agent that can perform this task
        for agent in current_performers:
            # Orange tasks are always non-autonomous
            if agent_colors[agent] == "orange":
                agent_autonomy[agent]["non_autonomous"] += 1
            elif agent not in prev_performers:
                # Agent couldn't perform previous task
                agent_autonomy[agent]["non_autonomous"] += 1
            else:
                # Agent could also perform previous task = autonomous (green or yellow only)
                agent_autonomy[agent]["autonomous"] += 1
        
        prev_performers = current_performers
    
    # Create horizontal stacked bar chart for agent autonomy
    autonomy_fig = go.Figure()
    
    agents = ["Human*", "TARS*"]
    colors_autonomous = {"Human*": "lightgreen", "TARS*": "lightgreen"}
    colors_non_autonomous = {"Human*": "lightcoral", "TARS*": "lightcoral"}
    
    for agent in agents:
        autonomous = agent_autonomy[agent]["autonomous"]
        non_autonomous = agent_autonomy[agent]["non_autonomous"]
        total = autonomous + non_autonomous
        
        # Non-autonomous tasks (first section)
        autonomy_fig.add_trace(go.Bar(
            name=f'{agent} Non-Autonomous',
            y=[agent],
            x=[non_autonomous],
            orientation='h',
            marker=dict(color=colors_non_autonomous[agent]),
            text=[f'Non-Autonomous: {non_autonomous} ({non_autonomous/total*100:.1f}%)' if total > 0 and non_autonomous > 0 else ''],
            textposition='inside',
            textfont=dict(color='black', size=11),
            hovertemplate=f'{agent}<br>Non-Autonomous Tasks: {non_autonomous}<br>Percentage: ' + (f'{non_autonomous/total*100:.1f}%' if total > 0 else '0%') + '<extra></extra>',
            showlegend=False
        ))
        
        # Autonomous tasks (second section)
        autonomy_fig.add_trace(go.Bar(
            name=f'{agent} Autonomous',
            y=[agent],
            x=[autonomous],
            orientation='h',
            marker=dict(color=colors_autonomous[agent]),
            text=[f'Autonomous: {autonomous} ({autonomous/total*100:.1f}%)' if total > 0 and autonomous > 0 else ''],
            textposition='inside',
            textfont=dict(color='black', size=11),
            hovertemplate=f'{agent}<br>Autonomous Tasks: {autonomous}<br>Percentage: ' + (f'{autonomous/total*100:.1f}%' if total > 0 else '0%') + '<extra></extra>',
            showlegend=False
        ))
    
    autonomy_fig.update_layout(
        title="Agent Autonomy: Task Continuity Across Entire Scenario",
        xaxis_title="Number of Tasks",
        yaxis_title="Agent",
        barmode='stack',
        height=300,
        plot_bgcolor='white',
        paper_bgcolor='white',
        showlegend=False,
        margin=dict(l=100, r=50, t=50, b=50)
    )

    return bar_fig_whole_scenario, bar_fig, bar_fig_spo, allocation_fig, autonomy_fig


@app.callback(
    Output("table-wrapper", "children"),
    Output("save-confirmation", "children"),
    Input("responsibility-table", "data"),
    Input("save-button", "n_clicks"),
    Input("upload-data", "contents"),
    Input("add-row-button", "n_clicks"),
    Input("copy-down-button", "n_clicks"),
    State("responsibility-table", "active_cell"),
    State("upload-data", "filename"),
    prevent_initial_call=True
)
def handle_table(data, save_clicks, upload_contents, add_row_clicks, copy_clicks, active_cell, upload_filename):
    ctx = callback_context
    triggered = ctx.triggered[0]["prop_id"].split(".")[0]

    save_message = ""

    # Case 1: Save button clicked
    if triggered == "save-button":
        df = pd.DataFrame(data)
        # Don't recreate Row column if it already exists
        df.to_csv(DATA_FILE, index=False)
        save_message = f"✅ Table saved to {DATA_FILE}"

    # Case 2: File uploaded
    elif triggered == "upload-data" and upload_contents is not None:
        content_type, content_string = upload_contents.split(',')
        decoded = base64.b64decode(content_string)
        try:
            df = pd.read_csv(io.StringIO(decoded.decode('utf-8')))
            save_message = f"✅ Loaded {upload_filename}"
        except Exception as e:
            return dash.no_update, f"⚠️ Error loading file: {str(e)}"

    # Case 3: Add Row button clicked
    elif triggered == "add-row-button":
        df = pd.DataFrame(data)
        new_row = {col: "" for col in df.columns}
        df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)

    # Case 4: Copy Down clicked
    elif triggered == "copy-down-button":
        df = pd.DataFrame(data)
        if active_cell and "row" in active_cell and "column_id" in active_cell:
            row = active_cell["row"]
            col = active_cell["column_id"]
            if row is not None and col is not None and row + 1 < len(df):
                df.at[row + 1, col] = df.at[row, col]

    # Case 5: Table edited directly
    else:
        df = pd.DataFrame(data)

    # Ensure Row column is properly maintained (no need to reassign if it exists)
    if 'Row' not in df.columns:
        df = df.assign(Row=lambda x: x.index + 1)

    updated_table = dash_table.DataTable(
        id='responsibility-table',
        columns=columns,
        data=df.to_dict("records"),
        editable=True,
        dropdown=dropdowns,
        style_data_conditional=style_table(df),
        style_cell={'textAlign': 'left', 'padding': '5px', 'whiteSpace': 'normal'},
        style_cell_conditional=[
            {
                'if': {'column_id': 'Human*'},
                'borderLeft': '3px solid black'
            },
            {
                'if': {'column_id': 'Human'},
                'borderRight': '3px solid black'
            },
            {
                'if': {'column_id': 'TARS'},
                'borderRight': '2px solid black'
            }
        ],
        style_header={'fontWeight': 'bold', 'backgroundColor': '#f0f0f0'},
        style_table={'overflowX': 'auto', 'border': '1px solid lightgrey'},
        row_deletable=True,
    )
    return updated_table, save_message


# Callback to compute Automation Proportion
@app.callback(
    Output("automation-proportion-results", "children"),
    Output("ap-summary-value", "children"),
    Output("ap-summary-value", "style"),
    Input("highlight-selector", "value"),
    Input("category-overrides-store", "data"),
    State("responsibility-table", "data")
)
def compute_automation_proportion(highlight_track, category_overrides, data):
    default_style = {"fontSize": "20px", "fontWeight": "bold", "color": "#888"}
    
    if not data or highlight_track == "none":
        return (
            html.P("Select a highlight track to compute the Automation Proportion.", 
                   style={"color": "#888", "fontStyle": "italic"}),
            "--",
            default_style
        )
    
    if category_overrides is None:
        category_overrides = {}
    
    df = pd.DataFrame(data)
    
    if "Category" not in df.columns:
        return html.P("Category column not found in data.", style={"color": "red"})
    
    categories = sorted(df["Category"].dropna().unique())
    K = len(categories)
    
    if K == 0:
        return (html.P("No categories found.", style={"color": "red"}), "--", default_style)
    
    category_results = []
    category_scores = []
    
    for category in categories:
        cat_df = df[df["Category"] == category]
        Tk = len(cat_df)
        
        if Tk == 0:
            continue
        
        weights = []
        task_details = []
        
        for _, row in cat_df.iterrows():
            task_name = row.get("Task Object", "Unknown")
            
            # Get agent colors
            agent_colors = {
                "HUMAN*": row["Human*"].strip().lower() if isinstance(row.get("Human*"), str) else "",
                "TARS*": row["TARS*"].strip().lower() if isinstance(row.get("TARS*"), str) else "",
            }
            
            # Determine chosen agent using the same logic as highlighting
            chosen_agent = get_chosen_agent_with_override(row, highlight_track, category_overrides, agent_colors)
            
            # Determine if there's TARS support for HUMAN* performer
            # Only applies to human-focused modes (human_full_support)
            # In agent-focused modes, the support direction is HUMAN→TARS*, which doesn't affect automation level
            tars_supporter_color = row["TARS"].strip().lower() if isinstance(row.get("TARS"), str) else ""
            is_human_focused_support = highlight_track == "human_full_support"
            has_tars_support = is_human_focused_support and chosen_agent == "HUMAN*" and tars_supporter_color in ["green", "yellow", "orange"]
            
            # Calculate weight
            if chosen_agent == "TARS*":
                w = 1.0  # Delegated (same regardless of HUMAN support)
                allocation = "Delegated"
            elif chosen_agent == "HUMAN*" and has_tars_support:
                w = 0.5  # Supported (only in human_full_support mode)
                allocation = "Supported"
            elif chosen_agent == "HUMAN*":
                w = 0.0  # Manual
                allocation = "Manual"
            else:
                w = 0.0  # Default to manual if no performer
                allocation = "Unassigned"
            
            weights.append(w)
            task_details.append({"task": task_name, "weight": w, "allocation": allocation, "performer": chosen_agent or "None"})
        
        # Category score
        pk = sum(weights) / Tk if Tk > 0 else 0
        category_scores.append(pk)
        
        category_results.append({
            "category": category,
            "Tk": Tk,
            "pk": pk,
            "tasks": task_details
        })
    
    # Overall Automation Proportion
    P = sum(category_scores) / K if K > 0 else 0
    
    # Build the display
    highlight_label = {
        "human_baseline": "Human-performer-only (no support)",
        "human_full_support": "Human-performer-only (full support)",
        "agent_whenever_possible": "Agent-whenever-possible (no support)",
        "agent_whenever_possible_full_support": "Agent-whenever-possible (full support)",
        "most_reliable": "Most reliable path"
    }.get(highlight_track, highlight_track)
    
    # Category breakdown table
    category_table_rows = []
    for result in category_results:
        category_table_rows.append(
            html.Tr([
                html.Td(result["category"], style={"padding": "8px", "borderBottom": "1px solid #ddd"}),
                html.Td(str(result["Tk"]), style={"padding": "8px", "borderBottom": "1px solid #ddd", "textAlign": "center"}),
                html.Td(f"{result['pk']:.3f}", style={"padding": "8px", "borderBottom": "1px solid #ddd", "textAlign": "center"})
            ])
        )
    
    detailed_results = html.Div([
        html.P([html.B("Selected Track: "), highlight_label], style={"marginBottom": "15px"}),
        
        # Override info if any
        html.Div([
            html.P([html.B("Category Overrides Applied: "), 
                   ", ".join([f"{k}→{v}" for k, v in category_overrides.items()]) if category_overrides else "None"],
                  style={"marginBottom": "15px", "color": "#666"})
        ]) if category_overrides else None,
        
        # Category scores table
        html.Table([
            html.Thead(html.Tr([
                html.Th("Category", style={"padding": "10px", "borderBottom": "2px solid #333", "textAlign": "left"}),
                html.Th("Tasks (Tₖ)", style={"padding": "10px", "borderBottom": "2px solid #333", "textAlign": "center"}),
                html.Th("Score (pₖ)", style={"padding": "10px", "borderBottom": "2px solid #333", "textAlign": "center"})
            ])),
            html.Tbody(category_table_rows)
        ], style={"width": "100%", "borderCollapse": "collapse", "marginBottom": "20px"}),
        
        # Final result
        html.Div([
            dcc.Markdown(f'''
**Computation:**

$$P = \\frac{{1}}{{{K}}} \\sum_{{k=1}}^{{{K}}} p_k = \\frac{{1}}{{{K}}} \\times {sum(category_scores):.3f} = \\mathbf{{{P:.3f}}}$$
            ''', mathjax=True),
            html.Div([
                html.Span(f"Automation Proportion: ", style={"fontSize": "18px"}),
                html.Span(f"{P:.1%}", style={"fontSize": "24px", "fontWeight": "bold", 
                                              "color": "#2196F3" if P > 0.5 else "#4CAF50" if P < 0.5 else "#FF9800"})
            ], style={"textAlign": "center", "padding": "15px", "backgroundColor": "#f9f9f9", "borderRadius": "8px", "marginTop": "15px"})
        ])
    ])
    
    # Summary value and style for the top box
    summary_color = "#2196F3" if P > 0.5 else "#4CAF50" if P < 0.5 else "#FF9800"
    summary_style = {"fontSize": "20px", "fontWeight": "bold", "color": summary_color}
    
    return detailed_results, f"{P:.1%}", summary_style


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8050, debug=False)