"""
Table formatting functions for the Mangrove ER Dashboard.
"""
# Imports will be updated as needed when moving functions from app.py 
import pandas as pd
import numpy as np
from er_model_core.er_model import ERModel
from pathlib import Path
import yaml

# Helper for pretty table titles
def pretty_table_title(title):
    return f"<span style='font-size:1.3em; font-weight:bold'>{title}</span>"

# Table: Biomass Table (was debug table)
def create_biomass_table(config):
    """
    Returns a DataFrame with years as rows and columns for each species:
    - Surviving Trees
    - DBH (cm)
    - Height (m)
    - Biomass per Tree (kg)
    - Total Biomass (kg)
    Uses model.species_metrics for all values.
    """
    import tempfile
    with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as tmp:
        yaml.dump(config, tmp)
        tmp_path = tmp.name
    model = ERModel(Path(tmp_path))
    model.run()
    df = model.species_metrics.copy()
    # Pivot to multi-index columns: (Species, Metric)
    df = df.pivot(index="Year", columns="Species")
    # Flatten columns
    SPECIES_DISPLAY_NAMES = {
        'species_A': 'Rhizophora spp.',
        'species_B': 'Avicennia germinans'
    }
    df.columns = [f"{SPECIES_DISPLAY_NAMES.get(sp, sp)}, {metric}" for metric, sp in df.columns]
    # Format numbers
    for col in df.columns:
        if "Surviving Trees" in col:
            df[col] = df[col].apply(lambda x: f"{x:,.0f}")
        elif "Biomass" in col or "DBH" in col or "Height" in col:
            df[col] = df[col].apply(lambda x: f"{x:,.2f}")
    return df

# Table: Surviving Trees Table
def create_survival_table(model):
    """
    Returns a DataFrame with years as rows and columns for each species and total surviving trees.
    Uses model.species_metrics for all values.
    """
    SPECIES_DISPLAY_NAMES = {
        'species_A': 'Rhizophora spp.',
        'species_B': 'Avicennia germinans'
    }
    df = model.species_metrics.copy()
    # Pivot to wide format: years as rows, species as columns
    surv = df.pivot(index="Year", columns="Species", values="Surviving Trees")
    # Rename columns to display names
    surv.columns = [SPECIES_DISPLAY_NAMES.get(sp, sp) for sp in surv.columns]
    # Add total surviving trees column
    surv["Total Surviving Trees"] = surv.sum(axis=1)
    # Format numbers
    for col in surv.columns:
        surv[col] = surv[col].apply(lambda x: f"{x:,.0f}")
    surv = surv.reset_index()
    return surv

def create_project_results_table(results: pd.DataFrame) -> pd.DataFrame:
    df = results.copy()
    # No need to cumsum soil_carbon; model now provides cumulative values
    # Optionally format numbers for display
    for col in df.columns:
        if df[col].dtype in [float, int]:
            df[col] = df[col].apply(lambda x: f"{x:,.2f}" if isinstance(x, float) else f"{x:,}")
    return df