import os
import tempfile
import pandas as pd
import plotly.express as px
from typing import Union, Tuple

def _save_fig(fig, prefix: str, output_dir: str) -> str:
    """
    Save a Plotly figure as a PNG to a temp file and return its path.
    """
    os.makedirs(output_dir, exist_ok=True)
    tmp = tempfile.NamedTemporaryFile(suffix='.png', prefix=prefix, dir=output_dir, delete=False)
    path = tmp.name
    tmp.close()
    try:
        fig.write_image(path, scale=2)
    except Exception as e:
        raise
    return path


def histogram_tool(
    file_path: str,
    column: str,
    output_dir: str = '/tmp',
    bins: int = 30
) -> Union[Tuple[px.Figure, str], str]:
    """
    Build a histogram for a numeric column, return a Plotly Figure and PNG path,
    or an error string starting with '❌'.
    """
    # Load data
    ext = os.path.splitext(file_path)[1].lower()
    try:
        df = pd.read_excel(file_path) if ext in ('.xls', '.xlsx') else pd.read_csv(file_path)
    except Exception as exc:
        return f"❌ Failed to load file: {exc}"

    # Validate column
    if column not in df.columns:
        return f"❌ Column '{column}' not found."

    # Coerce to numeric
    df[column] = pd.to_numeric(df[column], errors='coerce')
    series = df[column].dropna()
    if series.empty:
        return f"❌ No valid numeric data in '{column}'."

    # Create figure
    fig = px.histogram(
        df,
        x=column,
        nbins=bins,
        title=f"Histogram – {column}",
        template='plotly_dark'
    )
    # Save PNG
    img_path = _save_fig(fig, f"hist_{column}_", output_dir)
    return fig, img_path


def scatter_matrix_tool(
    file_path: str,
    cols: list[str],
    output_dir: str = '/tmp'
) -> Union[Tuple[px.Figure, str], str]:
    """
    Build a scatter-matrix for selected numeric columns, return figure and PNG path,
    or an error string starting with '❌'.
    """
    # Load data
    ext = os.path.splitext(file_path)[1].lower()
    try:
        df = pd.read_excel(file_path) if ext in ('.xls', '.xlsx') else pd.read_csv(file_path)
    except Exception as exc:
        return f"❌ Failed to load file: {exc}"

    # Validate columns
    missing = [c for c in cols if c not in df.columns]
    if missing:
        return f"❌ Missing columns: {', '.join(missing)}"

    # Filter numeric
    df_num = df[cols].apply(pd.to_numeric, errors='coerce').dropna()
    if df_num.empty:
        return f"❌ No valid numeric data in selected columns."

    # Create figure
    fig = px.scatter_matrix(
        df_num,
        dimensions=cols,
        title="Scatter-Matrix",
        template='plotly_dark'
    )
    # Save PNG
    img_path = _save_fig(fig, "scatter_matrix_", output_dir)
    return fig, img_path


def corr_heatmap_tool(
    file_path: str,
    output_dir: str = '/tmp',
    color_continuous_scale: str = 'RdBu'
) -> Union[Tuple[px.Figure, str], str]:
    """
    Build a correlation heatmap for numeric columns, return figure and PNG path,
    or an error string starting with '❌'.
    """
    # Load data
    ext = os.path.splitext(file_path)[1].lower()
    try:
        df = pd.read_excel(file_path) if ext in ('.xls', '.xlsx') else pd.read_csv(file_path)
    except Exception as exc:
        return f"❌ Failed to load file: {exc}"

    # Compute correlation
    df_num = df.select_dtypes(include='number').apply(pd.to_numeric, errors='coerce')
    if df_num.empty:
        return "❌ No numeric columns available for correlation."
    corr = df_num.corr()

    # Create figure
    fig = px.imshow(
        corr,
        color_continuous_scale=color_continuous_scale,
        title="Correlation Heatmap",
        labels=dict(color="Correlation"),
        template='plotly_dark'
    )
    # Save PNG
    img_path = _save_fig(fig, "corr_heatmap_", output_dir)
    return fig, img_path