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import pandas as pd

def transform(inputs, output_dir):
    source_path = inputs["source_csv"]["path"]
    df = pd.read_csv(source_path)
    original_columns = list(df.columns)

    df.columns = [
        str(column).strip().lower().replace(" ", "_")
        for column in df.columns
    ]
    df = df.dropna(how="all")

    warnings = []
    required_columns = {"customer", "region", "revenue"}
    missing_columns = sorted(required_columns.difference(set(df.columns)))
    if missing_columns:
        warnings.append("Missing expected columns: " + ", ".join(missing_columns))

    if "region" not in df.columns:
        df["region"] = "Unassigned"

    if "revenue" in df.columns:
        revenue_text = (
            df["revenue"]
            .astype(str)
            .str.replace("$", "", regex=False)
            .str.replace(",", "", regex=False)
        )
        df["revenue"] = pd.to_numeric(revenue_text, errors="coerce").fillna(0)
    else:
        df["revenue"] = 0

    summary = (
        df.groupby("region", dropna=False)
        .agg(
            rows=("region", "size"),
            total_revenue=("revenue", "sum"),
            average_revenue=("revenue", "mean"),
        )
        .reset_index()
        .sort_values("total_revenue", ascending=False)
    )
    summary["total_revenue"] = summary["total_revenue"].round(2)
    summary["average_revenue"] = summary["average_revenue"].round(2)

    cleaned_path = f"{output_dir}/cleaned-data.csv"
    profile_path = f"{output_dir}/region-profile.csv"
    df.to_csv(cleaned_path, index=False)
    summary.to_csv(profile_path, index=False)

    return {
        "summary": {
            "input_rows": int(len(df)),
            "input_columns": int(len(original_columns)),
            "regions": int(summary.shape[0]),
        },
        "tables": {
            "region_profile": summary.fillna("").astype(str).to_dict(orient="records"),
            "cleaned_preview": df.head(20).fillna("").astype(str).to_dict(orient="records"),
        },
        "files": {
            "cleaned_csv": {
                "path": cleaned_path,
                "filename": "cleaned-data.csv",
                "mimeType": "text/csv",
            },
            "profile_csv": {
                "path": profile_path,
                "filename": "region-profile.csv",
                "mimeType": "text/csv",
            },
        },
        "warnings": warnings,
    }