import pandas as pd
import pyarrow as pa
import io
from typing import Tuple, Dict, Any

def parse_file(file_content: bytes, filename: str) -> pd.DataFrame:
    """
    Parses the uploaded file (Excel or CSV) into a Pandas DataFrame.
    """
    file_obj = io.BytesIO(file_content)
    
    if filename.endswith(('.xlsx', '.xls')):
        df = pd.read_excel(file_obj)
    elif filename.endswith('.csv'):
        # Attempt to detect common delimiters if needed, default to comma
        df = pd.read_csv(file_obj)
    else:
        raise ValueError(f"Unsupported file format: {filename}")
    
    # Basic hygiene: strip whitespace from headers
    df.columns = [str(c).strip() for c in df.columns]
    
    return df

def get_column_metadata(df: pd.DataFrame) -> list:
    """
    Returns a list of column metadata (name and inferred type).
    """
    metadata = []
    for col in df.columns:
        dtype = str(df[col].dtype)
        # Simplify types for the frontend
        inferred_type = "numeric"
        if "object" in dtype or "string" in dtype:
            inferred_type = "categorical"
        elif "datetime" in dtype:
            inferred_type = "date"
        elif "bool" in dtype:
            inferred_type = "boolean"
            
        metadata.append({
            "name": col,
            "type": inferred_type,
            "native_type": dtype
        })
    return metadata

def dataframe_to_arrow_stream(df: pd.DataFrame) -> bytes:
    """
    Converts a Pandas DataFrame to an Apache Arrow IPC stream.
    """
    table = pa.Table.from_pandas(df)
    sink = pa.BufferOutputStream()
    with pa.ipc.new_stream(sink, table.schema) as writer:
        writer.write_table(table)
    return sink.getvalue().to_pybytes()