import pandas as pd
import io
import gradio as gr
from constants import REQUIRED_COLUMNS, MINIMAL_NUMBER_OF_ROWS, ANTIBODY_NAMES


def validate_csv_can_be_read(file_content: str) -> pd.DataFrame:
    """
    Validate that the CSV file can be read and parsed.

    Parameters
    ----------
    file_content: str
        The content of the uploaded CSV file.

    Returns
    -------
    pd.DataFrame
        The parsed DataFrame if successful.

    Raises
    ------
    gr.Error: If CSV cannot be read or parsed
    """
    try:
        # Read CSV content
        df = pd.read_csv(io.StringIO(file_content))
        return df

    except pd.errors.EmptyDataError:
        raise gr.Error("❌ CSV file is empty or contains no valid data")
    except pd.errors.ParserError as e:
        raise gr.Error(f"❌ Invalid CSV format<br><br>" f"Error: {str(e)}")
    except UnicodeDecodeError:
        raise gr.Error(
            "❌ File encoding error<br><br>"
            "Your file appears to have an unsupported encoding.<br>"
            "Please save your CSV file with UTF-8 encoding and try again."
        )


def validate_dataframe(df: pd.DataFrame) -> None:
    """
    Validate the DataFrame content and structure.

    Parameters
    ----------
    df: pd.DataFrame
        The DataFrame to validate.

    Raises
    ------
    gr.Error: If validation fails
    """
    # Required columns should be present
    missing_columns = set(REQUIRED_COLUMNS) - set(df.columns)
    if missing_columns:
        raise gr.Error(f"❌ Missing required columns: {', '.join(missing_columns)}")

    # Data should not be empty
    if df.empty:
        raise gr.Error("❌ CSV file is empty")

    # No missing values in required columns
    for col in REQUIRED_COLUMNS:
        missing_count = df[col].isnull().sum()
        if missing_count > 0:
            raise gr.Error(f"❌ Column '{col}' contains {missing_count} missing values")

    # Above minimal number of rows
    if len(df) < MINIMAL_NUMBER_OF_ROWS:
        raise gr.Error(f"❌ CSV should have at least {MINIMAL_NUMBER_OF_ROWS} rows")

    # All names should be unique
    n_duplicates = df["antibody_name"].duplicated().sum()
    if n_duplicates > 0:
        raise gr.Error(
            f"❌ CSV should have only one row per antibody. Found {n_duplicates} duplicates."
        )

    # All antibody names should be recognizable
    unrecognized_antibodies = set(df["antibody_name"]) - set(ANTIBODY_NAMES)
    if unrecognized_antibodies:
        raise gr.Error(
            f"❌ Found unrecognized antibody names: {', '.join(unrecognized_antibodies)}"
        )


def validate_csv_file(file_content: str) -> None:
    """
    Validate the uploaded CSV file.

    Parameters
    ----------
    file_content: str
        The content of the uploaded CSV file.

    Raises
    ------
    gr.Error: If validation fails
    """
    df = validate_csv_can_be_read(file_content)
    validate_dataframe(df)