import pytest
import pandas as pd
import gradio as gr
from validation import validate_csv_file, validate_csv_can_be_read, validate_dataframe
from constants import REQUIRED_COLUMNS, MINIMAL_NUMBER_OF_ROWS


class TestValidateCsvCanBeRead:
    """Test cases for validate_csv_can_be_read function"""

    def test_valid_csv_can_be_read(self, valid_csv_content):
        """Test that valid CSV content can be read"""
        df = validate_csv_can_be_read(valid_csv_content)
        assert isinstance(df, pd.DataFrame)
        assert len(df) == MINIMAL_NUMBER_OF_ROWS
        assert list(df.columns) == list(REQUIRED_COLUMNS)

    def test_empty_csv_raises_error(self):
        """Test that empty CSV raises an error"""
        empty_csv = ""

        with pytest.raises(gr.Error) as exc_info:
            validate_csv_can_be_read(empty_csv)

        assert "empty or contains no valid data" in str(exc_info.value)

    def test_invalid_csv_format_raises_error(self):
        """Test that invalid CSV format raises an error"""
        # Create a CSV with malformed structure that pandas cannot parse
        malformed_csv = 'column1,column2\nvalue1,"unclosed quote\nvalue4,value5'

        with pytest.raises(gr.Error) as exc_info:
            validate_csv_can_be_read(malformed_csv)

        assert "Invalid CSV format" in str(exc_info.value)

    def test_csv_with_quoted_fields_can_be_read(self):
        """Test that CSV with quoted fields can be read"""
        # Create CSV with quoted fields and enough rows
        base_row = 'AB001,"EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYAMHWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDYGDGYYFDYWGQGTLVTVSS","DIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSTPFTFGQGTKVEIK",95.2,0.85,0.92,0.78,0.81,72.5'
        csv_content = "antibody_id,vh_protein_sequence,vl_protein_sequence,SEC %Monomer,HIC,PR_CHO,AC-SINS_pH6.0,AC-SINS_pH7.4,Tm\n"
        csv_content += "\n".join([base_row] * MINIMAL_NUMBER_OF_ROWS)

        df = validate_csv_can_be_read(csv_content)
        assert isinstance(df, pd.DataFrame)
        assert len(df) == MINIMAL_NUMBER_OF_ROWS


class TestValidateDataframe:
    """Test cases for validate_dataframe function"""

    def test_valid_dataframe_passes(self, valid_input_dataframe):
        """Test that valid DataFrame passes validation"""
        validate_dataframe(valid_input_dataframe)

    def test_missing_columns_raises_error(self, valid_input_dataframe):
        """Test that DataFrame with missing columns raises an error"""
        missing_column = REQUIRED_COLUMNS[0]
        df = valid_input_dataframe.copy()
        df.drop(columns=[missing_column], inplace=True)

        with pytest.raises(gr.Error) as exc_info:
            validate_dataframe(df)

        assert f"Missing required columns: {missing_column}" in str(exc_info.value)

    def test_empty_dataframe_raises_error(self, valid_input_dataframe):
        """Test that empty DataFrame raises an error"""
        empty_df = valid_input_dataframe.head(0)

        with pytest.raises(gr.Error) as exc_info:
            validate_dataframe(empty_df)

        assert "CSV file is empty" in str(exc_info.value)

    def test_insufficient_rows_raises_error(self, valid_input_dataframe):
        """Test that DataFrame with insufficient rows raises an error"""
        df = valid_input_dataframe.head(MINIMAL_NUMBER_OF_ROWS - 1)
        with pytest.raises(gr.Error) as exc_info:
            validate_dataframe(df)

        assert f"CSV should have at least {MINIMAL_NUMBER_OF_ROWS} rows" in str(
            exc_info.value
        )

    def test_missing_values_raises_error(self, valid_input_dataframe):
        """Test that DataFrame with missing values raises an error"""
        bad_column = REQUIRED_COLUMNS[0]
        df = valid_input_dataframe.copy()
        df[bad_column] = [None] * len(df)
        with pytest.raises(gr.Error) as exc_info:
            validate_dataframe(df)

        assert f"contains {len(df)} missing values" in str(exc_info.value)

    def test_csv_with_extra_columns_passes(self, valid_input_dataframe):
        """Test that DataFrame with extra columns passes validation"""
        extra_column = "extra_column_1"
        df = valid_input_dataframe.copy()
        df[extra_column] = ["extra1"] * len(df)
        df[extra_column] = ["extra2"] * len(df)
        validate_dataframe(df)


class TestValidateCsvFile:
    """Test cases for the combined validate_csv_file function"""

    def test_valid_csv_passes(self, valid_csv_content):
        """Test that a valid CSV with all required columns passes validation"""
        validate_csv_file(valid_csv_content)