src/generate/cli.py

import json
import pathlib
from copy import deepcopy
from typing import Callable
from functools import partial

import click
import pandas as pd
import pandera.pandas as pa
from tqdm.auto import tqdm
from langchain_core.runnables import Runnable

from src.common.data import load_dataset
from src.common.schema import DatasetSchema
from src.generate.config import GenerationConfig
from src.generate.schema import GeneratedDatasetSchema
from src.generate.answer import make_root_model, matches_type, string_to_type
from src.generate.generators import GenerationAnswer, GENERATORS_NAME_TO_FACTORY


def _save_temp_file(
    row: dict,
    result: GenerationAnswer,
    temp_path: pathlib.Path,
) -> None:
    temp_file = temp_path / f"{row[DatasetSchema.id_]}.json"
    json.dump(
        {
            DatasetSchema.id_: row[DatasetSchema.id_],
            GeneratedDatasetSchema.generated_answer: result.model_dump(),
        },
        open(temp_file, "w"),
        ensure_ascii=False,
    )


def _generate_single_answer(
    row: dict,
    build_chain: Callable[[type], Runnable],
    temp_path: pathlib.Path = None,
) -> GenerationAnswer:
    if temp_path and (temp_path / f"{row[DatasetSchema.id_]}.json").exists():
        return GenerationAnswer.model_validate(
            json.load(open(temp_path / f"{row[DatasetSchema.id_]}.json", "r"))[
                GeneratedDatasetSchema.generated_answer
            ]
        )
    answer_type = make_root_model(row[DatasetSchema.answer_type])
    chain = build_chain(answer_type)

    row = dict(row)
    row.pop(DatasetSchema.correct_answer, None)

    result: GenerationAnswer = chain.invoke(row)
    if temp_path:
        _save_temp_file(row, result, temp_path)
    return result


@pa.check_input(DatasetSchema)
@pa.check_output(GeneratedDatasetSchema)
def _generate_answers(
    df: pd.DataFrame,
    build_chain: Callable[[type], Runnable],
    use_tqdm: bool = True,
    temp_path: pathlib.Path = None,
) -> pd.DataFrame:
    if use_tqdm:
        tqdm.pandas()
        df[GeneratedDatasetSchema.generated_answer] = df.progress_apply(
            partial(
                _generate_single_answer,
                build_chain=build_chain,
                temp_path=temp_path,
            ),
            axis=1,
        )
    else:
        df[GeneratedDatasetSchema.generated_answer] = df.apply(
            partial(
                _generate_single_answer,
                build_chain=build_chain,
                temp_path=temp_path,
            ),
            axis=1,
        )
    df = df[list(GeneratedDatasetSchema._collect_fields().keys())]
    return df


@click.command()
@click.option(
    "--config-path",
    type=click.Path(exists=True, dir_okay=False),
    default=pathlib.Path("configs/ollama.yaml"),
    help="Path to the configuration file.",
)
@click.option(
    "--output-path",
    type=click.Path(dir_okay=False),
    default=pathlib.Path("./gemma3:4b.jsonl"),
    help="Path to the output file.",
)
@click.option(
    "--temp-path",
    type=click.Path(dir_okay=True, file_okay=False),
    default=pathlib.Path("./tmp_gemma3:4b/"),
    help="Path to the temp files directory.",
)
@click.option(
    "--use-tqdm",
    is_flag=True,
    default=True,
    help="Whether to use tqdm for progress bar.",
)
def generate(
    config_path: pathlib.Path = pathlib.Path("configs/ollama.yaml"),
    output_path: pathlib.Path = pathlib.Path("./gemma3:4b.jsonl"),
    temp_path: pathlib.Path = pathlib.Path("./tmp_gemma3:4b/"),
    use_tqdm: bool = True,
):
    output_path = pathlib.Path(output_path)
    temp_path = pathlib.Path(temp_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)
    temp_path.mkdir(parents=True, exist_ok=True)

    config = GenerationConfig.from_file(config_path)
    df = load_dataset()
    # df = df.head(3)

    build_chain_function = GENERATORS_NAME_TO_FACTORY[config.build_function]
    build_chain_function = partial(
        build_chain_function,
        llm_class=config.llm_class,
        structured_output_method=config.structured_output_method,
        **config.kwargs,
    )

    df = _generate_answers(
        df, build_chain_function, use_tqdm=use_tqdm, temp_path=temp_path
    )

    df[GeneratedDatasetSchema.generated_answer] = df[
        GeneratedDatasetSchema.generated_answer
    ].apply(lambda x: x.model_dump())
    df.to_json(
        output_path,
        lines=True,
        orient="records",
        force_ascii=False,
    )


@pa.check_input(GeneratedDatasetSchema)
def _type_sanitycheck(
    generated_df: pd.DataFrame,
) -> tuple[bool, str]:
    generated_df[GeneratedDatasetSchema.generated_answer] = generated_df[
        GeneratedDatasetSchema.generated_answer
    ].apply(
        lambda x: GenerationAnswer.model_validate(deepcopy(x))
        if not isinstance(x, GenerationAnswer)
        else x
    )

    dataset_df = load_dataset()
    predicted_df = dataset_df.join(
        generated_df.set_index(GeneratedDatasetSchema.id_),
        on=DatasetSchema.id_,
        rsuffix="_generated",
    ).dropna(subset=[GeneratedDatasetSchema.generated_answer])

    if len(predicted_df) == 0:
        return False, "No valid predictions found."

    TYPE_MATCH = "type_match"
    predicted_df[TYPE_MATCH] = predicted_df.apply(
        lambda row: matches_type(
            row[GeneratedDatasetSchema.generated_answer].answer,
            string_to_type(row[DatasetSchema.answer_type]),
        ),
        axis=1,
    )

    if not predicted_df[TYPE_MATCH].all():
        return (
            False,
            f"Type mismatch found for {predicted_df[~predicted_df[TYPE_MATCH]][DatasetSchema.id_].tolist()}.",
        )

    return (
        True,
        f"All matched. Predicted count: {len(predicted_df)} of {len(dataset_df)}",
    )


@click.command()
@click.option(
    "--file",
    type=click.Path(exists=True, dir_okay=False),
    default=pathlib.Path("./gemma3:4b.jsonl"),
    help="Path to the generated dataset file.",
)
def type_sanitycheck(
    file: pathlib.Path = pathlib.Path("./gemma3:4b.jsonl"),
):
    df = pd.read_json(file, lines=True)
    types_correct, message = _type_sanitycheck(df)
    if not types_correct:
        click.echo(f"❌ Type sanity check failed: {message}")
        exit(1)
    click.echo(f"✅ Type sanity check passed: {message}")


@click.group()
def cli():
    pass


cli.add_command(generate)
cli.add_command(type_sanitycheck)


if __name__ == "__main__":
    cli()