"""Task parser for raw assignment text.

This module demonstrates how to convert a free‑form assignment description into a
structured data object using LangChain and Pydantic.

The main entry point is :func:`parse_task` which accepts a string and returns a
validated :class:`TaskCard` instance.

Example usage:

>>> from main import parse_task
>>> text = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
>>> card = parse_task(text)
>>> print(card.model_dump())
{"title": "мини-отчёт по LangChain", "subject": "LangChain", "deadline_hint": "к пятнице", "deliverable_type": "отчёт", "grading_hints": ["полнота", "пример кода"]}
"""

from __future__ import annotations

from typing import List

from pydantic import BaseModel, Field
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import PydanticOutputParser


class TaskCard(BaseModel):
    """Structured representation of an assignment description."""

    title: str = Field(..., description="Short title of the assignment")
    subject: str = Field(..., description="Subject or topic of the assignment")
    deadline_hint: str = Field(..., description="Free‑form deadline hint")
    deliverable_type: str = Field(..., description="What is expected to be submitted")
    grading_hints: List[str] = Field(..., description="List of grading criteria mentioned in the text")


# Prompt template – we ask the model to output JSON that matches TaskCard.
prompt_template = (
    "You are an assistant that extracts structured information from a short assignment description."
    " Return a JSON object with the following fields: title, subject, deadline_hint, deliverable_type, grading_hints."
    " Do not add any extra keys or text."
    " Example input: {input_text}\n"
    " {format_instructions}"
)

parser = PydanticOutputParser(pydantic_object=TaskCard)
prompt = PromptTemplate(
    template=prompt_template,
    input_variables=["input_text"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

# Use the default OpenAI model; the user can set OPENAI_API_KEY.
llm = ChatOpenAI(temperature=0)

# Chain: prompt -> LLM -> parser
chain = prompt | llm | parser


def parse_task(text: str) -> TaskCard:
    """Parse raw assignment text into a :class:`TaskCard`.

    Parameters
    ----------
    text:
        Raw assignment description.

    Returns
    -------
    TaskCard
        Validated structured data.
    """
    return chain.invoke({"input_text": text})


if __name__ == "__main__":
    import sys

    if len(sys.argv) < 2:
        print("Usage: python main.py '<assignment text>'")
        sys.exit(1)
    raw = sys.argv[1]
    card = parse_task(raw)
    print("Parsed card:\n", card.model_dump(indent=2))
    print("\nHuman‑readable summary:\n")
    print(f"Title: {card.title}")
    print(f"Subject: {card.subject}")
    print(f"Deadline hint: {card.deadline_hint}")
    print(f"Deliverable type: {card.deliverable_type}")
    print("Grading hints:")
    for hint in card.grading_hints:
        print(f"- {hint}")