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 """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}")