"""
Main entry point for the "сырой текст задания → плоская карточка" task.

The script demonstrates how to convert a free‑form description of an assignment into a structured data object using LangChain and Pydantic.

Usage examples are provided in the ``__main__`` section – three different raw texts are parsed and printed.
"""

from __future__ import annotations

import os
from typing import List

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

# ---------------------------------------------------------------------------
# 1. Define the data model that represents a task card.
# ---------------------------------------------------------------------------
class TaskCard(BaseModel):
    """Structured representation of an assignment description.

    The fields are intentionally generic – they capture the most common pieces of information
    that appear in the course tasks:

    * ``title`` – short name of the task.
    * ``subject`` – subject or topic area.
    * ``deadline_hint`` – free‑form hint about when the task should be finished.
    * ``deliverable_type`` – what is expected to be submitted (report, code, presentation…).
    * ``grading_hints`` – list of items that influence grading.
    """

    title: str = Field(..., description="Short name of the task")
    subject: str | None = Field(None, description="Subject or topic area")
    deadline_hint: str | None = Field(
        None,
        description="Free‑form hint about when the task should be finished",
    )
    deliverable_type: str | None = Field(
        None,
        description="What is expected to be submitted (report, code, presentation…)",
    )
    grading_hints: List[str] = Field(
        default_factory=list,
        description="List of items that influence grading",
    )

# ---------------------------------------------------------------------------
# 2. Build the prompt and parser.
# ---------------------------------------------------------------------------
parser = PydanticOutputParser(pydantic_object=TaskCard)

prompt_template = PromptTemplate(
    template=(
        "You are an assistant that extracts structured information from a free‑form task description."
        " Return only the data in JSON format that matches the following schema:\n{format_instructions}\n"
        "Input: {text}"
    ),
    input_variables=["text"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

# ---------------------------------------------------------------------------
# 3. Create the LLM instance.
# ---------------------------------------------------------------------------
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
    api_key=os.getenv("JOURNAL_MCP_PAT"),
    temperature=0.0,
)

# ---------------------------------------------------------------------------
# 4. Helper that runs the chain and returns a TaskCard.
# ---------------------------------------------------------------------------
async def parse_task(text: str) -> TaskCard:
    """Parse *text* into a :class:`TaskCard` using LangChain.

    The function is asynchronous because ``ChatOpenAI`` uses an async API.  It can be called from
    synchronous code via ``asyncio.run``.
    """

    chain = prompt_template | llm | parser
    result = await chain.ainvoke({"text": text})
    return result

# ---------------------------------------------------------------------------
# 5. Demo – three example texts.
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    import asyncio

    examples = [
        "Сдайте к пятнице мини‑отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода.",
        "На следующей неделе подготовьте презентацию о Qdrant. Должно быть 10 слайдов, включать примеры кода. Оценка по содержанию и дизайну.",
        "Разработайте скрипт на Python, который генерирует случайный пароль длиной 12 символов. Сдача – код в репозитории. Оценка: корректность и безопасность.",
    ]

    async def demo():
        for i, txt in enumerate(examples, start=1):
            card = await parse_task(txt)
            print(f"\nExample {i}:")
            print("Raw text:")
            print(txt)
            print("\nParsed card: ")
            # Pretty‑print the model using ``model_dump`` – it returns a dict.
            print(card.model_dump(indent=2, sort_keys=False))

    asyncio.run(demo())