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

The script demonstrates how to:
1. Define a Pydantic model that represents a parsed task card.
2. Build a LangChain chain that takes an informal description and returns a validated
   :class:`TaskCard` instance.
3. Print the raw JSON, the ``model_dump`` representation and a human‑readable summary.

The example is intentionally self‑contained – it does not rely on any external files
and can be executed with:

```bash
pip install -r requirements.txt
python main.py
```
"""

from __future__ import annotations

import os
from typing import List

from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import PydanticOutputParser

# Import the model defined in models.py
from models import TaskCard

# ---------------------------------------------------------------------------
# 1. Configure LLM – use BroJS endpoint via environment variable
# ---------------------------------------------------------------------------
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,
)

# ---------------------------------------------------------------------------
# 2. Prepare parser and prompt template
# ---------------------------------------------------------------------------
parser = PydanticOutputParser(pydantic_object=TaskCard)

prompt_template = PromptTemplate(
    template="""
You are an assistant that extracts structured information from a short informal task description.
Return the data in the format specified by the following instructions.

Input: {input_text}
{format_instructions}
""",
    input_variables=["input_text"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

chain = prompt_template | llm | parser

# ---------------------------------------------------------------------------
# 3. Example usage – three different informal descriptions
# ---------------------------------------------------------------------------
examples: List[str] = [
    "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 применении RAG в чат‑ботах. Требуется 10 слайдов, оценка – содержание и дизайн.",
    "Разработайте скрипт на Python, который парсит CSV и выводит статистику. Срок: до конца месяца. Оценка: корректность кода и комментарии.",
]

for idx, text in enumerate(examples, 1):
    print(f"\nExample {idx}:\n{text}\n")
    result = chain.invoke({"input_text": text})
    # ``result`` is already a TaskCard instance because of the parser.
    print("Parsed object (model_dump):", result.model_dump())
    print("Human‑readable summary:\n", str(result))

# ---------------------------------------------------------------------------
# 4. If run as script, execute examples
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    # The loop above already demonstrates the functionality.
    pass
"