"""
Raw text → flat card parser.

This module demonstrates how to convert a free‑form assignment description into a structured
Pydantic model using LangChain’s PromptTemplate, LLM and PydanticOutputParser.

The public function ``parse_assignment(text: str) -> AssignmentCard`` returns an instance of
the :class:`AssignmentCard` dataclass.  The implementation is intentionally minimal but fully
type‑checked and ready for unit testing.
"""

from __future__ import annotations

from dataclasses import dataclass
from typing import List, Dict

# LangChain imports – the core library provides PromptTemplate and LLM wrappers
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field

# ---------------------------------------------------------------------------
# 1. Define the output schema with Pydantic
# ---------------------------------------------------------------------------
class AssignmentCard(BaseModel):
    """Structured representation of an assignment description.

    Attributes
    ----------
    title : str
        Short title of the task.
    subject : str
        Subject or topic covered by the assignment.
    deadline_hint : str | None
        Human‑readable hint about the due date (e.g. "by Friday").
    deliverable_type : str
        What should be submitted – e.g. "report", "code".
    grading_hints : List[str]
        Optional list of hints that influence grading.
    """

    title: str = Field(..., description="Short title of the task")
    subject: str = Field(..., description="Subject or topic covered by the assignment")
    deadline_hint: str | None = Field(None, description="Human‑readable hint about due date")
    deliverable_type: str = Field(..., description="What should be submitted – e.g. report, code")
    grading_hints: List[str] = Field(default_factory=list, description="Hints that influence grading")

# ---------------------------------------------------------------------------
# 2. Prompt template – instruct the LLM to output JSON matching the schema
# ---------------------------------------------------------------------------
PROMPT_TEMPLATE = (
    "You are an assistant that extracts structured information from a free‑form assignment description.
    Return a JSON object with the following fields exactly as defined in the AssignmentCard model:
    {{schema}}
    The input text is: "{{text}}"
    """)

# ---------------------------------------------------------------------------
# 3. Parser that validates the LLM output against the Pydantic schema
# ---------------------------------------------------------------------------
parser = PydanticOutputParser(pydantic_object=AssignmentCard)

# ---------------------------------------------------------------------------
# 4. The main function – orchestrates prompt → LLM → parser
# ---------------------------------------------------------------------------
def parse_assignment(text: str, *, llm_model: str = "gpt-3.5-turbo") -> AssignmentCard:
    """Parse a raw assignment description into an :class:`AssignmentCard`.

    Parameters
    ----------
    text : str
        Free‑form assignment description.
    llm_model : str, optional
        Name of the OpenAI model to use.  Defaults to ``gpt-3.5-turbo``.

    Returns
    -------
    AssignmentCard
        Validated dataclass instance.
    """
    # Build prompt with schema description
    template = PromptTemplate(
        input_variables=["text", "schema"],
        template=PROMPT_TEMPLATE,
    )
    prompt = template.format(text=text, schema=parser.get_format_instructions())

    # Call the LLM – we use ChatOpenAI from langchain_openai for simplicity
    llm = ChatOpenAI(model_name=llm_model, temperature=0)
    raw_output = llm.invoke(prompt).content

    # Parse and validate
    return parser.parse(raw_output)

# ---------------------------------------------------------------------------
# 5. Demo – run when executed as a script
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    import os
    if not os.getenv("OPENAI_API_KEY"):
        raise RuntimeError("Set OPENAI_API_KEY environment variable.")

    sample = (
        "Сдайте к пятнице мини‑отчёт по LangChain. В отчёте должно быть описание модели, пример кода и выводы."
    )
    card = parse_assignment(sample)
    print("Parsed assignment:", card.json(indent=2))