"""Task 69dd4221f309a98be0006b2e – Structured task card parser.

The script accepts a single natural‑language description of a course task and
returns a validated Pydantic model instance.  It demonstrates a one‑shot
prompting pattern with a structured output parser.

Usage:
    python main.py "<task description>"

The script prints the raw model dump and a short human‑readable summary.
"""

import sys
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

# ---------------------------------------------------------------------------
# 1. Pydantic model
# ---------------------------------------------------------------------------
class TaskCard(BaseModel):
    """Compact representation of a course task.

    All fields are optional because the model may not be able to infer every
    piece of information from a very short description.  The parser will
    still return a valid instance – missing values will be ``None``.
    """

    title: str | None = Field(None, description="Short title of the task")
    subject: str | None = Field(None, description="Subject or topic of the task")
    deadline_hint: str | None = Field(
        None, description="Free‑form hint about the deadline (e.g. 'к пятнице')"
    )
    deliverable_type: str | None = Field(
        None, description="What is expected to be submitted (report, code, etc.)"
    )
    grading_hints: List[str] | None = Field(
        None, description="List of hints mentioned about grading"
    )

# ---------------------------------------------------------------------------
# 2. Prompt + chain
# ---------------------------------------------------------------------------
parser = PydanticOutputParser(pydantic_object=TaskCard)

prompt_template = """You are a helper that extracts structured information from a
course task description.  Return a JSON object that matches the following
schema:

{schema}

The input description is:

{description}

Respond ONLY with the JSON object.  Do not add any extra text.
"""

prompt = PromptTemplate(
    template=prompt_template,
    input_variables=["description"],
    partial_variables={"schema": parser.get_format_instructions()},
)

# LLM – use the default OpenAI OSS endpoint via environment variables
llm = ChatOpenAI(model="openai/gpt-oss-20b:free", temperature=0.2)

chain = prompt | llm | parser

# ---------------------------------------------------------------------------
# 3. Main entry point
# ---------------------------------------------------------------------------

def main() -> None:
    if len(sys.argv) < 2:
        print("Usage: python main.py '<task description>'")
        sys.exit(1)

    description = sys.argv[1]
    result: TaskCard = chain.invoke({"description": description})

    # Print raw model dump
    print("\n--- Parsed model ---")
    print(result.model_dump(indent=2))

    # Human‑readable summary
    print("\n--- Summary ---")
    print(f"Title: {result.title or 'N/A'}")
    print(f"Subject: {result.subject or 'N/A'}")
    print(f"Deadline hint: {result.deadline_hint or 'N/A'}")
    print(f"Deliverable: {result.deliverable_type or 'N/A'}")
    if result.grading_hints:
        print("Grading hints:")
        for hint in result.grading_hints:
            print(f"- {hint}")
    else:
        print("Grading hints: N/A")


if __name__ == "__main__":
    main()