import os
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):
    title: str = Field(..., description="Title of the task")
    subject: str = Field(..., description="Subject or domain of the task")
    deadline_hint: str | None = Field(None, description="Short hint about deadline")
    deliverable_type: str = Field(..., description="What to submit: report, code, presentation etc.")
    grading_hints: list[str] = Field(default_factory=list, description="Hints for grading such as completeness, code example")

parser = PydanticOutputParser(pydantic_object=TaskCard)
prompt_template = PromptTemplate(
    template="""
Given the following informal task description:\n{task_description}\n\nReturn a JSON object with fields: title, subject, deadline_hint, deliverable_type, grading_hints.\n{format_instructions}
""",
    input_variables=["task_description"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.0,
)

async def main():
    task_desc = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
    chain = prompt_template | llm | parser
    result = await chain.ainvoke({"task_description": task_desc})
    print("Parsed object:\n", result)
    print("\nSummary:")
    for key, value in result.items():
        print(f"{key}: {value}")

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())