from pydantic import BaseModel, Field
from typing import List
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 assignment")
    subject: str = Field(..., description="Subject or topic of the assignment")
    deadline_hint: str = Field(..., description="Free‑form deadline hint")
    deliverable_type: str = Field(..., description="What to submit: report, code, presentation, etc.")
    grading_hints: List[str] = Field(..., description="List of grading hints mentioned in the text")

parser = PydanticOutputParser(pydantic_object=TaskCard)

prompt_template = (
    "You are an assistant that extracts structured information from a short assignment description. "
    "Return the data in the following JSON format exactly as required by the parser. "
    "Do not add any extra keys or text.\n"
    "{format_instructions}\n"
    "Input: {input_text}\n"
    "Output:"
)

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

llm = ChatOpenAI(temperature=0)

chain = prompt | llm | parser

if __name__ == "__main__":
    # Example input
    input_text = "Сдайте к пятнице мини-отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
    result = chain.invoke({"input_text": input_text})
    print("Parsed object:")
    print(result.model_dump())
    print("\nHuman readable summary:\n")
    print(f"Title: {result.title}")
    print(f"Subject: {result.subject}")
    print(f"Deadline: {result.deadline_hint}")
    print(f"Deliverable: {result.deliverable_type}")
    print(f"Grading hints: {', '.join(result.grading_hints)}")