"""
Task: Convert raw task text to flat card using LangChain and Pydantic.
"""

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="Task title")
    subject: str = Field(..., description="Subject or topic of the task")
    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")

# Prompt template
prompt_template = """
You are an assistant that extracts structured information from a natural language task description.
Return the data in the following JSON format:
{format_instructions}

Task description: {task_text}
"""

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

llm = ChatOpenAI(temperature=0)
chain = prompt | llm | parser

if __name__ == "__main__":
    import sys
    if len(sys.argv) < 2:
        print("Usage: python main.py '<task description>'")
        sys.exit(1)
    task_text = sys.argv[1]
    result = chain.invoke({"task_text": task_text})
    print("Parsed card:\n", result.model_dump(indent=2))
    # Human readable summary
    print("\nSummary:\n")
    print(f"Title: {result.title}")
    print(f"Subject: {result.subject}")
    print(f"Deadline hint: {result.deadline_hint}")
    print(f"Deliverable type: {result.deliverable_type}")
    print(f"Grading hints: {', '.join(result.grading_hints)}")
"""