#!/usr/bin/env python
"""Structured output extraction with LangChain and Pydantic.

This script demonstrates how to parse a free‑text description of either a person
or a meeting into a validated Pydantic model using LangChain's structured
output facilities.  It can be run from the command line with a sample text or
with user input.
"""

import os
import sys
from typing import List, Union

from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field

# ---------------------------------------------------------------------------
# 1. Pydantic models
# ---------------------------------------------------------------------------

class PersonInfo(BaseModel):
    name: str = Field(..., description="Full name of the person")
    age: int | None = Field(None, description="Age in years, optional")
    profession: str = Field(..., description="Current profession or job title")
    skills: List[str] = Field(..., description="List of professional skills")

class MeetingNotes(BaseModel):
    date: str = Field(..., description="Date of the meeting in ISO format or natural language")
    participants: List[str] = Field(..., description="Names of participants")
    topics: List[str] = Field(..., description="Main discussion topics")
    decisions: List[str] = Field(..., description="Decisions made during the meeting")
    next_steps: List[str] = Field(..., description="Action items to be completed after the meeting")

# ---------------------------------------------------------------------------
# 2. LangChain LLM setup
# ---------------------------------------------------------------------------

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,
)

# ---------------------------------------------------------------------------
# 3. Prompt templates
# ---------------------------------------------------------------------------

person_prompt = PromptTemplate(
    input_variables=["text"],
    template="""
You are an assistant that extracts structured data about a person from the following text.

Text: {text}

Return the data as a JSON object that matches the PersonInfo schema.

{format_instructions}
""",
)

meeting_prompt = PromptTemplate(
    input_variables=["text"],
    template="""
You are an assistant that extracts structured data about a meeting from the following text.

Text: {text}

Return the data as a JSON object that matches the MeetingNotes schema.

{format_instructions}
""",
)

# ---------------------------------------------------------------------------
# 4. Output parsers
# ---------------------------------------------------------------------------

person_parser = PydanticOutputParser(pydantic_object=PersonInfo)
meeting_parser = PydanticOutputParser(pydantic_object=MeetingNotes)

# ---------------------------------------------------------------------------
# 5. Heuristic to decide which schema to use
# ---------------------------------------------------------------------------

def choose_schema(text: str) -> str:
    """Return 'person' or 'meeting' based on simple keyword heuristics.

    The heuristic is intentionally simple: if the text contains words that
    are more likely to appear in a meeting description (e.g. "meeting", "agenda",
    "participants", "decisions", "action items") we choose the meeting schema.
    Otherwise we default to the person schema.
    """
    meeting_keywords = {
        "meeting",
        "agenda",
        "participants",
        "decisions",
        "action items",
        "next steps",
        "topics",
        "date",
    }
    text_lower = text.lower()
    if any(word in text_lower for word in meeting_keywords):
        return "meeting"
    return "person"

# ---------------------------------------------------------------------------
# 6. Main extraction function
# ---------------------------------------------------------------------------

def extract(text: str) -> Union[PersonInfo, MeetingNotes]:
    schema_type = choose_schema(text)
    if schema_type == "person":
        prompt = person_prompt
        parser = person_parser
    else:
        prompt = meeting_prompt
        parser = meeting_parser

    chain = prompt | llm | parser
    result = chain.invoke({"text": text})
    return result

# ---------------------------------------------------------------------------
# 7. CLI
# ---------------------------------------------------------------------------

def main():
    if len(sys.argv) > 1:
        # First argument is the text to parse
        input_text = " ".join(sys.argv[1:])
    else:
        # Provide two built‑in examples
        examples = {
            "person": "Анна, 28 лет, Python-разработчик. Навыки: FastAPI, Docker.",
            "meeting": "Встреча 12.09.2026. Участники: Иван, Мария. Темы: проект X, бюджет. Решения: утвердить бюджет. Next steps: подготовить презентацию.",
        }
        print("Choose example: 1 - person, 2 - meeting, or type your own text.")
        choice = input("> ").strip()
        if choice == "1":
            input_text = examples["person"]
        elif choice == "2":
            input_text = examples["meeting"]
        else:
            input_text = choice

    print("\nInput text:\n" + input_text + "\n")
    try:
        obj = extract(input_text)
        print("\nParsed object (model_dump):")
        print(obj.model_dump(indent=2))
        print("\nSummary: ", obj)
    except Exception as e:
        print("Error during parsing:", e)

if __name__ == "__main__":
    main()