from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field, SecretStr
from langchain.agents import create_agent
import sys

# LLM placeholder configuration
llm = ChatOpenAI(
    model="openai/gpt-oss-20b",
    base_url='https://platform.brojs.ru/jrnl-bh/api/inference/v1',
    api_key=SecretStr("jrnl_30283ab953615cbb6846ff9940a1eedce0b76d7b2f59a2394f29e74643e6a90d"),
    temperature=0.7,
)

# ---------- Pydantic models ----------
class PersonInfo(BaseModel):
    """Information about a person."""
    name: str = Field(description="Full name of the person")
    age: int | None = Field(default=None, description="Age in years, optional")
    profession: str = Field(description="Current occupation or job title")
    skills: list[str] = Field(description="List of professional skills")

class MeetingNotes(BaseModel):
    """Summary of a meeting."""
    date: str = Field(description="Date of the meeting (ISO format)")
    participants: list[str] = Field(description="Names of attendees")
    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 for follow‑up")

# ---------- Agent creation ----------
agent_person = create_agent(
    model=llm,
    response_format=PersonInfo,
    system_prompt="You are an assistant that extracts structured person information from a single sentence.",
)

agent_meeting = create_agent(
    model=llm,
    response_format=MeetingNotes,
    system_prompt="You are an assistant that extracts structured meeting notes from a paragraph of text.",
)

# ---------- Simple heuristic to choose schema ----------
def detect_schema(text: str) -> str:
    """Return 'person' or 'meeting' based on simple keyword heuristics."""
    lower = text.lower()
    if any(word in lower for word in ("profession", "skills", "age")):
        return "person"
    if any(word in lower for word in ("meeting", "participants", "decisions", "next steps")):
        return "meeting"
    # Default to person if ambiguous
    return "person"

# ---------- CLI ----------
def main():
    examples = [
        "Анна, 28 лет, Python-разработчик. Навыки: FastAPI, Docker.",
        ("Встреча с командой по проекту X прошла 2024-05-20.\n"
         "Участники: Иван, Мария, Алексей.\n"
         "Темы: планирование спринта, распределение задач.\n"
         "Решения: назначить ответственных за каждый модуль.\n"
         "Next steps: подготовить спецификации к 2024-05-27."),
    ]

    if len(sys.argv) > 1:
        texts = [" ".join(sys.argv[1:])]
    else:
        texts = examples

    for txt in texts:
        print("\n=== Input ===")
        print(txt)
        schema_type = detect_schema(txt)
        agent = agent_person if schema_type == "person" else agent_meeting
        result = agent.invoke({"messages": [{"role": "user", "content": txt}]})
        structured = result["structured_response"]
        print("\n=== Output ===")
        print(structured.model_dump(indent=2))
        print("\n---")

if __name__ == "__main__":
    main()