import os
import sys
import asyncio
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from pydantic import BaseModel, Field
from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.prompts import PromptTemplate

load_dotenv()

# Pydantic models
class PersonInfo(BaseModel):
    name: str = Field(description="Full name of the person")
    age: int | None = Field(description="Age in years, optional", default=None)
    profession: str = Field(description="Current profession")
    skills: list[str] = Field(description="List of skills")

class MeetingNotes(BaseModel):
    date: str = Field(description="Date of the meeting")
    participants: list[str] = Field(description="List of participants")
    topics: list[str] = Field(description="Discussion topics")
    decisions: list[str] = Field(description="Decisions made")
    next_steps: list[str] = Field(description="Next steps to be taken")

# LLM configuration
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,
)

# Backend for deepagents
backend = CompositeBackend([
    LocalShellBackend(workspace_dir="./workspace"),
    FilesystemBackend(),
])

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

# Prompt template
prompt_template = PromptTemplate(
    input_variables=["format_instructions", "text"],
    template=(
        "Extract structured data from the following text. "
        "Follow the format:\n{format_instructions}\n\nText:\n{text}\n\nOutput:"
    ),
)

# Helper to decide which parser to use
def choose_parser(text: str):
    lower = text.lower()
    if any(word in lower for word in ["age", "years", "profession", "skills", "skill"]):
        return person_parser
    if any(word in lower for word in ["meeting", "participants", "topics", "decisions", "next steps", "next_step"]):
        return meeting_parser
    # Default to person_parser
    return person_parser

# Tool for extraction
@tool
def extract_structured(text: str) -> str:
    """
    Extract structured data from the given text and return a JSON string.
    """
    parser = choose_parser(text)
    format_instructions = parser.get_format_instructions()
    prompt = prompt_template.format(format_instructions=format_instructions, text=text)
    raw_output = llm.invoke(prompt).content
    try:
        parsed_obj = parser.parse(raw_output)
        return parsed_obj.model_dump_json()
    except Exception as e:
        return f"Error parsing output: {e}"

# Create the agent
agent = create_deep_agent(
    model=llm,
    tools=[extract_structured],
    backend=backend,
    system_prompt="You are a helpful agent that extracts structured data from text.",
)

# CLI logic
async def run_agent(text: str):
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=text)]},
        {"configurable": {"thread_id": "session-1"}},
    )
    output = result["messages"][-1].content
    try:
        data = PersonInfo.model_validate_json(output)
        obj_type = "PersonInfo"
    except Exception:
        try:
            data = MeetingNotes.model_validate_json(output)
            obj_type = "MeetingNotes"
        except Exception:
            print("Failed to parse JSON output.")
            return
    print("\nParsed object:")
    print(data.model_dump())
    print("\nSummary:")
    if obj_type == "PersonInfo":
        print(f"{obj_type}: {data.name}, age={data.age}, profession={data.profession}, skills={data.skills}")
    else:
        print(f"{obj_type}: date={data.date}, participants={data.participants}, topics={data.topics}, decisions={data.decisions}, next_steps={data.next_steps}")

def main():
    if len(sys.argv) > 1:
        input_text = " ".join(sys.argv[1:])
        asyncio.run(run_agent(input_text))
    else:
        examples = [
            (
                "Анна, 28 лет, Python-разработчик. Навыки: FastAPI, Docker.",
                "PersonInfo example",
            ),
            (
                "Встреча 12.09.2026. Участники: Иван, Мария. Темы: проект X, бюджет. Решения: увеличить бюджет. Следующие шаги: подготовить отчёт.",
                "MeetingNotes example",
            ),
        ]
        for text, title in examples:
            print(f"\n=== {title} ===")
            asyncio.run(run_agent(text))

if __name__ == "__main__":
    main()