import os
import asyncio
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 CompositeBackend, LocalShellBackend, FilesystemBackend
from pydantic import BaseModel, Field
from langchain_core.output_parsers import PydanticOutputParser

# LLM configuration – OpenRouter
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 file operations and shell execution (not used directly but required by deepagents)
backend = CompositeBackend([
    LocalShellBackend(workspace_dir="./workspace"),
    FilesystemBackend(),
])

# Structured output model for the AI fluency plan
class FluencyPlan(BaseModel):
    goal: str = Field(description="Overall goal of the AI fluency plan")
    timeline: str = Field(description="High‑level timeline (e.g., 3 months, 6 months)")
    milestones: list[str] = Field(description="Key milestones to achieve along the way")
    resources: list[str] = Field(description="Recommended courses, books, tools, and communities")
    assessment: str = Field(description="How to assess progress and adjust the plan")

parser = PydanticOutputParser(pydantic_object=FluencyPlan)

# Simple validation tool to ensure the plan is not empty
@tool
def validate_plan(plan: str) -> str:
    """Validate that the plan contains at least one milestone and resources."""
    if "milestones" not in plan.lower() or "resources" not in plan.lower():
        return "Plan is missing essential sections."
    return "OK"

# Create the deep agent
agent = create_deep_agent(
    model=llm,
    tools=[validate_plan],
    backend=backend,
    system_prompt="You are an expert educational planner. Your task is to create a detailed personal AI fluency plan based on the course "
                 "Ai Fluency and the Build a personal AI fluency plan assignment. The output must be a JSON object that matches the FluencyPlan schema."
)

async def main():
    # Human message with the assignment description
    human_msg = HumanMessage(content="Create a personal AI fluency plan for completing the Ai Fluency course and the Build a personal AI fluency plan assignment. Use the provided schema.")

    # Invoke the agent
    result = await agent.ainvoke(
        {"messages": [human_msg]},
        {"configurable": {"thread_id": "ai-fluency-plan"}},
    )

    # Extract the assistant message
    assistant_msg = result["messages"][-1].content

    # Parse the JSON using the Pydantic parser to ensure correctness
    try:
        plan_obj = parser.parse(assistant_msg)
        print("✅ Generated AI Fluency Plan:\n", plan_obj.json(indent=2))
    except Exception as e:
        print("❌ Failed to parse plan:", e)
        print("Raw output:", assistant_msg)

if __name__ == "__main__":
    asyncio.run(main())