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

# Load environment variables (OPENAI_API_KEY)
load_dotenv()

# 1. Define the Pydantic model for the task card
class TaskCard(BaseModel):
    title: str = Field(description="Краткое название задания")
    subject: str = Field(description="Предмет или тема задания")
    deadline_hint: str = Field(description="Краткая подсказка о сроке выполнения, например 'к пятнице'")
    deliverable_type: str = Field(description="Тип сдачи: отчёт, код, презентация и т.п.")
    grading_hints: list[str] = Field(description="Список критериев оценки, упомянутых в тексте")

# 2. Create the parser that will enforce the structure
parser = PydanticOutputParser(pydantic_object=TaskCard)

# 3. Prompt template that asks the model to output the data in the required format
prompt = PromptTemplate(
    template="""
Ниже приведено описание задания. Ваша задача — извлечь из него следующую информацию и вернуть в формате JSON, соответствующем модели TaskCard:

{input_text}

{format_instructions}
""",
    input_variables=["input_text"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

# 4. 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,
)

# 5. Define a tool that performs the parsing chain
@tool
def parse_task(input_text: str) -> str:
    """Parse a raw task description into a structured JSON string."""
    chain = prompt | llm | parser
    result = chain.invoke({"input_text": input_text})
    # parser returns a dict; convert to JSON string for the agent to return
    return result.model_dump_json()

# 6. Backend for the agent (filesystem + local shell, though not used here)
backend = CompositeBackend([
    LocalShellBackend(workspace_dir="./workspace"),
    FilesystemBackend(),
])

# 7. Create the deep agent with the parsing tool
agent = create_deep_agent(
    model=llm,
    tools=[parse_task],
    backend=backend,
    system_prompt="You are a task‑card extractor. Use the provided tool to parse the input and return the JSON string.",
)

# 8. Main entry point
async def main():
    # Example input – replace with any user text
    user_text = "Сдайте к пятнице мини‑отчёт по LangChain: 2 страницы, упор на агентов. Оценка: за полноту и за пример кода."
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=user_text)]},
        {"configurable": {"thread_id": "session-1"}},
    )
    # The agent returns the tool output as the last message
    output = result["messages"][-1].content
    print("Parsed JSON:")
    print(output)
    # Pretty‑print the parsed object
    card = TaskCard.parse_raw(output)
    print("\nHuman‑readable summary:")
    print(f"Title: {card.title}")
    print(f"Subject: {card.subject}")
    print(f"Deadline hint: {card.deadline_hint}")
    print(f"Deliverable type: {card.deliverable_type}")
    print(f"Grading hints: {', '.join(card.grading_hints)}")

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