import os
import asyncio
import json
from pathlib import Path

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain.tools import tool
from langchain_text_splitter import RecursiveCharacterTextSplitter

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from langchain_core.messages import HumanMessage

# ---------- LLM ----------
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 ----------
backend = CompositeBackend(
    [
        LocalShellBackend(workspace_dir="./workspace"),
        FilesystemBackend(),
    ]
)

# ---------- Embeddings ----------
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
)

# ---------- Vector Store ----------
vector_store = Chroma(
    collection_name="faq",
    embedding_function=embeddings,
    persist_directory="./chroma_faq",
)

# ---------- Load FAQ into Chroma ----------
def load_faq_to_chroma() -> None:
    """Read .md files from data/ and add them to the Chroma collection."""
    data_dir = Path("data")
    if not data_dir.exists():
        return
    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    docs = []
    for md_file in data_dir.glob("*.md"):
        content = md_file.read_text(encoding="utf-8")
        chunks = splitter.split_text(content)
        for i, chunk in enumerate(chunks):
            docs.append(
                Document(
                    page_content=chunk,
                    metadata={"source": md_file.name, "chunk": i},
                )
            )
    if docs:
        vector_store.add_documents(docs)
        vector_store.persist()

# ---------- Tools ----------
@tool
def search_course_docs(query: str, k: int = 3) -> str:
    """Search the knowledge base for relevant information."""
    docs = vector_store.similarity_search(query, k=k)
    return "\n\n".join(d.page_content for d in docs) if docs else "No results found."

# Load meta data once
META_PATH = Path("meta.json")
if META_PATH.exists():
    META_DATA = json.loads(META_PATH.read_text(encoding="utf-8"))
else:
    META_DATA = {}

@tool
def fetch_course_meta(query: str) -> str:
    """Return course metadata (schedule, etc.)."""
    # Simple lookup: return the whole meta if query matches a key
    for key, value in META_DATA.items():
        if key.lower() in query.lower():
            return f"{key}: {value}"
    # Fallback: return all metadata
    return json.dumps(META_DATA, indent=2)

# ---------- Agent ----------
system_prompt = (
    "You are a helpful FAQ bot. Use search_course_docs for questions about course materials. "
    "Use fetch_course_meta for questions about schedule or metadata. "
    "Do not call both tools unless necessary. "
    "In your answer, indicate source: chroma or mcp_meta."
)

agent = create_deep_agent(
    model=llm,
    tools=[search_course_docs, fetch_course_meta],
    backend=backend,
    system_prompt=system_prompt,
)

# ---------- CLI ----------
PRESET_QUESTIONS = [
    "What topics are covered in the introductory module?",
    "Explain the advanced algorithm discussed in chapter 3.",
    "What is the schedule for the next semester?",
]

async def run_preset():
    for q in PRESET_QUESTIONS:
        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=q)]},
            {"configurable": {"thread_id": "session-1"}},
        )
        print("\nQuestion:", q)
        print("Answer:", result["messages"][-1].content)

async def interactive_loop():
    print("\nEnter your question (type 'exit' to quit):")
    while True:
        user_input = input("> ")
        if user_input.lower() in ("exit", "quit"):
            break
        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=user_input)]},
            {"configurable": {"thread_id": "session-1"}},
        )
        print("Answer:", result["messages"][-1].content)

async def main():
    load_faq_to_chroma()
    await run_preset()
    await interactive_loop()

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