import os
import asyncio
import json
import httpx
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 deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from langchain_core.messages import HumanMessage

# ---------------------------
# Configuration
# ---------------------------
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    raise RuntimeError("OPENAI_API_KEY not set in environment")

# LLM via OpenRouter
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=OPENAI_API_KEY,
    temperature=0.0,
)

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

# ---------------------------
# Chroma DB utilities
# ---------------------------
CHROMA_DIR = Path("./chroma_faq")
CHROMA_DIR.mkdir(parents=True, exist_ok=True)
vector_store = Chroma(
    collection_name="faq_collection",
    persist_directory=str(CHROMA_DIR),
    embedding_function=embeddings,
)


def load_faq_to_chroma(md_dir: str = "data"):
    """Load all .md files from md_dir into ChromaDB.
    Each file is split into chunks and added to the vector store.
    """
    md_path = Path(md_dir)
    if not md_path.exists():
        raise FileNotFoundError(f"Markdown directory {md_dir} not found")
    for md_file in md_path.glob("*.md"):
        text = md_file.read_text(encoding="utf-8")
        # Simple chunking: split by double newlines
        chunks = [c.strip() for c in text.split("\n\n") if c.strip()]
        docs = [Document(page_content=c, metadata={"source": md_file.name}) for c in chunks]
        vector_store.add_documents(docs)
    vector_store.persist()

# ---------------------------
# Tools
# ---------------------------
@tool
def search_course_docs(query: str, k: int = 3) -> str:
    """Search the local FAQ ChromaDB for relevant passages."""
    docs = vector_store.similarity_search(query, k=k)
    if not docs:
        return "No relevant information found in the course materials."
    return "\n\n---\n\n".join(f"{d.metadata.get('source', 'unknown')}\n{d.page_content}" for d in docs)

@tool
def fetch_course_meta(query: str) -> str:
    """Mock MCP-style tool that fetches course metadata from a local JSON file.
    In production this would be an HTTP call to an MCP server.
    """
    # For simplicity, we use a local JSON file. In a real scenario, replace with httpx.get.
    meta_path = Path("course_meta.json")
    if not meta_path.exists():
        return "Course metadata not available."
    data = json.loads(meta_path.read_text(encoding="utf-8"))
    # Very naive search: return any entry where query is a substring of title or description
    results = [f"{item['title']}: {item['description']}" for item in data if query.lower() in item.get('title', '').lower() or query.lower() in item.get('description', '').lower()]
    return "\n".join(results) if results else "No matching metadata found."

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

agent = create_deep_agent(
    model=llm,
    tools=[search_course_docs, fetch_course_meta],
    backend=backend,
    system_prompt=(
        "You are a helpful FAQ bot for the course.\n"
        "When a user asks about course content, use the search_course_docs tool.\n"
        "When a user asks about schedule, metadata, or other non-content info, use fetch_course_meta.\n"
        "Do not call both tools unless absolutely necessary.\n"
        "In your final answer, prepend 'source: chroma' or 'source: mcp_meta' to indicate the origin."
    ),
)

# ---------------------------
# CLI
# ---------------------------
PRESET_QUESTIONS = [
    "What is the deadline for the final project?",  # likely in metadata
    "Explain the concept of tokenization in NLP.",  # content
    "How many lectures are there in the first module?",  # content
]

async def run_agent(question: str, thread_id: str = "session-1"):
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=question)]},
        {"configurable": {"thread_id": thread_id}},
    )
    # The last message is the agent's reply
    reply = result["messages"][-1].content
    print(f"\nQ: {question}\nA: {reply}\n")

async def main():
    # Load data into Chroma if not already persisted
    if not CHROMA_DIR.exists() or not any(CHROMA_DIR.iterdir()):
        print("Loading FAQ data into ChromaDB...")
        load_faq_to_chroma()
    print("\n--- Predefined questions ---")
    for q in PRESET_QUESTIONS:
        await run_agent(q)
    print("\n--- Interactive mode (type 'exit' to quit) ---")
    while True:
        user_input = input("You: ")
        if user_input.lower() in {"exit", "quit"}:
            break
        await run_agent(user_input)

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