import os
import asyncio
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

# ----------------- Configuration -----------------
# Load OpenRouter API key from .env or environment variable
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    raise RuntimeError("OPENAI_API_KEY not set in environment")

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

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

# ----------------- ChromaDB setup -----------------
CHROMA_PATH = Path("./chroma_faq")
CHROMA_COLLECTION = "faq_collection"
vector_store = Chroma(
    collection_name=CHROMA_COLLECTION,
    embedding_function=embeddings,
    persist_directory=str(CHROMA_PATH),
)

# Load markdown files into Chroma if not already loaded
if not CHROMA_PATH.exists() or not any(CHROMA_PATH.iterdir()):
    data_dir = Path("data")
    docs = []
    for md_file in data_dir.glob("*.md"):
        text = md_file.read_text(encoding="utf-8")
        docs.append(Document(page_content=text, metadata={"source": md_file.name}))
    vector_store.add_documents(docs)
    vector_store.persist()

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

@tool
def fetch_course_meta(query: str) -> str:
    """Mock MCP-style tool that returns course metadata.
    In production this would be an HTTP call to an MCP server.
    Here we return a static JSON-like string based on the query.
    """
    # Simple static mapping for demo purposes
    meta = {
        "schedule": "Monday 10:00-12:00, Wednesday 14:00-16:00",
        "instructor": "Dr. Ivanov",
        "location": "Room 101",
    }
    key = query.lower().strip()
    return meta.get(key, f"No metadata found for '{query}'.")

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

# ----------------- Agent -----------------
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 materials, use the search_course_docs tool.\n"
        "When a user asks about schedule, instructor, or location, use the fetch_course_meta tool.\n"
        "Do not use both tools unless absolutely necessary.\n"
        "In your final answer, prefix the response with 'source: chroma' or 'source: mcp_meta' to indicate where the information came from."
    ),
)

# ----------------- CLI -----------------
PRESET_QUESTIONS = [
    "What topics are covered in the first lecture?",
    "When is the next class?",
    "Who is the instructor?",
]

async def run_cli():
    print("Welcome to the Course FAQ Bot!\n")
    for i, q in enumerate(PRESET_QUESTIONS, 1):
        print(f"{i}. {q}")
    print("\nEnter your own question or type 'exit' to quit.")
    while True:
        user_input = input("\n> ")
        if user_input.lower() in {"exit", "quit"}:
            print("Goodbye!")
            break
        result = await agent.ainvoke(
            {"messages": ["HumanMessage(content=\"{}\")".format(user_input)]},
            {"configurable": {"thread_id": "session-1"}},
        )
        # The agent returns a dict with 'messages'; take the last one
        content = result["messages"][-1].content
        print(content)

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