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 API key from .env or environment variable
os.environ.setdefault("OPENAI_API_KEY", os.getenv("OPENAI_API_KEY", ""))

# LLM via 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,
)

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

# ----------------- Chroma DB -----------------
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 (idempotent)
DATA_DIR = Path("./data")
if not CHROMA_PATH.exists() or not any(CHROMA_PATH.iterdir()):
    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.
    """
    meta = {
        "schedule": "Mon 10-12, Wed 14-16, Fri 9-11",
        "instructor": "Dr. Ivanov",
        "credits": 3,
    }
    # Simple keyword matching
    if "schedule" in query.lower():
        return f"Course schedule: {meta['schedule']}"
    if "instructor" in query.lower():
        return f"Instructor: {meta['instructor']}"
    if "credits" in query.lower():
        return f"Credits: {meta['credits']}"
    return "No metadata matches your 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. Use the search_course_docs tool for questions about lecture materials and fetch_course_meta for questions about schedule, instructor, or credits. Do not use both tools unless necessary. In your final answer, prefix the source with 'source: chroma' or 'source: mcp_meta'.",
)

# ----------------- CLI -----------------
PRESET_QUESTIONS = [
    "What topics are covered in lecture 3?",
    "When is the next class?",
    "Who is the instructor for this course?",
]

async def run_agent(question: str, thread_id: str = "session-1"):
    result = await agent.ainvoke(
        {"messages": ["HumanMessage(content=\"{}\")".format(question)]},
        {"configurable": {"thread_id": thread_id}},
    )
    # The agent returns a dict with 'messages'; extract last content
    content = result["messages"][-1].content
    print(f"\nQ: {question}\nA: {content}\n")

async def main():
    print("--- FAQ Bot Demo ---")
    for i, q in enumerate(PRESET_QUESTIONS, 1):
        await run_agent(q, thread_id=f"demo-{i}")
    print("Enter your own question (or 'exit' to quit):")
    while True:
        q = input("> ")
        if q.lower() in {"exit", "quit"}:
            break
        await run_agent(q, thread_id="interactive")

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