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 ----------
DATA_DIR = Path("data")
CHROMA_DIR = Path("./chroma_faq")
CHROMA_COLLECTION = "faq_collection"
MOCK_META_URL = "http://localhost:8000/meta.json"  # change if you use a different mock

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

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,
)

# ---------- Chroma setup ----------
vector_store = Chroma(
    collection_name=CHROMA_COLLECTION,
    embedding_function=embeddings,
    persist_directory=str(CHROMA_DIR),
)

# ---------- Load FAQ into Chroma ----------

def load_faq_to_chroma():
    if not CHROMA_DIR.exists():
        CHROMA_DIR.mkdir(parents=True, exist_ok=True)
    # If collection already exists, skip loading
    if vector_store.get_collection().count() > 0:
        return
    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, k: int = 3) -> str:
    """Search local course documents in Chroma and return top k snippets."""
    results = vector_store.similarity_search(query, k=k)
    snippets = [f"{res.metadata.get('source', 'unknown')}\n{res.page_content[:200]}..." for res in results]
    return "\n\n".join(snippets)

@tool
def fetch_course_meta(query: str) -> str:
    """Mock MCP tool that fetches course metadata from a local JSON endpoint."""
    try:
        response = httpx.get(MOCK_META_URL, timeout=5)
        response.raise_for_status()
        data = response.json()
        # Simple filtering: return items that contain the query string (case-insensitive)
        matches = [item for item in data if query.lower() in json.dumps(item).lower()]
        return json.dumps(matches, indent=2) if matches else "No metadata found for the query."
    except Exception as e:
        return f"Error fetching metadata: {e}"

# ---------- Backend ----------
backend = CompositeBackend(
    default=LocalShellBackend(root_dir="./workspace", virtual_mode=True, inherit_env=True),
    routes={},
)

# ---------- Agent ----------
agent = create_deep_agent(
    model=llm,
    tools=[search_course_docs, fetch_course_meta],
    backend=backend,
    system_prompt="You are a helpful course assistant. Use the search_course_docs tool for questions about lecture materials and fetch_course_meta for schedule or metadata questions. In your answer, prepend 'source: chroma' or 'source: mcp_meta' to indicate the used tool.",
)

# ---------- CLI ----------
PRESET_QUESTIONS = [
    "What topics are covered in Lecture 3?",  # should use chroma
    "Explain the concept of tokenization in NLP.",  # chroma
    "When is the next midterm exam scheduled?",  # mcp_meta
]

async def run_agent(question: str, thread_id: str = "session-1"):
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=question)]},
        {"configurable": {"thread_id": thread_id}},
    )
    return result["messages"][-1].content

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

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