import asyncio, os
from pathlib import Path
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from langchain_chroma import Chroma
from langchain_ollama import OllamaEmbeddings
import httpx
import json

# ---------- 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(),
])

# ---------- Chroma DB ----------
CHROMA_PATH = Path("./chroma_faq")
CHROMA_PATH.mkdir(exist_ok=True)

embeddings = OllamaEmbeddings(model="nomic-embed-text")

# Load or create vector store
if CHROMA_PATH.exists() and any(CHROMA_PATH.iterdir()):
    chroma = Chroma(persist_directory=str(CHROMA_PATH), embedding_function=embeddings)
else:
    # Load markdown files
    docs = []
    for md_file in Path("data").glob("*.md"):
        text = md_file.read_text(encoding="utf-8")
        docs.append(text)
    chroma = Chroma.from_texts(docs, embedding=embeddings, persist_directory=str(CHROMA_PATH))
    chroma.persist()

@tool
def search_course_docs(query: str, k: int = 3) -> str:
    """Search local course documents in Chroma."""
    results = chroma.similarity_search(query, k=k)
    return "\n---\n".join(doc.page_content for doc in results) if results else "No relevant docs found."

# ---------- MCP‑style tool ----------
# For demo we use a static JSON file. In production this would be an HTTP call.
META_JSON = Path("meta.json")
if not META_JSON.exists():
    # Create a simple mock meta file
    META_JSON.write_text(json.dumps({
        "schedule": "Mon 10-12, Wed 14-16, Fri 9-11",
        "instructor": "Dr. Smith",
        "location": "Room 101"
    }))

@tool
def fetch_course_meta(query: str) -> str:
    """Return course metadata matching the query keyword."""
    data = json.loads(META_JSON.read_text())
    # Simple keyword search in values
    for key, value in data.items():
        if query.lower() in key.lower() or query.lower() in str(value).lower():
            return f"{key}: {value}"
    return "No metadata found for the query."

# ---------- 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"
        "If the question is about course content, use search_course_docs.\n"
        "If the question is about schedule, instructor, or location, use fetch_course_meta.\n"
        "Do not call both tools unless necessary.\n"
        "In your answer, prefix the source with 'source: chroma' or 'source: mcp_meta'."
    ),
)

# ---------- CLI ----------
PRESET_QUESTIONS = [
    "What is the main topic of the first lecture?",
    "How can I access the lecture slides?",
    "When is the next class?"
]

async def run_cli():
    print("--- FAQ Bot CLI ---")
    for i, q in enumerate(PRESET_QUESTIONS, 1):
        print(f"\nPreset {i}: {q}")
        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=q)]},
            {"configurable": {"thread_id": f"preset-{i}"}},
        )
        print(result["messages"][-1].content)
    print("\nEnter your own question (or 'exit'): ")
    while True:
        user_q = input("> ")
        if user_q.lower() in {"exit", "quit"}:
            break
        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=user_q)]},
            {"configurable": {"thread_id": "interactive"}},
        )
        print(result["messages"][-1].content)

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