import os
import asyncio
import json
from pathlib import Path
from typing import List

import httpx
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

# ---------------------
# 1. Chroma DB helpers
# ---------------------
CHROMA_PATH = Path("./chroma_faq")
DATA_DIR = Path("./data")


def load_faq_to_chroma() -> None:
    """Load all .md files from data/ into a persistent Chroma store."""
    if CHROMA_PATH.exists():
        # already loaded
        return
    CHROMA_PATH.mkdir(parents=True, exist_ok=True)
    embeddings = OllamaEmbeddings(model="nomic-embed-text")
    db = Chroma.from_folder(str(DATA_DIR), embedding=embeddings, persist_directory=str(CHROMA_PATH))
    db.persist()

@tool
def search_course_docs(query: str, k: int = 3) -> str:
    """Search local course FAQ documents in Chroma DB."""
    load_faq_to_chroma()
    embeddings = OllamaEmbeddings(model="nomic-embed-text")
    db = Chroma(persist_directory=str(CHROMA_PATH), embedding=embeddings)
    docs = db.similarity_search(query, k=k)
    if not docs:
        return "No relevant FAQ found."
    return "\n\n---\n\n".join(doc.page_content for doc in docs)

# ---------------------
# 2. MCP‑style tool
# ---------------------
# For demo we use a local JSON file served by python -m http.server
# The file is located at ./meta/course_meta.json

META_URL = "http://localhost:8000/course_meta.json"

@tool
def fetch_course_meta(query: str) -> str:
    """Fetch course metadata (e.g., schedule) from a mock MCP server."""
    try:
        response = httpx.get(META_URL, timeout=5.0)
        response.raise_for_status()
        data = response.json()
    except Exception as e:
        return f"Error fetching metadata: {e}"
    # Simple keyword search in the JSON
    results: List[str] = []
    for key, value in data.items():
        if query.lower() in key.lower() or query.lower() in str(value).lower():
            results.append(f"{key}: {value}")
    return "\n".join(results) if results else "No metadata matches your query."

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

SYSTEM_PROMPT = (
    "You are a helpful FAQ assistant for the course.\n"
    "When a user asks a question, first decide whether the answer is best found in the local FAQ documents or in the course metadata.\n"
    "If the answer is in the FAQ, use the tool `search_course_docs`.\n"
    "If the answer requires schedule or other metadata, use the tool `fetch_course_meta`.\n"
    "Do not call both tools unless absolutely necessary.\n"
    "In your final response, prepend the source: `source: chroma` or `source: mcp_meta`."
)

agent = create_deep_agent(
    model=llm,
    tools=[search_course_docs, fetch_course_meta],
    backend=backend,
    system_prompt=SYSTEM_PROMPT,
)

# ---------------------
# 4. CLI
# ---------------------
PRESET_QUESTIONS = [
    "What is the deadline for the final project?",  # FAQ
    "When does the next lecture on deep learning start?",  # metadata
    "Explain the concept of attention mechanism.",  # FAQ
]

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

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

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