import asyncio
import os
import json
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
# ------------------------------------------------------------------
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 (OpenRouter only)
# ------------------------------------------------------------------
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,
)

# ------------------------------------------------------------------
# Chroma vector store (persisted)
# ------------------------------------------------------------------
CHROMA_PATH = Path("./chroma_faq")
vector_store = Chroma(
    collection_name="faq_collection",
    embedding_function=embeddings,
    persist_directory=str(CHROMA_PATH),
)

# ------------------------------------------------------------------
# Utility: load markdown files into Chroma
# ------------------------------------------------------------------
async def load_faq_to_chroma(md_dir: str = "data"):
    md_dir = Path(md_dir)
    if not md_dir.is_dir():
        raise FileNotFoundError(f"Markdown directory {md_dir} not found")
    docs = []
    for md_file in md_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()
    print(f"Loaded {len(docs)} documents into Chroma (persisted at {CHROMA_PATH})")

# ------------------------------------------------------------------
# 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')}] {doc.page_content}" for doc in results)

@tool
def fetch_course_meta(query: str) -> str:
    """Simulate an MCP-style tool that fetches course metadata.
    In production this would be a real HTTP call to an MCP server.
    Here we use a local JSON file as a mock response.
    """
    meta_file = Path("meta.json")
    if not meta_file.is_file():
        return "Metadata source not available."
    data = json.loads(meta_file.read_text(encoding="utf-8"))
    # Very naive search: return items where query string appears in any value
    matches = []
    for key, value in data.items():
        if isinstance(value, str) and query.lower() in value.lower():
            matches.append(f"{key}: {value}")
    if not matches:
        return "No metadata matches found."
    return "\n".join(matches)

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

# ------------------------------------------------------------------
# DeepAgent creation
# ------------------------------------------------------------------
agent = create_deep_agent(
    model=llm,
    tools=[search_course_docs, fetch_course_meta],
    backend=backend,
    system_prompt=(
        "You are a helpful FAQ assistant for the course. "
        "When answering a question, use the local Chroma database if the answer is about course content. "
        "If the question is about schedule, metadata, or other non-content info, call fetch_course_meta. "
        "Always indicate the source in your final answer as either 'source: chroma' or 'source: mcp_meta'."
    ),
)

# ------------------------------------------------------------------
# CLI helpers
# ------------------------------------------------------------------
PRESET_QUESTIONS = [
    "What topics are covered in the first lecture?",
    "Explain the concept of recursion as described in the notes.",
    "When is the next lab session scheduled?",
]

async def run_interactive():
    print("--- FAQ Bot CLI ---")
    print("Type 'exit' to quit.")
    while True:
        user_input = input("\nQuestion: ")
        if user_input.lower() in {"exit", "quit"}:
            break
        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=user_input)]},
            {"configurable": {"thread_id": "session-1"}},
        )
        # The last message is the assistant's reply
        reply = result["messages"][-1].content
        print("\nAnswer:\n", reply)

async def run_presets():
    for q in PRESET_QUESTIONS:
        print("\nQuestion:", q)
        result = await agent.ainvoke(
            {"messages": [HumanMessage(content=q)]},
            {"configurable": {"thread_id": "session-1"}},
        )
        reply = result["messages"][-1].content
        print("Answer:\n", reply)

# ------------------------------------------------------------------
# Main entry point
# ------------------------------------------------------------------
async def main():
    # Load data into Chroma if not already persisted
    if not CHROMA_PATH.is_dir() or not any(CHROMA_PATH.iterdir()):
        await load_faq_to_chroma()
    # Run preset questions first
    await run_presets()
    # Then interactive mode
    await run_interactive()

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