import os
import asyncio
from pathlib import Path
from langchain_community.embeddings import OllamaEmbeddings
from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain_openai import ChatOpenAI
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
from langchain_core.messages import HumanMessage

# ---------------------------
# 1. Embeddings & Chroma setup
# ---------------------------
# Using OllamaEmbeddings with nomic-embed-text as required by the "Исправить" section.
# The embeddings are used for both loading the FAQ and for the search tool.
embeddings = OllamaEmbeddings(model="nomic-embed-text")

# Persistent Chroma collection for the FAQ knowledge base.
vector_store = Chroma(
    collection_name="faq_collection",
    embedding_function=embeddings,
    persist_directory="./chroma_faq"
)

# ---------------------------
# 2. Load FAQ markdown files into Chroma
# ---------------------------

def load_faq_to_chroma(data_dir: str = "data"):
    """Load all .md files from *data_dir* into the persistent Chroma collection.
    Each file is split into documents with a simple line‑based splitter.
    """
    data_path = Path(data_dir)
    if not data_path.exists():
        raise FileNotFoundError(f"Data directory {data_dir} not found")
    docs = []
    for md_file in data_path.glob("*.md"):
        text = md_file.read_text(encoding="utf-8")
        # Simple split by double newlines to create chunks
        for i, chunk in enumerate(text.split("\n\n")):
            docs.append(Document(page_content=chunk, metadata={"source": md_file.name, "chunk": i}))
    vector_store.add_documents(docs)
    vector_store.persist()

# ---------------------------
# 3. Tools
# ---------------------------
@tool
def search_course_docs(query: str, k: int = 3) -> str:
    """Search the FAQ knowledge base for relevant information."""
    docs = vector_store.similarity_search(query, k=k)
    if not docs:
        return "No relevant information found in the FAQ."
    return "\n\n---\n\n".join(f"**{doc.metadata.get('source')}** (chunk {doc.metadata.get('chunk')}):\n{doc.page_content}" for doc in docs)

@tool
def fetch_course_meta(query: str) -> str:
    """Mock MCP‑style tool that returns course metadata.
    In production this would perform an HTTP GET to an MCP server.
    Here we simply return a static JSON string based on the query.
    """
    # Simple static mapping for demo purposes
    meta = {
        "schedule": "Monday 10:00-12:00, Wednesday 14:00-16:00",
        "instructor": "Dr. Ivanov",
        "credits": "3"
    }
    key = query.lower().strip()
    return meta.get(key, f"No metadata found for '{query}'.")

# ---------------------------
# 4. Agent setup with deepagents
# ---------------------------
# LLM via OpenRouter as per course requirement
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 instructs the agent to choose the appropriate tool and to label the source.
system_prompt = (
    "You are a helpful FAQ assistant.\n"
    "When a user asks a question about course materials, use the tool `search_course_docs`.\n"
    "When a user asks about schedule, instructor, or credits, use the tool `fetch_course_meta`.\n"
    "Do not call both tools unless necessary.\n"
    "In your final answer, prepend the source label: `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,
)

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

async def run_agent(question: str, thread_id: str = "session-1"):
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=question)]},
        {"configurable": {"thread_id": thread_id}},
    )
    # The last message is the agent's response
    return result["messages"][-1].content

async def main():
    # Ensure FAQ is loaded
    load_faq_to_chroma()

    print("--- FAQ Bot Demo ---\n")
    for i, q in enumerate(PRESET_QUESTIONS, 1):
        print(f"Q{i}: {q}")
        answer = await run_agent(q, thread_id=f"demo-{i}")
        print(f"A{i}: {answer}\n")

    # Interactive mode
    print("Enter your own questions (type 'exit' to quit):")
    while True:
        user_input = input("> ")
        if user_input.lower() in {"exit", "quit"}:
            break
        answer = await run_agent(user_input, thread_id="interactive")
        print(answer)

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