import os
from fastapi import FastAPI, UploadFile, File, Depends, HTTPException, Form
from fastapi.middleware.cors import CORSMiddleware
from sqlalchemy.orm import Session
from db import Base, engine, get_db
from models import Product, CustomerProfile
from schemas import (
    ProductOut, CustomerOut, CustomerUpdate,
    ChatRequest, ChatResponse, ConversationOut
)
from services import extract_and_upsert_products_from_llm, ensure_default_customers, get_or_create_conversation, add_message, get_history
from typing import List
import tempfile

app = FastAPI(title="engine-ddo", openapi_url="/openapi.json")

# CORS for Streamlit UI Space
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize DB
Base.metadata.create_all(bind=engine)

@app.get("/health")
def health():
    return {"status": "ok"}

# -------- PRODUCTS --------
@app.post("/products/ingest", response_model=List[ProductOut])
async def ingest_products(public_offering: UploadFile = File(...), private_notes: UploadFile = File(...), db: Session = Depends(get_db)):
    # Save temp files to pass paths to the service
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as f1:
        f1.write(await public_offering.read())
        public_path = f1.name
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as f2:
        f2.write(await private_notes.read())
        notes_path = f2.name

    try:
        # Call the new service to process PDFs with an LLM
        extract_and_upsert_products_from_llm(db, public_path, notes_path)
    finally:
        # Clean up the temporary files
        os.remove(public_path)
        os.remove(notes_path)

    # Return all products from the database
    rows = db.query(Product).order_by(Product.name.asc()).all()
    return rows

@app.get("/products/list", response_model=List[ProductOut])
def list_products(db: Session = Depends(get_db)):
    rows = db.query(Product).order_by(Product.name.asc()).all()
    return rows

# -------- CUSTOMERS --------
@app.get("/customers/list", response_model=List[CustomerOut])
def list_customers(db: Session = Depends(get_db)):
    ensure_default_customers(db)
    rows = db.query(CustomerProfile).order_by(CustomerProfile.name.asc()).all()
    return rows

@app.post("/customers/update", response_model=CustomerOut)
def update_customer(payload: CustomerUpdate, db: Session = Depends(get_db)):
    row = db.query(CustomerProfile).filter_by(name=payload.name).first()
    if not row:
        row = CustomerProfile(name=payload.name)
        db.add(row)
    if payload.attributes is not None:
        row.attributes = payload.attributes
    if payload.wcltv is not None:
        row.wcltv = payload.wcltv
    if payload.n is not None:
        row.n = payload.n
    db.commit()
    db.refresh(row)
    return row

# -------- INTERACTIONS (chat) --------

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
MODEL = os.environ.get("LLM_MODEL", "gpt-4o-mini")

async def llm_reply(system_prompt: str, history: list, user_text: str) -> str:
    """Return a reply from an external LLM if OPENAI_API_KEY set, else a rule-based stub."""
    if OPENAI_API_KEY:
        try:
            from openai import OpenAI
            client = OpenAI(api_key=OPENAI_API_KEY)
            messages = [{"role": "system", "content": system_prompt}] + history + [{"role": "user", "content": user_text}]
            resp = client.chat.completions.create(model=MODEL, messages=messages)
            return resp.choices[0].message.content.strip()
        except Exception as e:
            return f"[LLM error fallback] I couldn't reach the model ({e}). Let's continue anyway."
    # Fallback deterministic reply for demo
    return "Thanks for the details! Could you share your main need, budget, and timeline? I can match a product for you."

@app.post("/interactions/chat", response_model=ChatResponse)
async def chat(req: ChatRequest, db: Session = Depends(get_db)):
    profile = req.profile_name or "random"
    convo = get_or_create_conversation(db, profile)
    add_message(db, convo.id, sender="customer", text=req.user_text)

    # Build history for LLM
    hist = []
    for turn in get_history(db, convo.id):
        role = "user" if turn["sender"] == "customer" else "assistant"
        hist.append({"role": role, "content": turn["text"]})

    system_prompt = (
        "You are a helpful sales assistant. Keep answers short, ask clarifying questions, and reference products generically."
    )
    reply = await llm_reply(system_prompt, hist, req.user_text)
    add_message(db, convo.id, sender="agent", text=reply)

    return {"reply": reply, "conversation_id": convo.id}

@app.get("/interactions/history", response_model=ConversationOut)
async def history(profile_name: str, db: Session = Depends(get_db)):
    convo = get_or_create_conversation(db, profile_name)
    hist = get_history(db, convo.id)
    return {"id": convo.id, "profile_name": profile_name, "history": hist}