src/rag/chatbot.py

import os
import requests
from src.rag.router import classify_query, extract_filters
from src.rag.retriever import retrieve_faq, load_vector_store
from src.rag.searcher import (
    search_properties, get_leases_expiring,
    get_leases_vacant_or_pending
)

GROQ_MODEL = "llama-3.3-70b-versatile"

SYSTEM_PROMPT = """You are a helpful AI assistant for a resale real estate business in Mumbai, India.

RULES:
- Answer ONLY from the data provided to you in this prompt.
- Never invent prices, features, lease status, or property details.
- If you cannot find the info, say: "I can't find this in our system right now. Please check with our sales team."
- Be friendly, short, and clear.
- Use Indian number formatting: ₹1,20,00,000 (1.2 crore) or ₹85 lakh (₹85,00,000).
- After property listings, always add: "For a site visit or more details, WhatsApp us or contact our sales team."
- After FAQ answers, optionally add: "If you need more info, feel free to ask."
- For manager queries, be factual and structured with tables.
"""


def _fmt_price(price_inr: float) -> str:
    """Format price in Indian notation."""
    if price_inr >= 10000000:
        return f"₹{price_inr/10000000:.2f} Cr"
    else:
        return f"₹{price_inr/100000:.1f} L"


def format_properties(props: list[dict]) -> str:
    if not props:
        return "We don't have any properties matching your filters in current listings."
    lines = []
    for i, p in enumerate(props, 1):
        lines.append(
            f"{i}. **{p['title']}**\n"
            f"   • {p['bhk']} BHK | {p['property_type']} | {p['area_sqft']} sq ft\n"
            f"   • Price: {_fmt_price(p['price_inr'])} | Floor: {p['floor']}/{p['total_floors']}\n"
            f"   • Location: {p['location']}, {p['society']}\n"
            f"   • Furnishing: {p['furnishing']} | Parking: {p['parking']} | Grade: {p['condition_grade']}\n"
            f"   • Amenities: {p['amenities']}"
        )
    return "\n\n".join(lines)


def format_lease_table(records: list[dict]) -> str:
    if not records:
        return "No records found for this query."
    lines = [
        "| Prop ID | Property | Location | Rent/mo | Status | Lease End | Follow-up |",
        "|---------|----------|----------|---------|--------|-----------|-----------|"
    ]
    for r in records:
        rent = f"₹{r['monthly_rent']:,}" if r.get('monthly_rent') else "—"
        lease_end = r.get('lease_end') or "—"
        lines.append(
            f"| {r['property_id']} | {r['title']} ({r['bhk']}BHK) "
            f"| {r['location']} | {rent} "
            f"| {r['lease_status']} | {lease_end} | {r['followup_person']} |"
        )
    return "\n".join(lines)


def call_groq(api_key: str, messages: list) -> str:
    url = "https://api.groq.com/openai/v1/chat/completions"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": GROQ_MODEL,
        "messages": messages,
        "max_tokens": 1024,
        "temperature": 0.3
    }
    resp = requests.post(url, json=payload, headers=headers, timeout=30)
    if resp.status_code != 200:
        raise ValueError(f"Groq API error {resp.status_code}: {resp.text[:200]}")
    return resp.json()["choices"][0]["message"]["content"]


def chat(query: str, role: str = "customer", history: list = None) -> dict:
    query_type = classify_query(query, role)
    context = ""
    properties = []
    lease_records = []

    if query_type == "property_filter":
        filters = extract_filters(query)
        properties = search_properties(**filters)
        prop_text = format_properties(properties)
        context = f"PROPERTY INVENTORY RESULTS:\n{prop_text}"

    elif query_type == "lease" and role == "manager":
        q_lower = query.lower()
        if "vacant" in q_lower or "expired" in q_lower or "pending" in q_lower:
            lease_records = get_leases_vacant_or_pending()
        else:
            days = 30
            if "15 days" in q_lower:
                days = 15
            elif "week" in q_lower:
                days = 7
            elif "month" in q_lower or "30 days" in q_lower:
                days = 30
            elif "60 days" in q_lower or "2 months" in q_lower:
                days = 60
            lease_records = get_leases_expiring(days)
        table = format_lease_table(lease_records)
        context = f"LEASE DATA:\n{table}"

    elif query_type == "faq":
        faq_context = retrieve_faq(query)
        context = f"FAQ KNOWLEDGE BASE:\n{faq_context}"

    # Build messages for Groq (OpenAI-compatible)
    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
    if history:
        for h in history[-6:]:
            msg_role = "user" if h["role"] == "user" else "assistant"
            messages.append({"role": msg_role, "content": h["content"]})

    user_content = f"CONTEXT:\n{context}\n\nUSER QUERY: {query}"
    messages.append({"role": "user", "content": user_content})

    try:
        api_key = os.environ.get("GROQ_API_KEY", "").strip()
        if not api_key:
            raise ValueError(
                "GROQ_API_KEY is not set. Go to Space Settings → Repository Secrets and add GROQ_API_KEY."
            )
        reply = call_groq(api_key, messages)
    except Exception as e:
        reply = f"⚠️ AI unavailable: {str(e)[:200]}"

    return {
        "reply": reply,
        "query_type": query_type,
        "properties": properties,
        "lease_records": lease_records,
    }