import json
import re
import os
import joblib
import pandas as pd
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory

# =========================================================
# OPTIONAL GEMINI
# =========================================================
import os

GEMINI_ENABLED = False
gemini_client = None

try:
    from google import genai

    api_key = os.getenv("GEMINI_API_KEY")

    if api_key:
        gemini_client = genai.Client(api_key=api_key)
        GEMINI_ENABLED = True
        print("Gemini chatbot enabled ✅")
    else:
        print("No GEMINI_API_KEY found — fallback to rules only.")

except Exception as e:
    print("Gemini init failed:", e)
    GEMINI_ENABLED = False
    gemini_client = None
    
# =========================================================
# IMPORT INFERENCE HELPERS
# =========================================================
try:
    from inference_core import (
        predict_price,
        calibrate_prediction,
        recommend_locations,
        get_recommended_area_examples,
        get_target_area_overview,
    )
except Exception as e:
    print("chatbot import error:", e)


# =========================================================
# LOAD DATA
# =========================================================
bandung_listings = pd.read_csv("bandung_listing_features.csv")

factory = StemmerFactory()
stemmer = factory.create_stemmer()


# =========================================================
# HELPERS
# =========================================================
def fmt_price(val_juta):
    if val_juta is None or pd.isna(val_juta):
        return "—"
    if val_juta >= 1000:
        return f"Rp {val_juta/1000:.2f} Miliar"
    return f"Rp {val_juta:,.0f} Juta"


def extract_location(text):
    lowered = text.lower()
    for loc in bandung_listings["location"].dropna().unique():
        if loc.lower() in lowered:
            return loc
    return None


def extract_number(text):
    match = re.search(r"(\d[\d.,]*)", text)
    if not match:
        return None
    return float(match.group(1).replace(",", "."))


def extract_bedrooms(text):
    match = re.search(r"(\d+)\s*(kamar|bed)", text.lower())
    if match:
        return int(match.group(1))
    return None


def gemini_chat_response(user_msg, history):
    if not GEMINI_ENABLED or gemini_client is None:
        return None

    try:
        prompt = f"""
You are a smart Bandung housing advisor.

User: {user_msg}

Rules:
- Respond in natural, complete sentences
- Be helpful and specific (location, price range, or insight)
- Keep it concise but NOT robotic
- Avoid one-word or vague answers
- Max 2–3 sentences
- Under 60 words
"""

        if history:
            for turn in history[-4:]:
                prompt += f"{turn['role']}: {turn['content']}\n"

        prompt += f"user: {user_msg}"

        response = gemini_client.models.generate_content(
            model="gemini-2.5-flash-lite",
            contents=prompt,
            config={
                "max_output_tokens": 60,
                "temperature": 0.3,
                "top_p": 0.95
            }
        )

        if hasattr(response, "text") and response.text:
            return response.text.strip()

    except Exception as e:
        print("Gemini failed, fallback to rules:", e)

    return None


# =========================================================
# STATE
# =========================================================
EMPTY_STATE = json.dumps(
    {
        "location": None,
        "bedroom_total": None,
        "building_area_m2": None,
        "budget": None,
    }
)


# =========================================================
# RULE-BASED FALLBACK
# =========================================================
def rule_chat(user_msg, history, state_str):
    state = json.loads(state_str)

    text = user_msg.strip()

    location = extract_location(text)
    bedrooms = extract_bedrooms(text)
    number = extract_number(text)

    if location:
        state["location"] = location

    if bedrooms:
        state["bedroom_total"] = bedrooms

    if number and "budget" in text.lower():
        state["budget"] = number

    lowered = text.lower()

    # =========================
    # PRICE
    # =========================
    if any(w in lowered for w in ["harga", "price", "estimasi", "berapa"]):
        if not state.get("location"):
            bot_msg = "Area mana yang ingin dicek? Contoh: Dago, Buah Batu."
        elif not state.get("bedroom_total"):
            bot_msg = "Berapa kamar tidur?"
        else:
            try:
                features = {
                    "location": state["location"],
                    "building_type": "Rumah",
                    "bedroom_total": state["bedroom_total"],
                    "bathroom_total": 2,
                    "carport_total": 1,
                    "building_area_m2": 120,
                }

                pred = predict_price(features)
                result = calibrate_prediction(pred)

                bot_msg = (
                    f"📍 **{state['location']}**\n"
                    f"💰 Estimasi harga: **{fmt_price(result['Predicted Price (IDR Mn)'])}**\n"
                    f"📉 Range: {fmt_price(result['Lower Range'])} — {fmt_price(result['Upper Range'])}"
                )

            except Exception as e:
                bot_msg = f"Prediction error: {e}"

    # =========================
    # RECOMMEND
    # =========================
    elif any(w in lowered for w in ["alternatif", "recommend", "mirip"]):
        if not state.get("location"):
            bot_msg = "Area targetnya apa dulu?"
        else:
            try:
                recs = recommend_locations(
                    {"location": state["location"]},
                    top_n=5
                )

                lines = [f"📍 Area mirip dengan **{state['location']}**:"]
                for _, row in recs.iterrows():
                    lines.append(
                        f"- {row['Alternative Area']} "
                        f"({row['Similarity Score']:.3f})"
                    )

                bot_msg = "\n".join(lines)

            except Exception as e:
                bot_msg = f"Recommendation error: {e}"

    # =========================
    # GENERAL
    # =========================
    else:
        bot_msg = (
            "Hi, aku bisa bantu:\n"
            "- estimasi harga properti\n"
            "- area alternatif\n"
            "- market context Bandung\n\n"
            "Contoh: *harga rumah 3 kamar di Dago*"
        )

    history = history + [
        {"role": "user", "content": user_msg},
        {"role": "assistant", "content": bot_msg},
    ]

    return history, json.dumps(state)


# =========================================================
# MAIN CHAT
# =========================================================
def chat(user_msg, history, state_str):
    if history is None:
        history = []

    if not state_str:
        state_str = EMPTY_STATE

    text = user_msg.strip()

    if not text:
        return history, state_str

    # Gemini first
    gemini_reply = gemini_chat_response(user_msg, history)
    print("GEMINI CHAT REPLY:", gemini_reply)

    if gemini_reply and len(gemini_reply.split()) >= 5:
        history = history + [
            {"role": "user", "content": user_msg},
            {"role": "assistant", "content": gemini_reply},
        ]
        return history, state_str

    # fallback
    return rule_chat(user_msg, history, state_str)