# prediction.py

import pandas as pd
import numpy as np
import pickle
import json

from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics.pairwise import cosine_similarity
from scipy.spatial.distance import cdist

# =========================================================
# LOAD FILES
# =========================================================
# Memuat semua file yang dibutuhkan:
# - kmeans_model.pkl  : model clustering yang sudah dilatih
# - app_metadata.json : metadata seperti nama fitur, label cluster, dan range harga
# - df_clean.csv      : dataset smartphone yang sudah dibersihkan
# - feat_df_clean.csv : dataset fitur yang sudah dinormalisasi untuk similarity

with open("kmeans_model.pkl", "rb") as f:
    kmeans = pickle.load(f)

with open("app_metadata.json") as f:
    meta = json.load(f)

df = pd.read_csv("df_clean.csv", index_col=0)
feat_df = pd.read_csv("feat_df_clean.csv", index_col=0)

FEATURE_COLS = meta["FEATURE_COLS"]

CLUSTER_LABELS = {
    int(k): v
    for k, v in meta["CLUSTER_LABELS"].items()
}

price_min = meta["price_min"]
price_max = meta["price_max"]

# =========================================================
# PRIORITY WEIGHTS
# =========================================================
# Setiap priority memiliki bobot yang berbeda untuk setiap fitur.
# Bobot ini menentukan fitur mana yang paling penting saat menghitung
# kecocokan antara preferensi user dengan smartphone.
#
# Contoh: priority "gaming" memberikan bobot tertinggi ke cpu_tier_norm
# dan ram_norm karena performa CPU dan RAM paling penting untuk gaming.

PRIORITY_WEIGHTS = {
    'gaming': {
        'cpu_tier_norm': 0.35,    # raw processing power is #1 for gamers
        'ram_norm':      0.30,    # games need memory for loading assets
        'battery_norm':  0.25,    # games are battery-hungry
        'price_to_perf': 0.25,    # gaming phones can be pricey; value matters
        'camera_norm':   0.05,    # camera is secondary for gamers
        'battery_efficiency': 0.05,
    },
    
    'camera': {
        'camera_norm':        0.55,  # dominant: highest MP sensor is the main ask
        'cpu_tier_norm':      0.15,  # camera processing pipeline needs a decent chip
        'ram_norm':           0.10,
        'battery_norm':       0.10,
        'battery_efficiency': 0.10,
        'price_to_perf':      0.10,
    },
    
    'budget': {
        'battery_norm':       0.15,
        'battery_efficiency': 0.25,
        'price_to_perf':      0.40,
        'ram_norm':           0.10,
        'cpu_tier_norm':      0.05,
        'camera_norm':        0.05,
    },
    
    'balanced': {
        'cpu_tier_norm':      0.20,
        'ram_norm':           0.20,
        'price_to_perf':      0.20,
        'battery_norm':       0.15,
        'camera_norm':        0.20,
        'battery_efficiency': 0.10,
    },
    
}

# Mapping priority ke nama segmen cluster tertentu
# Gaming dan camera langsung diarahkan ke cluster yang relevan
PRIORITY_TO_SEGMENT = {
    'gaming': 'Gaming / Performance',
    'camera': 'Camera-focused',
    'budget': 'Budget',
    'flagship': 'Flagship',
}

# =========================================================
# PRICE SCALER
# =========================================================
# MinMaxScaler digunakan untuk mengubah nilai harga (USD) ke skala 0-1
# agar bisa dibandingkan dengan fitur-fitur lain yang sudah dinormalisasi.
# Scaler di-fit dengan nilai min dan max harga dari dataset.

price_scaler = MinMaxScaler()
price_scaler.fit([
    [price_min],
    [price_max]
])

# =========================================================
# BUILD USER VECTOR
# =========================================================
# Fungsi ini membuat vektor preferensi user berdasarkan:
# - budget_usd : anggaran user dalam USD
# - priority   : fokus utama user (gaming/camera/battery/balanced)
#
# Vektor ini nantinya akan dibandingkan dengan vektor fitur setiap
# smartphone menggunakan cosine similarity.

def build_user_vector(
    budget_usd: float,
    priority: str
) -> np.ndarray:

    # Ambil bobot sesuai priority user
    weights = PRIORITY_WEIGHTS.get(
        priority,
        PRIORITY_WEIGHTS['balanced']
    )

    max_w = max(weights.values())

    # Normalisasi budget user ke skala 0-1
    budget_pos = float(
        np.clip(
            price_scaler.transform([[budget_usd]])[0][0],
            0.0,
            1.0
        )
    )

    vec = np.zeros(len(FEATURE_COLS))

    for i, feat in enumerate(FEATURE_COLS):

        if feat == 'price_norm':
            # Posisi harga user dalam skala dataset
            vec[i] = budget_pos

        elif feat == 'price_to_perf':
            # User dengan budget rendah cenderung lebih memperhatikan
            # nilai per harga, sehingga nilainya dibalik (1 - budget_pos)
            w = weights.get(feat, 0.0) / max_w
            vec[i] = w * (1.0 - budget_pos)

        else:
            # Fitur lain dinormalisasi berdasarkan bobotnya
            w = weights.get(feat, 0.0)
            vec[i] = (w / max_w) if w > 0 else 0.5

    return vec

# =========================================================
# RECOMMEND PHONES
# =========================================================
# Fungsi utama rekomendasi dengan tambahan filter spesifikasi.
#
# Parameter:
# - budget_usd    : anggaran maksimum user (USD)
# - priority      : fokus utama (gaming/camera/battery/balanced)
# - top_n         : jumlah rekomendasi yang ditampilkan
# - min_ram       : minimal RAM yang diinginkan (GB), default 0 = tidak difilter
# - min_camera_mp : minimal kamera utama (MP), default 0 = tidak difilter
# - min_battery   : minimal kapasitas baterai (mAh), default 0 = tidak difilter
#
# Alur kerja:
# 1. Bangun vektor preferensi user
# 2. Tentukan cluster terbaik berdasarkan priority
# 3. Filter smartphone berdasarkan budget + spesifikasi minimum
# 4. Hitung cosine similarity antara vektor user dan vektor setiap smartphone
# 5. Hitung proximity (seberapa dekat harga smartphone dengan budget user)
# 6. Gabungkan similarity dan proximity menjadi Match Score
# 7. Kembalikan top-N smartphone dengan Match Score tertinggi

def recommend_phones(
    budget_usd: float,
    priority: str,
    top_n: int = 5,
    min_ram: int = 0,
    min_camera_mp: int = 0,
    min_battery: int = 0,
    min_year: int = 0,
):

    # -----------------------------------------
    # STEP 1: Bangun vektor preferensi user
    # -----------------------------------------
    user_vec = build_user_vector(budget_usd, priority).reshape(1, -1)

    # -----------------------------------------
    # STEP 2: Tentukan cluster terbaik
    # -----------------------------------------
    reverse_lbl = {
        label: cid
        for cid, label in CLUSTER_LABELS.items()
    }

    if priority in PRIORITY_TO_SEGMENT:
        # Untuk gaming dan camera, langsung arahkan ke cluster yang sesuai
        best_cluster = reverse_lbl.get(
            PRIORITY_TO_SEGMENT[priority],
            int(np.argmin(cdist(user_vec, kmeans.cluster_centers_)[0]))
        )
    else:
        # Untuk balanced dan battery, cari cluster terdekat dari vektor user
        best_cluster = int(
            np.argmin(
                cdist(user_vec, kmeans.cluster_centers_, metric='euclidean')[0]
            )
        )

    seg_name = CLUSTER_LABELS[best_cluster]

    # -----------------------------------------
    # STEP 3: Filter berdasarkan budget + spesifikasi minimum
    # -----------------------------------------
    in_cluster = df['cluster_kmeans'] == best_cluster
    in_budget  = df['price'] <= budget_usd

    # Filter spesifikasi minimum dari user
    in_ram     = df['ram'] >= min_ram             if min_ram > 0        else True
    in_camera  = df['main_camera_mp'] >= min_camera_mp if min_camera_mp > 0 else True
    in_battery = df['battery_capacity'] >= min_battery if min_battery > 0   else True
    in_year    = df['launched year'] >= min_year if min_year > 0 else True

    pool = df[in_cluster & in_budget & in_ram & in_camera & in_battery & in_year].copy()

    # Jika tidak ada smartphone yang memenuhi semua kriteria dalam cluster,
    # perluas pencarian ke semua cluster (tetapi tetap filter spesifikasi)
    if pool.empty:
        pool = df[in_budget & in_ram & in_camera & in_battery & in_year].copy()
        seg_name += " (all clusters)"

    # Jika masih kosong setelah filter spesifikasi, kembalikan pesan kosong
    if pool.empty:
        return pd.DataFrame(), seg_name + " — no phones match your filters"

    # -----------------------------------------
    # STEP 4 & 5: Hitung cosine similarity + proximity harga
    # -----------------------------------------
    ALPHA = 0.4  # Bobot proximity harga terhadap similarity (40% proximity, 60% similarity)

    pool_X = feat_df.loc[pool.index, FEATURE_COLS].values

    # Cosine similarity: mengukur seberapa "searah" vektor user dengan vektor smartphone
    cos_sims = cosine_similarity(user_vec, pool_X)[0]

    # Proximity: mengukur seberapa dekat harga smartphone dengan budget user
    # Semakin dekat harganya ke budget, semakin tinggi nilai proximity-nya
    proximity = np.clip(
        1.0 - np.abs(budget_usd - pool['price'].values) / (budget_usd + 1e-6),
        0, 1
    )

    # -----------------------------------------
    # STEP 6: Gabungkan menjadi Match Score
    # -----------------------------------------
    pool['Match Score (%)'] = (
        cos_sims * (ALPHA * proximity + (1.0 - ALPHA)) * 100
    ).round(1)

    # -----------------------------------------
    # STEP 7: Ambil top-N hasil terbaik
    # -----------------------------------------
    result_cols = [
        'brand', 'model', 'price',
        'ram', 'battery_capacity', 'main_camera_mp',
        'Match Score (%)'
    ]
    result_cols = [c for c in result_cols if c in pool.columns]

    result = (
        pool
        .nlargest(top_n, 'Match Score (%)')[result_cols]
        .reset_index(drop=True)
    )
    result.index += 1

    return result, seg_name