# cear_model.py

import os
import json
import numpy as np
import pandas as pd

# ---------------- Weight loading ---------------- #

def _load_platform_weights() -> dict:
    """
    Load platform weights from platform_weights.json.
    Supports multiple key schemes:
      - W_C / W_A
      - trend_weight / risk_weight
      - C_weight / A_weight
    """
    script_dir = os.path.dirname(os.path.abspath(__file__))
    json_path = os.path.join(script_dir, "platform_weights.json")

    if not os.path.exists(json_path):
        print("WARNING: platform_weights.json not found. Using default weights.")
        # Sensible defaults if file missing
        return {
            "tiktok":    {"W_C": 1.00, "W_A": 1.00},
            "instagram": {"W_C": 0.80, "W_A": 0.90},
            "youtube":   {"W_C": 0.60, "W_A": 0.60},
            "twitter":   {"W_C": 0.70, "W_A": 0.80},
            "reddit":    {"W_C": 0.50, "W_A": 0.50},
            "facebook":  {"W_C": 0.30, "W_A": 0.40},
            "other":     {"W_C": 0.20, "W_A": 0.30},
        }

    with open(json_path, "r", encoding="utf-8") as f:
        raw = json.load(f)

    # Normalize key names into W_C and W_A
    norm = {}
    for platform, vals in raw.items():
        if not isinstance(vals, dict):
            vals = {}
        w_c = (
            vals.get("W_C")
            or vals.get("c_weight")
            or vals.get("C_weight")
            or vals.get("trend_weight")
            or 0.0
        )
        w_a = (
            vals.get("W_A")
            or vals.get("a_weight")
            or vals.get("A_weight")
            or vals.get("risk_weight")
            or 0.0
        )
        norm[platform.lower()] = {"W_C": float(w_c), "W_A": float(w_a)}

    return norm


PLATFORM_WEIGHTS = _load_platform_weights()


class CEARModel:
    """
    Core CEAR scoring model.

    Inputs:
        user_df: DataFrame with columns:
            - 'platform_name': str
            - 'minutes_per_week': numeric
            - optional 'variety_score': numeric (0–10)

        satisfaction: optional float (0–10)
        fomo:         optional float (0–10)

    Returns dict:
        {
          "C_Score": float,
          "A_Risk": float,
          "D_Index": float,
          "Avg_Variety": float | None,
          "Satisfaction": float | None,
          "FOMO": float | None,
          "Per_Platform_Efficiency": [
              {"platform_name": str, "Cultural_Efficiency": float}, ...
          ]
        }
    """

    def __init__(self, weights: dict | None = None) -> None:
        self.weights = weights if weights is not None else PLATFORM_WEIGHTS

    # ---------- internals ---------- #

    @staticmethod
    def _diminishing_returns(minutes: float) -> float:
        """Log10-based diminishing returns on minutes."""
        minutes = max(float(minutes), 0.0)
        return float(np.log10(minutes + 1.0))

    def _weights_dataframe(self) -> pd.DataFrame:
        if not self.weights:
            return pd.DataFrame(columns=["platform_name", "W_C", "W_A"])

        w_df = pd.DataFrame.from_dict(self.weights, orient="index")
        w_df.index = w_df.index.astype(str).str.lower()
        w_df.index.name = "platform_name"
        w_df = w_df.reset_index()

        # Ensure W_C / W_A exist even if missing
        if "W_C" not in w_df.columns:
            w_df["W_C"] = 0.0
        if "W_A" not in w_df.columns:
            w_df["W_A"] = 0.0

        return w_df[["platform_name", "W_C", "W_A"]]

    # ---------- public API ---------- #

    def calculate_scores(
        self,
        user_df: pd.DataFrame,
        satisfaction: float | None = None,
        fomo: float | None = None,
    ) -> dict:
        if user_df is None or user_df.empty:
            return {
                "C_Score": 0.0,
                "A_Risk": 0.0,
                "D_Index": 0.0,
                "Avg_Variety": None,
                "Satisfaction": satisfaction,
                "FOMO": fomo,
                "Per_Platform_Efficiency": [],
            }

        df = user_df.copy()

        # Normalize names and convert minutes
        df["platform_name"] = (
            df["platform_name"].astype(str).str.strip().str.lower()
        )
        df["minutes_per_week"] = pd.to_numeric(
            df["minutes_per_week"], errors="coerce"
        ).fillna(0.0)
        df["minutes_per_week"] = df["minutes_per_week"].clip(lower=0.0)

        # Attach weights
        w_df = self._weights_dataframe()
        df = df.merge(w_df, on="platform_name", how="left")
        df[["W_C", "W_A"]] = df[["W_C", "W_A"]].fillna(0.0)

        total_mins = float(df["minutes_per_week"].sum())

        # 1. Core contributions
        df["C_Contrib"] = df.apply(
            lambda row: row["W_C"] * self._diminishing_returns(row["minutes_per_week"]),
            axis=1,
        )
        df["A_Contrib"] = df["W_A"] * df["minutes_per_week"]

        C_Score = float(df["C_Contrib"].sum())
        A_Risk = float(df["A_Contrib"].sum())

        # 2. D-Index (effective number of platforms via inverse Herfindahl)
        if total_mins > 0:
            shares = df["minutes_per_week"] / total_mins
            H = float((shares**2).sum())
            D_Index = float(1.0 / H) if H > 0 else 0.0
        else:
            D_Index = 0.0

        # 3. Per-platform cultural efficiency (scaled 0–100)
        df["Cultural_Efficiency"] = df["C_Contrib"] / df["minutes_per_week"].replace(
            0.0, np.nan
        )
        eff_df = df.loc[
            df["minutes_per_week"] > 0, ["platform_name", "Cultural_Efficiency"]
        ].copy()
        eff_df = eff_df.dropna()

        if not eff_df.empty:
            max_ce = float(eff_df["Cultural_Efficiency"].max())
            if max_ce > 0:
                eff_df["Cultural_Efficiency"] = (
                    eff_df["Cultural_Efficiency"] / max_ce * 100.0
                )
            else:
                eff_df["Cultural_Efficiency"] = 0.0

            eff_df = eff_df.sort_values("Cultural_Efficiency", ascending=False)
            per_platform_eff = eff_df.to_dict("records")
        else:
            per_platform_eff = []

        # 4. Weighted average variety, if provided
        avg_variety = None
        if "variety_score" in df.columns and total_mins > 0:
            if df["variety_score"].notna().any():
                avg_variety = float(
                    np.average(
                        df["variety_score"].fillna(0.0),
                        weights=df["minutes_per_week"],
                    )
                )

        return {
            "C_Score": C_Score,
            "A_Risk": A_Risk,
            "D_Index": D_Index,
            "Avg_Variety": avg_variety,
            "Satisfaction": satisfaction,
            "FOMO": fomo,
            "Per_Platform_Efficiency": per_platform_eff,
        }