# context_engine.py
"""
Model-free context & emotion heuristics.

Replaces the prior transformer-based emotion classifier with a
fast, deterministic heuristic that infers:
  - primary_emotion: one of ('joy','sadness','anger','fear','surprise','neutral')
  - emotion_confidence: float (0.0 - 1.0) indicating heuristic strength
  - conversation_mode: Ping-Pong / Standard / Deep Dive
  - emoji suggestions and min-word guidance

Design notes:
  - Uses emoji presence, punctuation, uppercase emphasis, keywords, negations,
    question density, message length, and repetition to infer emotion.
  - Intentionally conservative: returns moderate confidences unless strong signals.
  - No external libraries or model downloads required.
"""
from typing import Tuple
import re

# Keyword lists (tunable)
_JOY_KEYWORDS = {"happy", "great", "awesome", "fantastic", "nice", "love", "yay", "yay!", "cool", "amazing", "thanks", "thank you", "cheers"}
_SADNESS_KEYWORDS = {"sad", "unhappy", "depressed", "upset", "down", "sadder", "melancholy", "sorrow", "lonely"}
_ANGER_KEYWORDS = {"angry", "frustrat", "frustrated", "mad", "furious", "annoyed", "irritat", "rage", "disgusted"}
_FEAR_KEYWORDS = {"scared", "afraid", "anxious", "worried", "panic", "nervous", "fear"}
_SURPRISE_KEYWORDS = {"wow", "whoa", "surpris", "unexpected", "amazed", "shocked"}
_NEGATIONS = {"not", "don't", "didn't", "can't", "couldn't", "won't", "never", "n't"}

_EMOJI_POSITIVE = {"😊","🙂","😄","😁","🎉","👍","🤝","😃","✨","😍"}
_EMOJI_NEGATIVE = {"😢","😞","☹️","😡","😭","😠","😤","😖","😩","😓"}
_EMOJI_SURPRISE = {"😲","😯","😮","🤯","😳"}

def _count_emojis(text: str):
    # simple unicode emoji detection by ranges + common emoji symbols (lightweight)
    # also check presence in our small emoji sets
    pos = sum(1 for e in _EMOJI_POSITIVE if e in text)
    neg = sum(1 for e in _EMOJI_NEGATIVE if e in text)
    sup = sum(1 for e in _EMOJI_SURPRISE if e in text)
    # rough generic emoji count (fallback)
    generic = len(re.findall(r'[\U0001F300-\U0001FAFF\U00002700-\U000027BF]', text))
    return {"positive": pos, "negative": neg, "surprise": sup, "generic": generic}

def _word_tokens(text: str):
    return re.findall(r"\w+", text.lower())

def _keyword_score(tokens, keywords):
    return sum(1 for t in tokens if any(t.startswith(k) for k in keywords))

def _has_upper_emphasis(text: str):
    # Count words that are ALL CAPS and length>=2
    caps = [w for w in re.findall(r"\b[A-Z]{2,}\b", text)]
    return len(caps)

def _question_density(tokens, text: str):
    qwords = {"what","why","how","which","when","where","who","do","does","did","can","could","would","should","is","are","was","were"}
    qcount = sum(1 for t in tokens if t in qwords)
    total = max(1, len(tokens))
    return qcount / total

def _detect_emotion(text: str) -> Tuple[str, float]:
    """
    Rule-based emotion detection.
    Returns (label, confidence)
    """
    if not text or not text.strip():
        return ("neutral", 0.0)

    t = text.strip()
    tokens = _word_tokens(t)
    lower = t.lower()

    # simple signals
    emoji_counts = _count_emojis(t)
    positive_emoji = emoji_counts["positive"]
    negative_emoji = emoji_counts["negative"]
    surprise_emoji = emoji_counts["surprise"]
    generic_emoji = emoji_counts["generic"]

    upper_caps = _has_upper_emphasis(t)
    exclamations = t.count("!")
    question_marks = t.count("?")
    repeated_punct = bool(re.search(r'([!?])\1{2,}', t))  # e.g., "!!!" or "???" or "!?!!"

    # keyword matches
    joy_kw = _keyword_score(tokens, _JOY_KEYWORDS)
    sad_kw = _keyword_score(tokens, _SADNESS_KEYWORDS)
    anger_kw = _keyword_score(tokens, _ANGER_KEYWORDS)
    fear_kw = _keyword_score(tokens, _FEAR_KEYWORDS)
    surprise_kw = _keyword_score(tokens, _SURPRISE_KEYWORDS)

    negation_present = any(n in tokens for n in _NEGATIONS)
    q_density = _question_density(tokens, t)
    length = len(tokens)

    # scoring heuristics (base 0)
    scores = {
        "joy": 0.0,
        "sadness": 0.0,
        "anger": 0.0,
        "fear": 0.0,
        "surprise": 0.0,
        "neutral": 0.0
    }

    # Emoji-weighted signals
    scores["joy"] += positive_emoji * 0.35
    scores["sadness"] += negative_emoji * 0.4
    scores["surprise"] += surprise_emoji * 0.4

    # Keyword signals (normalized)
    scores["joy"] += min(joy_kw * 0.25, 1.0)
    scores["sadness"] += min(sad_kw * 0.3, 1.0)
    scores["anger"] += min(anger_kw * 0.35, 1.0)
    scores["fear"] += min(fear_kw * 0.3, 1.0)
    scores["surprise"] += min(surprise_kw * 0.3, 1.0)

    # punctuation / emphasis
    if exclamations >= 2 or upper_caps >= 2 or repeated_punct:
        # could be joy or anger depending on words
        if joy_kw or positive_emoji:
            scores["joy"] += 0.4
        if anger_kw or negative_emoji:
            scores["anger"] += 0.45
        # otherwise, boost surprise
        if not (joy_kw or anger_kw):
            scores["surprise"] += 0.25

    # question-dense messages -> information-seeking / surprise / neutral
    if q_density > 0.2 or question_marks >= 1:
        scores["surprise"] += 0.2
        scores["neutral"] += 0.15

    # negativity via negation nearby to positive words -> reduce joy, raise neutral/anger
    if negation_present and joy_kw:
        scores["joy"] = max(0.0, scores["joy"] - 0.5)
        scores["neutral"] += 0.2
        scores["anger"] += 0.1

    # sadness signals for short emotive messages like "so sad" or "feeling down"
    if sad_kw and length <= 6:
        scores["sadness"] += 0.3

    # length-based adjust: very short messages default to small_talk/neutral unless strong signal
    if length <= 3 and sum(scores.values()) < 0.5:
        scores["neutral"] += 0.5

    # normalize into selection
    # pick top-scoring emotion
    top_em = max(scores.items(), key=lambda kv: kv[1])
    label = top_em[0]
    raw_score = float(top_em[1])

    # compute confidence: scale raw_score to 0..1 with heuristics
    # higher length + multiple signals -> higher confidence
    confidence = raw_score
    # boost confidence for multiple corroborating signals
    corroborators = 0
    if positive_emoji + negative_emoji + surprise_emoji + generic_emoji > 0:
        corroborators += 1
    if upper_caps > 0 or exclamations > 0 or repeated_punct:
        corroborators += 1
    if any([joy_kw, sad_kw, anger_kw, fear_kw, surprise_kw]):
        corroborators += 1
    # boost based on corroborators (0..3)
    confidence = min(1.0, confidence + (0.12 * corroborators))

    # fallback: if very low signal, mark neutral with low confidence
    if confidence < 0.15:
        label = "neutral"
        confidence = round(max(confidence, 0.05), 2)
    else:
        confidence = round(confidence, 2)

    return (label, confidence)

def get_smart_context(user_text: str):
    """
    Returns a short persona instruction block including:
      - Conversation Mode
      - Emotional context (heuristic)
      - Emoji suggestions
      - Minimum verbosity hint
    """
    try:
        text = (user_text or "").strip()
        label, confidence = _detect_emotion(text)
        word_count = len(_word_tokens(text))
        q_density = _question_density(_word_tokens(text), text)

        # Conversation Mode determination (same as before)
        if word_count < 4:
            conversation_mode = "Ping-Pong Mode (Fast)"
            min_words_hint = 12
        elif word_count < 20:
            conversation_mode = "Standard Chat Mode (Balanced)"
            min_words_hint = 30
        else:
            conversation_mode = "Deep Dive Mode (Detailed)"
            min_words_hint = 70

        # Map emotion label to friendly guidance & emoji suggestions
        if label == "joy":
            emotional_context = "User: Positive/Energetic. Vibe: Upbeat — be warm and slightly playful."
            emoji_examples = "😊 🎉 🙂"
            emoji_range = (1, 2)
        elif label == "sadness":
            emotional_context = "User: Low Energy. Vibe: Supportive — be gentle and empathetic."
            emoji_examples = "🤍 🌤️"
            emoji_range = (0, 1)
        elif label == "anger":
            emotional_context = "User: Frustrated. Vibe: De-escalate — calm, solution-first."
            emoji_examples = "🙏 🛠️"
            emoji_range = (0, 1)
        elif label == "fear":
            emotional_context = "User: Anxious. Vibe: Reassure and clarify."
            emoji_examples = "🤝 🛡️"
            emoji_range = (0, 1)
        elif label == "surprise":
            emotional_context = "User: Curious/Alert. Vibe: Engage and explain."
            emoji_examples = "🤔 ✨"
            emoji_range = (0, 2)
        else:
            emotional_context = "User: Neutral/Professional. Vibe: Helpful and efficient."
            emoji_examples = "💡 🙂"
            emoji_range = (0, 2)

        # Slightly adjust min_words_hint if question density is high
        if q_density > 0.25:
            min_words_hint = max(min_words_hint, 30)

        # Build instruction block
        return (
            f"\n[PSYCHOLOGICAL PROFILE]\n"
            f"1. Interaction Mode: {conversation_mode}\n"
            f"2. {emotional_context} (detected_emotion={label}, confidence={confidence})\n"
            f"3. Emoji Suggestions: Use {emoji_range[0]}–{emoji_range[1]} emoji(s). Examples: {emoji_examples}\n"
            f"4. Minimum Word Guidance: Aim for ~{min_words_hint} words unless user explicitly requests 'short' or 'brief'.\n"
            f"5. Directive: Mirror user's energy; prefer natural phrasing and avoid robotic one-line replies.\n"
        )
    except Exception as e:
        # conservative fallback
        return (
            "\n[PSYCHOLOGICAL PROFILE]\n"
            "1. Interaction Mode: Standard Chat Mode (Balanced)\n"
            "2. User: Neutral. Vibe: Helpful and efficient.\n"
            "3. Emoji Suggestions: Use 0–1 emoji. Examples: 🙂\n"
            "4. Minimum Word Guidance: Aim for ~30 words.\n"
        )