# =========================
# 🤖 BOT DETECTION (BERT)
# =========================

from transformers import pipeline

# =========================
# LOAD MODEL (SAFE)
# =========================
try:
    bot_model = pipeline(
        "text-classification",
        model="unitary/toxic-bert",   # ringan & cepat
        device=-1  # CPU (HF aman)
    )
    print("✅ BERT bot model loaded")

except Exception as e:
    print("⚠️ BERT model gagal load:", e)
    bot_model = None


# =========================
# 🔥 FALLBACK RULE-BASED
# =========================
def fallback_bot_detection(text):
    score = 0

    if len(text) < 20:
        score += 1

    if text.count("!") > 2:
        score += 1

    if len(set(text.split())) < 4:
        score += 1

    if text.isupper():
        score += 1

    label = "Bot" if score >= 2 else "Human"

    return {
        "text": text,
        "score": score / 4,
        "label": label,
        "method": "fallback"
    }


# =========================
# 🔥 MAIN FUNCTION
# =========================
def detect_bot_bert(texts):
    results = []

    # 🔥 jika model gagal → fallback semua
    if bot_model is None:
        print("⚠️ fallback mode aktif")
        return [fallback_bot_detection(t) for t in texts[:20]]

    try:
        for t in texts[:20]:

            # batasi panjang (biar tidak error)
            t_clean = t[:512]

            res = bot_model(t_clean)[0]

            score = float(res["score"])

            # interpretasi label
            if res["label"].lower() in ["toxic", "toxic"]:
                label = "Bot" if score > 0.6 else "Human"
            else:
                label = "Human"

            results.append({
                "text": t,
                "score": round(score, 3),
                "label": label,
                "method": "bert"
            })

        return results

    except Exception as e:
        print("❌ BERT inference error:", e)

        # fallback kalau inference gagal
        return [fallback_bot_detection(t) for t in texts[:20]]