import gradio as gr
import torch
import numpy as np
from transformers import pipeline, RobertaForSequenceClassification, RobertaTokenizer
from motif_tagging import detect_motifs
import re
import matplotlib.pyplot as plt
import io
from PIL import Image
from datetime import datetime
from transformers import pipeline as hf_pipeline  # prevent name collision with gradio pipeline

def get_emotion_profile(text):
    emotions = emotion_pipeline(text)
    if isinstance(emotions, list) and isinstance(emotions[0], list):
        emotions = emotions[0]
    return {e['label'].lower(): round(e['score'], 3) for e in emotions}
# Emotion model (no retraining needed)
emotion_pipeline = hf_pipeline(
    "text-classification",
    model="j-hartmann/emotion-english-distilroberta-base",
    top_k=6,
    truncation=True
)

# --- Timeline Visualization Function ---
def generate_abuse_score_chart(dates, scores, labels):
    import matplotlib.pyplot as plt
    import io
    from PIL import Image
    from datetime import datetime
    import re

    # Determine if all entries are valid dates
    if all(re.match(r"\d{4}-\d{2}-\d{2}", d) for d in dates):
        parsed_x = [datetime.strptime(d, "%Y-%m-%d") for d in dates]
        x_labels = [d.strftime("%Y-%m-%d") for d in parsed_x]
    else:
        parsed_x = list(range(1, len(dates) + 1))
        x_labels = [f"Message {i+1}" for i in range(len(dates))]

    fig, ax = plt.subplots(figsize=(8, 3))
    ax.plot(parsed_x, scores, marker='o', linestyle='-', color='darkred', linewidth=2)

    for x, y in zip(parsed_x, scores):
        ax.text(x, y + 2, f"{int(y)}%", ha='center', fontsize=8, color='black')

    ax.set_xticks(parsed_x)
    ax.set_xticklabels(x_labels)
    ax.set_xlabel("")  # No axis label
    ax.set_ylabel("Abuse Score (%)")
    ax.set_ylim(0, 105)
    ax.grid(True)
    plt.tight_layout()

    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    return Image.open(buf)


# --- Abuse Model ---
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "SamanthaStorm/tether-multilabel-v3"
model      = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer  = AutoTokenizer.from_pretrained(model_name, use_fast=False)

LABELS = [
    "recovery", "control", "gaslighting", "guilt tripping", "dismissiveness", "blame shifting",
    "nonabusive","projection", "insults", "contradictory statements", "obscure language"
]

THRESHOLDS = {
    "recovery": 0.4,
    "control": 0.45,
    "gaslighting": 0.25,
    "guilt tripping": .20,
    "dismissiveness": 0.25,
    "blame shifting": 0.25,
    "projection": 0.25,
    "insults": 0.05,
    "contradictory statements": 0.25,
    "obscure language": 0.25,
    "nonabusive": 1.0
}

PATTERN_WEIGHTS = {
    "recovery": 0.7,
    "control": 1.4,
    "gaslighting": 1.50,
    "guilt tripping": 1.2,
    "dismissiveness": 0.9,
    "blame shifting": 0.8,
    "projection": 0.5,
    "insults": 1.4,
    "contradictory statements": 1.0,
    "obscure language": 0.9,
    "nonabusive": 0.0
}

ESCALATION_RISKS = {
    "blame shifting": "low",
    "contradictory statements": "moderate",
    "control": "high",
    "dismissiveness": "moderate",
    "gaslighting": "moderate",
    "guilt tripping": "moderate",
    "insults": "moderate",
    "obscure language": "low",
    "projection": "low",
    "recovery phase": "low"
}
RISK_STAGE_LABELS = {
    1: "🌀 Risk Stage: Tension-Building\nThis message reflects rising emotional pressure or subtle control attempts.",
    2: "🔥 Risk Stage: Escalation\nThis message includes direct or aggressive patterns, suggesting active harm.",
    3: "🌧️ Risk Stage: Reconciliation\nThis message reflects a reset attempt—apologies or emotional repair without accountability.",
    4: "🌸 Risk Stage: Calm / Honeymoon\nThis message appears supportive but may follow prior harm, minimizing it."
}

ESCALATION_QUESTIONS = [
    ("Partner has access to firearms or weapons", 4),
    ("Partner threatened to kill you", 3),
    ("Partner threatened you with a weapon", 3),
    ("Partner has ever choked you, even if you considered it consensual at the time", 4),
    ("Partner injured or threatened your pet(s)", 3),
    ("Partner has broken your things, punched or kicked walls, or thrown things ", 2),
    ("Partner forced or coerced you into unwanted sexual acts", 3),
    ("Partner threatened to take away your children", 2),
    ("Violence has increased in frequency or severity", 3),
    ("Partner monitors your calls/GPS/social media", 2)
]
def get_emotional_tone_tag(emotions, sentiment, patterns, abuse_score):
    sadness = emotions.get("sadness", 0)
    joy = emotions.get("joy", 0)
    neutral = emotions.get("neutral", 0)
    disgust = emotions.get("disgust", 0)
    anger = emotions.get("anger", 0)
    fear = emotions.get("fear", 0)
    disgust = emotions.get("disgust", 0)

    # 1. Performative Regret
    if (
        sadness > 0.4 and
        any(p in patterns for p in ["blame shifting", "guilt tripping", "recovery phase"]) and
        (sentiment == "undermining" or abuse_score > 40)
    ):
        return "performative regret"

    # 2. Coercive Warmth
    if (
        (joy > 0.3 or sadness > 0.4) and
        any(p in patterns for p in ["control", "gaslighting"]) and
        sentiment == "undermining"
    ):
        return "coercive warmth"

    # 3. Cold Invalidation
    if (
        (neutral + disgust) > 0.5 and
        any(p in patterns for p in ["dismissiveness", "projection", "obscure language"]) and
        sentiment == "undermining"
    ):
        return "cold invalidation"

    # 4. Genuine Vulnerability
    if (
        (sadness + fear) > 0.5 and
        sentiment == "supportive" and
        all(p in ["recovery phase"] for p in patterns)
    ):
        return "genuine vulnerability"

    # 5. Emotional Threat
    if (
        (anger + disgust) > 0.5 and
        any(p in patterns for p in ["control", "insults", "dismissiveness"]) and
        sentiment == "undermining"
    ):
        return "emotional threat"

    # 6. Weaponized Sadness
    if (
        sadness > 0.6 and
        any(p in patterns for p in ["guilt tripping", "projection"]) and
        sentiment == "undermining"
    ):
        return "weaponized sadness"

    # 7. Toxic Resignation
    if (
        neutral > 0.5 and
        any(p in patterns for p in ["dismissiveness", "obscure language"]) and
        sentiment == "undermining"
    ):
        return "toxic resignation"
    # 8. Aggressive Dismissal
    if (
        anger > 0.5 and
        any(p in patterns for p in ["aggression", "insults", "control"]) and
        sentiment == "undermining"
    ):
        return "aggressive dismissal"
    # 9. Deflective Hostility
    if (
        (0.2 < anger < 0.7 or 0.2 < disgust < 0.7) and
        any(p in patterns for p in ["deflection", "projection"]) and
        sentiment == "undermining"
    ):
        return "deflective hostility"   
    # 10. Mocking Detachment
    if (
        (neutral + joy) > 0.5 and
        any(p in patterns for p in ["mockery", "insults", "projection"]) and
        sentiment == "undermining"
    ):
        return "mocking detachment"
        # 11. Contradictory Gaslight
    if (
        (joy + anger + sadness) > 0.5 and
        any(p in patterns for p in ["gaslighting", "contradictory statements"]) and
        sentiment == "undermining"
    ):
        return "contradictory gaslight"
        # 12. Calculated Neutrality
    if (
        neutral > 0.6 and
        any(p in patterns for p in ["obscure language", "deflection", "dismissiveness"]) and
        sentiment == "undermining"
    ):
        return "calculated neutrality"
     # 13. Forced Accountability Flip
    if (
        (anger + disgust) > 0.5 and
        any(p in patterns for p in ["blame shifting", "manipulation", "projection"]) and
        sentiment == "undermining"
    ):
        return "forced accountability flip"
        # 14. Conditional Affection
    if (
        joy > 0.4 and
        any(p in patterns for p in ["apology baiting", "control", "recovery phase"]) and
        sentiment == "undermining"
    ):
        return "conditional affection"
    
    if (
        (anger + disgust) > 0.5 and
        any(p in patterns for p in ["blame shifting", "projection", "deflection"]) and
        sentiment == "undermining"
    ):
        return "forced accountability flip"

    # Emotional Instability Fallback
    if (
        (anger + sadness + disgust) > 0.6 and
        sentiment == "undermining"
    ):
        return "emotional instability"
        
    return None
# 🔄 New DARVO score model (regression-based)
from torch.nn.functional import sigmoid
import torch

# Load your trained DARVO regressor from Hugging Face Hub
darvo_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-darvo-regressor-v1")
darvo_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-darvo-regressor-v1", use_fast=False)
darvo_model.eval()

def predict_darvo_score(text):
    inputs = darvo_tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        logits = darvo_model(**inputs).logits
        score = sigmoid(logits).item()
    return round(score, 4)  # Rounded for display/output
def detect_weapon_language(text):
    weapon_keywords = [
        "knife", "knives", "stab", "cut you", "cutting",
        "gun", "shoot", "rifle", "firearm", "pistol",
        "bomb", "blow up", "grenade", "explode",
        "weapon", "armed", "loaded", "kill you", "take you out"
    ]
    text_lower = text.lower()
    return any(word in text_lower for word in weapon_keywords)
def get_risk_stage(patterns, sentiment):
    if "insults" in patterns:
        return 2
    elif "recovery phase" in patterns:
        return 3
    elif "control" in patterns or "guilt tripping" in patterns:
        return 1
    elif sentiment == "supportive" and any(p in patterns for p in ["projection", "dismissiveness"]):
        return 4
    return 1

def generate_risk_snippet(abuse_score, top_label, escalation_score, stage):
    import re

    # Extract aggression score if aggression is detected
    if isinstance(top_label, str) and "aggression" in top_label.lower():
        try:
            match = re.search(r"\(?(\d+)\%?\)?", top_label)
            aggression_score = int(match.group(1)) / 100 if match else 0
        except:
            aggression_score = 0
    else:
        aggression_score = 0

    # Revised risk logic
    if abuse_score >= 85 or escalation_score >= 16:
        risk_level = "high"
    elif abuse_score >= 60 or escalation_score >= 8 or aggression_score >= 0.25:
        risk_level = "moderate"
    elif stage == 2 and abuse_score >= 40:
        risk_level = "moderate"
    else:
        risk_level = "low"

    if isinstance(top_label, str) and " – " in top_label:
        pattern_label, pattern_score = top_label.split(" – ")
    else:
        pattern_label = str(top_label) if top_label is not None else "Unknown"
        pattern_score = ""

    WHY_FLAGGED = {
        "control": "This message may reflect efforts to restrict someone’s autonomy, even if it's framed as concern or care.",
        "gaslighting": "This message could be manipulating someone into questioning their perception or feelings.",
        "dismissiveness": "This message may include belittling, invalidating, or ignoring the other person’s experience.",
        "insults": "Direct insults often appear in escalating abusive dynamics and can erode emotional safety.",
        "blame shifting": "This message may redirect responsibility to avoid accountability, especially during conflict.",
        "guilt tripping": "This message may induce guilt in order to control or manipulate behavior.",
        "recovery phase": "This message may be part of a tension-reset cycle, appearing kind but avoiding change.",
        "projection": "This message may involve attributing the abuser’s own behaviors to the victim.",
        "contradictory statements": "This message may contain internal contradictions used to confuse, destabilize, or deflect responsibility.",
        "obscure language": "This message may use overly formal, vague, or complex language to obscure meaning or avoid accountability.",
        "default": "This message contains language patterns that may affect safety, clarity, or emotional autonomy."
    }

    explanation = WHY_FLAGGED.get(pattern_label.lower(), WHY_FLAGGED["default"])

    base = f"\n\n🛑 Risk Level: {risk_level.capitalize()}\n"
    base += f"This message shows strong indicators of **{pattern_label}**. "

    if risk_level == "high":
        base += "The language may reflect patterns of emotional control, even when expressed in soft or caring terms.\n"
    elif risk_level == "moderate":
        base += "There are signs of emotional pressure or verbal aggression that may escalate if repeated.\n"
    else:
        base += "The message does not strongly indicate abuse, but it's important to monitor for patterns.\n"

    base += f"\n💡 *Why this might be flagged:*\n{explanation}\n"
    base += f"\nDetected Pattern: **{pattern_label} ({pattern_score})**\n"
    base += "🧠 You can review the pattern in context. This tool highlights possible dynamics—not judgments."
    return base


    # --- Step X: Detect Immediate Danger Threats ---
THREAT_MOTIFS = [
    "i'll kill you", "i’m going to hurt you", "you’re dead", "you won't survive this",
    "i’ll break your face", "i'll bash your head in", "i’ll snap your neck",
    "i’ll come over there and make you shut up", "i'll knock your teeth out",
    "you’re going to bleed", "you want me to hit you?", "i won’t hold back next time",
    "i swear to god i’ll beat you", "next time, i won’t miss", "i’ll make you scream",
    "i know where you live", "i'm outside", "i’ll be waiting", "i saw you with him",
    "you can’t hide from me", "i’m coming to get you", "i'll find you", "i know your schedule",
    "i watched you leave", "i followed you home", "you'll regret this", "you’ll be sorry",
    "you’re going to wish you hadn’t", "you brought this on yourself", "don’t push me",
    "you have no idea what i’m capable of", "you better watch yourself",
    "i don’t care what happens to you anymore", "i’ll make you suffer", "you’ll pay for this",
    "i’ll never let you go", "you’re nothing without me", "if you leave me, i’ll kill myself",
    "i'll ruin you", "i'll tell everyone what you did", "i’ll make sure everyone knows",
    "i’m going to destroy your name", "you’ll lose everyone", "i’ll expose you", 
    "your friends will hate you", "i’ll post everything", "you’ll be cancelled",
    "you’ll lose everything", "i’ll take the house", "i’ll drain your account",
    "you’ll never see a dime", "you’ll be broke when i’m done", "i’ll make sure you lose your job",
    "i’ll take your kids", "i’ll make sure you have nothing", "you can’t afford to leave me",
    "don't make me do this", "you know what happens when i’m mad", "you’re forcing my hand",
    "if you just behaved, this wouldn’t happen", "this is your fault",
    "you’re making me hurt you", "i warned you", "you should have listened"
]


def compute_abuse_score(matched_scores, sentiment):
    if not matched_scores:
        return 0

    # Weighted average of passed patterns
    weighted_total = sum(score * weight for _, score, weight in matched_scores)
    weight_sum = sum(weight for _, _, weight in matched_scores)
    base_score = (weighted_total / weight_sum) * 100

    # Boost for pattern count
    pattern_count = len(matched_scores)
    scale = 1.0 + 0.25 * max(0, pattern_count - 1)  # 1.25x for 2, 1.5x for 3+
    scaled_score = base_score * scale

    # Pattern floors
    FLOORS = {
        "control": 40,
        "gaslighting": 30,
        "insults": 25,
        "aggression": 40
    }
    floor = max(FLOORS.get(label, 0) for label, _, _ in matched_scores)
    adjusted_score = max(scaled_score, floor)

    # Sentiment tweak
    if sentiment == "undermining" and adjusted_score < 50:
        adjusted_score += 10

    return min(adjusted_score, 100)
    
    
def analyze_single_message(text, thresholds):
    motif_hits, matched_phrases = detect_motifs(text)

    # Get emotion profile
    emotion_profile = get_emotion_profile(text)
    sentiment_score = emotion_profile.get("anger", 0) + emotion_profile.get("disgust", 0)

    # Get model scores
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()

    # Sentiment override if neutral is high while critical thresholds are passed
    if emotion_profile.get("neutral", 0) > 0.85 and any(
        scores[LABELS.index(l)] > thresholds[l]
        for l in ["control", "blame shifting"]
    ):
        sentiment = "undermining"
    else:
        sentiment = "undermining" if sentiment_score > 0.25 else "supportive"

    weapon_flag = detect_weapon_language(text)

    adjusted_thresholds = {
        k: v + 0.05 if sentiment == "supportive" else v
        for k, v in thresholds.items()
    }
    darvo_score = predict_darvo_score(text)

    threshold_labels = [
        label for label, score in zip(LABELS, scores)
        if score > adjusted_thresholds[label]
    ]

    top_patterns = sorted(
        [(label, score) for label, score in zip(LABELS, scores)],
        key=lambda x: x[1],
        reverse=True
    )[:2]
    # Post-threshold validation: strip recovery if it occurs with undermining sentiment
    if "recovery" in threshold_labels and tone_tag == "forced accountability flip":
        threshold_labels.remove("recovery")
        top_patterns = [p for p in top_patterns if p[0] != "recovery"]
        print("⚠️ Removing 'recovery' due to undermining sentiment (not genuine repair)")

    matched_scores = [
        (label, score, PATTERN_WEIGHTS.get(label, 1.0))
        for label, score in zip(LABELS, scores)
        if score > adjusted_thresholds[label]
    ]

    abuse_score_raw = compute_abuse_score(matched_scores, sentiment)
    abuse_score = abuse_score_raw

    # Risk stage logic
    stage = get_risk_stage(threshold_labels, sentiment) if threshold_labels else 1
    if weapon_flag and stage < 2:
        stage = 2
    if weapon_flag:
        abuse_score_raw = min(abuse_score_raw + 25, 100)

    abuse_score = min(
        abuse_score_raw,
        100 if "control" in threshold_labels else 95
    )

# Tag must happen after abuse score is finalized
    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score)

# ---- Profanity + Anger Override Logic ----
    profane_words = {"fuck", "fucking", "bitch", "shit", "cunt", "ho", "asshole", "dick", "whore", "slut"}
    tokens = set(text.lower().split())
    has_profane = any(word in tokens for word in profane_words)

    anger_score = emotion_profile.get("Anger", 0)
    short_text = len(tokens) <= 10
    insult_score = next((s for l, s in top_patterns if l == "insults"), 0)

    if has_profane and anger_score > 0.75 and short_text:
        print("⚠️ Profanity + Anger Override Triggered")
        top_patterns = sorted(top_patterns, key=lambda x: x[1], reverse=True)
        if top_patterns[0][0] != "insults":
            top_patterns.insert(0, ("insults", insult_score))
        if "insults" not in threshold_labels:
            threshold_labels.append("insults")
        top_patterns = [("insults", insult_score)] + [p for p in top_patterns if p[0] != "insults"]
# Debug
    print(f"Emotional Tone Tag: {tone_tag}")
    # Debug
    print(f"Emotional Tone Tag: {tone_tag}")
    print("Emotion Profile:")
    for emotion, score in emotion_profile.items():
        print(f"  {emotion.capitalize():10}: {score}")
    print("\n--- Debug Info ---")
    print(f"Text: {text}")
    print(f"Sentiment (via emotion): {sentiment} (score: {round(sentiment_score, 3)})")
    print("Abuse Pattern Scores:")
    for label, score in zip(LABELS, scores):
        passed = "✅" if score > adjusted_thresholds[label] else "❌"
        print(f"  {label:25} → {score:.3f} {passed}")
    print(f"Matched for score: {[(l, round(s, 3)) for l, s, _ in matched_scores]}")
    print(f"Abuse Score Raw: {round(abuse_score_raw, 1)}")
    print("------------------\n")

    return abuse_score, threshold_labels, top_patterns, {"label": sentiment}, stage, darvo_score, tone_tag

def analyze_composite(msg1, msg2, msg3, *answers_and_none):
    from collections import Counter

    none_selected_checked = answers_and_none[-1]
    responses_checked = any(answers_and_none[:-1])
    none_selected = not responses_checked and none_selected_checked

    escalation_score = None
    if not none_selected:
        escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a)

    messages = [msg1, msg2, msg3]
    active = [(m, f"Message {i+1}") for i, m in enumerate(messages) if m.strip()]
    if not active:
        return "Please enter at least one message."
        
    # Flag any threat phrases present in the messages
    import re

    def normalize(text):
        import unicodedata
        text = text.lower().strip()
        text = unicodedata.normalize("NFKD", text)  # handles curly quotes
        text = text.replace("’", "'")               # smart to straight
        return re.sub(r"[^a-z0-9 ]", "", text)

    def detect_threat_motifs(message, motif_list):
        norm_msg = normalize(message)
        return [
            motif for motif in motif_list
            if normalize(motif) in norm_msg
    ]

# Collect matches per message
    immediate_threats = [detect_threat_motifs(m, THREAT_MOTIFS) for m, _ in active]
    flat_threats = [t for sublist in immediate_threats for t in sublist]
    threat_risk = "Yes" if flat_threats else "No"
    results = [(analyze_single_message(m, THRESHOLDS.copy()), d) for m, d in active]

    abuse_scores = [r[0][0] for r in results]
    stages = [r[0][4] for r in results]
    darvo_scores = [r[0][5] for r in results]
    tone_tags = [r[0][6] for r in results]
    dates_used = [r[1] for r in results]

    predicted_labels = [label for r in results for label, _ in r[0][2]]
    high = {'control'}
    moderate = {'gaslighting', 'dismissiveness', 'obscure language', 'insults', 'contradictory statements', 'guilt tripping'}
    low = {'blame shifting', 'projection', 'recovery phase'}
    counts = {'high': 0, 'moderate': 0, 'low': 0}
    for label in predicted_labels:
        if label in high:
            counts['high'] += 1
        elif label in moderate:
            counts['moderate'] += 1
        elif label in low:
            counts['low'] += 1

    # Pattern escalation logic
    pattern_escalation_risk = "Low"
    if counts['high'] >= 2 and counts['moderate'] >= 2:
        pattern_escalation_risk = "Critical"
    elif (counts['high'] >= 2 and counts['moderate'] >= 1) or (counts['moderate'] >= 3) or (counts['high'] >= 1 and counts['moderate'] >= 2):
        pattern_escalation_risk = "High"
    elif (counts['moderate'] == 2) or (counts['high'] == 1 and counts['moderate'] == 1) or (counts['moderate'] == 1 and counts['low'] >= 2) or (counts['high'] == 1 and sum(counts.values()) == 1):
        pattern_escalation_risk = "Moderate"

    checklist_escalation_risk = "Unknown" if escalation_score is None else (
        "Critical" if escalation_score >= 20 else
        "Moderate" if escalation_score >= 10 else
        "Low"
    )

    escalation_bump = 0
    for result, _ in results:
        abuse_score, _, _, sentiment, stage, darvo_score, tone_tag = result
        if darvo_score > 0.65:
            escalation_bump += 3
        if tone_tag in ["forced accountability flip", "emotional threat"]:
            escalation_bump += 2
        if abuse_score > 80:
            escalation_bump += 2
        if stage == 2:
            escalation_bump += 3

    def rank(label):
        return {"Low": 0, "Moderate": 1, "High": 2, "Critical": 3, "Unknown": 0}.get(label, 0)

    combined_score = rank(pattern_escalation_risk) + rank(checklist_escalation_risk) + escalation_bump
    escalation_risk = (
        "Critical" if combined_score >= 6 else
        "High" if combined_score >= 4 else
        "Moderate" if combined_score >= 2 else
        "Low"
    )

    if escalation_score is None:
        escalation_text = "🚫 **Escalation Potential: Unknown** (Checklist not completed)\n⚠️ This section was not completed. Escalation potential is estimated using message data only.\n"
        hybrid_score = 0
    else:
        hybrid_score = escalation_score + escalation_bump
        escalation_text = f"📈 **Escalation Potential: {escalation_risk} ({hybrid_score}/29)**\n"
        escalation_text += "📋 This score combines your safety checklist answers *and* detected high-risk behavior.\n"
        escalation_text += f"• Pattern Risk: {pattern_escalation_risk}\n"
        escalation_text += f"• Checklist Risk: {checklist_escalation_risk}\n"
        escalation_text += f"• Escalation Bump: +{escalation_bump} (from DARVO, tone, intensity, etc.)"

    # Composite Abuse Score
    composite_abuse_scores = []
    for result, _ in results:
        _, _, top_patterns, sentiment, _, _, _ = result
        matched_scores = [(label, score, PATTERN_WEIGHTS.get(label, 1.0)) for label, score in top_patterns]
        final_score = compute_abuse_score(matched_scores, sentiment["label"])
        composite_abuse_scores.append(final_score)
    composite_abuse = int(round(sum(composite_abuse_scores) / len(composite_abuse_scores)))

    most_common_stage = max(set(stages), key=stages.count)
    stage_text = RISK_STAGE_LABELS[most_common_stage]
    # Derive top label list for each message
    top_labels = [r[0][1][0] if r[0][1] else r[0][2][0][0] for r in results]
    avg_darvo = round(sum(darvo_scores) / len(darvo_scores), 3)
    darvo_blurb = ""
    if avg_darvo > 0.25:
        level = "moderate" if avg_darvo < 0.65 else "high"
        darvo_blurb = f"\n\n🎭 **DARVO Score: {avg_darvo}** → This indicates a **{level} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame."

    out = f"Abuse Intensity: {composite_abuse}%\n"
    out += "📊 This reflects the strength and severity of detected abuse patterns in the message(s).\n\n"
    out += generate_risk_snippet(composite_abuse, top_labels[0], hybrid_score, most_common_stage)
    out += f"\n\n{stage_text}"
    out += darvo_blurb
    out += "\n\n🎭 **Emotional Tones Detected:**\n"
    for i, tone in enumerate(tone_tags):
        out += f"• Message {i+1}: *{tone or 'none'}*\n"
    # --- Add Immediate Danger Threats section
    if flat_threats:
        out += "\n\n🚨 **Immediate Danger Threats Detected:**\n"
        for t in set(flat_threats):
            out += f"• \"{t}\"\n"
        out += "\n⚠️ These phrases may indicate an imminent risk to physical safety."
    else:
        out += "\n\n🧩 **Immediate Danger Threats:** None explicitly detected.\n"
        out += "This does *not* rule out risk, but no direct threat phrases were matched."
    pattern_labels = [r[0][2][0][0] for r in results]
    timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, pattern_labels)
    out += "\n\n" + escalation_text

    return out, timeline_image
    
textbox_inputs = [gr.Textbox(label=f"Message {i+1}") for i in range(3)]
quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS]
none_box = gr.Checkbox(label="None of the above")

iface = gr.Interface(
    fn=analyze_composite,
    inputs=textbox_inputs + quiz_boxes + [none_box],
    outputs=[
        gr.Textbox(label="Results"),
        gr.Image(label="Abuse Score Timeline", type="pil")
    ],
    title="Abuse Pattern Detector + Escalation Quiz",
    description="Enter up to three messages that concern you. For the most accurate results, enter messages that happened during a recent time period that felt emotionally intense or 'off.'",
    allow_flagging="manual"
)

if __name__ == "__main__":
    iface.launch()