Upload app (23).py

app (23).py

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+import gradio as gr
+import spaces
+import torch
+import numpy as np
+import re
+import matplotlib.pyplot as plt
+import io
+from PIL import Image
+from datetime import datetime
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+from motif_tagging import detect_motifs
+from functools import lru_cache
+from torch.nn.functional import sigmoid
+
+# ----- Models -----
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Emotion model (CPU for stability)
+emotion_pipeline = pipeline(
+    "text-classification",
+    model="j-hartmann/emotion-english-distilroberta-base",
+    top_k=6,
+    truncation=True,
+    device=-1  # Force CPU usage
+)
+
+# Abuse Model
+model_name = "SamanthaStorm/tether-multilabel-v4"  # Or your HF Hub path
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+model.to(device)
+
+# DARVO Model
+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()
+darvo_model.to(device)
+
+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 = 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-v4"
+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": 0.27,
+    "control": 0.47,
+    "gaslighting": 0.48,
+    "guilt tripping": .56,
+    "dismissiveness": 0.25,
+    "blame shifting": 0.55,
+    "projection": 0.59,
+    "insults": 0.33,
+    "contradictory statements": 0.27,
+    "obscure language": 0.65,
+    "nonabusive": 1.0
+}
+
+PATTERN_WEIGHTS = {
+    "recovery": 0.5,
+    "control": 1.4,
+    "gaslighting": 1.0,
+    "guilt tripping": 0.9,
+    "dismissiveness": 0.9,
+    "blame shifting": 0.8,
+    "projection": 0.5,
+    "insults": 1.2,
+    "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"
+]
+
+
+@spaces.GPU
+def compute_abuse_score(matched_scores, sentiment):
+    """
+    Compute abuse score with more conservative adjustments.
+    """
+    if not matched_scores:
+        return 0.0
+
+    sorted_scores = sorted(matched_scores, key=lambda x: x[1], reverse=True)
+    highest_score = sorted_scores[0][1]
+    num_patterns = len(matched_scores)
+
+    # Scale down base score more aggressively if multiple patterns are present
+    if num_patterns > 1:
+        highest_score *= (1 - (num_patterns - 1) * 0.2)  # Reduce by 20% for each additional pattern
+
+    base_score = highest_score * 100
+
+    critical_patterns = {
+        'gaslighting': 1.4,  # Reduced
+        'guilt tripping': 1.3, # Reduced
+        'blame shifting': 1.2, # Reduced
+        'control': 1.3,       # Reduced
+        'insults': 1.1,       # Reduced
+        'manipulation': 1.2,
+        'love bombing': 1.2,
+        'emotional blackmail': 1.4,
+        'dismissiveness': 1.1,
+        'contradictory statements': 1.1
+    }
+
+    for label, score, _ in matched_scores:
+        if label in critical_patterns and score > 0.5:
+            base_score *= critical_patterns[label]
+
+    # Further reduce combination multipliers
+    if len(matched_scores) >= 2:
+        base_score *= 1.1  # Reduced
+    if len(matched_scores) >= 3:
+        base_score *= 1.05  # Reduced
+
+    # Reduce high confidence boost
+    if any(score > 0.8 for _, score, _ in matched_scores):
+        base_score *= 1.05  # Reduced
+
+    # Sentiment modifier (more nuanced)
+    if emotion_profile.get("neutral", 0) > 0.85 and any(
+        scores[LABELS.index(l)] > thresholds[l] * 0.8  # Scale down thresholds for neutral sentiment
+        for l in ["control", "blame shifting", "insults", "guilt tripping"] # Consider more labels
+    ):
+        sentiment = "undermining"  # Only override if multiple patterns are present with moderate confidence
+    elif sentiment_score > 0.35:  # Increased threshold
+        sentiment = "undermining"
+    else:
+        sentiment = "supportive"
+
+    # Reduce minimum score and threshold for activation
+    if any(score > 0.9 for _, score, _ in matched_scores):  # Higher threshold
+        base_score = max(base_score, 75.0)  # Reduced
+    elif any(score > 0.7 for _, score, _ in matched_scores):  # Moderate threshold
+        base_score = max(base_score, 60.0)  # Reduced
+
+    return min(round(base_score, 1), 100.0)
+
+@lru_cache(maxsize=1024)  # Cache results for performance
+def analyze_single_message(text, thresholds):
+    print("⚡ ENTERED analyze_single_message")
+    stage = 1
+    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).to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
+
+    # Sentiment override
+    if emotion_profile.get("neutral", 0) > 0.85 and any(
+        scores[LABELS.index(l)] > thresholds[l] * 0.8  # Scale down thresholds for neutral sentiment
+        for l in ["control", "blame shifting", "insults", "guilt tripping"]  # Consider more labels
+    ):
+        sentiment = "undermining"  # Only override if multiple patterns are present with moderate confidence
+    elif sentiment_score > 0.35:  # Increased threshold
+        sentiment = "undermining"
+    else:
+        sentiment = "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]
+    ]
+    matched_scores = [(label, score, PATTERN_WEIGHTS.get(label, 1.0)) for label, score in zip(LABELS, scores) if score > adjusted_thresholds[label]]
+
+    if not threshold_labels:
+        return 0.0, [], [], {"label": sentiment}, 1, 0.0, None
+
+    top_patterns = sorted(
+        [(label, score) for label, score in zip(LABELS, scores)],
+        key=lambda x: x[1],
+        reverse=True
+    )[:2]
+
+    # Abuse score
+    abuse_score = compute_abuse_score(matched_scores, sentiment) # Calculate before adjustments
+
+    if weapon_flag:
+        abuse_score = min(abuse_score + 25, 100)  # Apply weapon adjustment directly to abuse_score
+        if stage < 2:
+            stage = 2
+
+    abuse_score = min(abuse_score, 100 if "control" in threshold_labels else 95) # Apply cap after weapon adjustment
+
+    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score)
+
+
+    threshold_labels = [label for label, score in zip(LABELS, scores) if score > adjusted_thresholds[label]]
+    matched_scores = [(label, score, PATTERN_WEIGHTS.get(label, 1.0)) for label, score in zip(LABELS, scores) if score > adjusted_thresholds[label]]
+
+    if not threshold_labels:
+        return 0.0, [], [], {"label": sentiment}, 1, 0.0, None
+
+    # Remove recovery tag if tone is fake
+    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)")
+
+    # Override profanity/anger for short texts
+    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)
+    short_text = len(tokens) <= 10
+    anger_score = emotion_profile.get("anger", 0)
+    if has_profane and anger_score > 0.75 and short_text:
+        print("⚠️ Profanity + Anger Override Triggered")
+        insult_score = next((s for l, s in top_patterns if l == "insults"), 0)
+        if ("insults", insult_score) not in top_patterns:
+            top_patterns = [("insults", insult_score)] + top_patterns
+        if "insults" not in threshold_labels:
+            threshold_labels.append("insults")
+
+    # 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
+
+
+
+@spaces.GPU
+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
+
+    if none_selected:
+        escalation_score = 0
+        escalation_note = "Checklist completed: no danger items reported."
+        escalation_completed = True
+    elif responses_checked:
+        escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a)
+        escalation_note = "Checklist completed."
+        escalation_completed = True
+    else:
+        escalation_score = None
+        escalation_note = "Checklist not completed."
+        escalation_completed = False
+
+    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.", None
+        
+    # 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.lower(), 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"
+    )
+
+    none_selected_checked = answers_and_none[-1]
+    responses_checked = any(answers_and_none[:-1])
+    none_selected = not responses_checked and none_selected_checked
+
+# Determine escalation_score
+    if none_selected:
+        escalation_score = 0
+        escalation_completed = True
+    elif responses_checked:
+        escalation_score = sum(
+            w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a
+        )
+        escalation_completed = True
+    else:
+        escalation_score = None
+        escalation_completed = False
+
+# Build escalation_text and hybrid_score
+    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
+    elif escalation_score == 0:
+        escalation_text = (
+            "✅ **Escalation Checklist Completed:** No danger items reported.\n"
+            "🧭 **Escalation potential estimated from detected message patterns only.**\n"
+            f"• Pattern Risk: {pattern_escalation_risk}\n"
+            f"• Checklist Risk: None reported\n"
+            f"• Escalation Bump: +{escalation_bump} (from DARVO, tone, intensity, etc.)"
+        )
+        hybrid_score = escalation_bump
+    else:
+        hybrid_score = escalation_score + escalation_bump
+        escalation_text = (
+            f"📈 **Escalation Potential: {escalation_risk} ({hybrid_score}/29)**\n"
+            "📋 This score combines your safety checklist answers *and* detected high-risk behavior.\n"
+            f"• Pattern Risk: {pattern_escalation_risk}\n"
+            f"• Checklist Risk: {checklist_escalation_risk}\n"
+            f"• Escalation Bump: +{escalation_bump} (from DARVO, tone, intensity, etc.)"
+        )
+    
+    # Composite Abuse Score (weighted average based on message length)
+    composite_abuse_scores = []
+    message_lengths = [len(m.split()) for m, _ in active]
+    total_length = sum(message_lengths)
+
+    for result, length in zip(results, message_lengths):
+        abuse_score = result[0][0]
+        weight = length / total_length if total_length > 0 else 1 / len(results) if len(results) > 0 else 1
+        composite_abuse_scores.append(abuse_score * weight)
+    composite_abuse = int(round(sum(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
+    # safe derive top_labels
+    top_labels = []
+    for result, _ in results:
+        threshold_labels = result[1]
+        top_patterns     = result[2]
+        if threshold_labels:
+            top_labels.append(threshold_labels[0])
+        elif top_patterns:
+            top_labels.append(top_patterns[0][0])
+        else:
+            top_labels.append("none")   # or whatever default you prefer
+    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 = [
+    pats[0][0] if (pats := r[0][2]) else "none"
+    for r in results
+    ]
+    timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, top_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")
+
+
+# ─── FINAL “FORCE LAUNCH” (no guards) ────────────────────────
+
+demo = 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, include messages from a recent emotionally intense period."
+    ),
+    flagging_mode="manual"
+)
+# This single call will start the server and block,
+# keeping the container alive on Spaces.
+demo.launch()
+
+