Delete app.py

app.py

@@ -1,816 +0,0 @@
-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()
-
-