Delete app.py

app.py

@@ -1,778 +0,0 @@
-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=None,
-    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": 0.55,
-    "control": 0.25,
-    "gaslighting": 0.50,
-    "guilt tripping": .20,
-    "dismissiveness": 0.15,
-    "blame shifting": 0.30,
-    "projection": 0.40,
-    "insults": 0.30,
-    "contradictory statements": 0.50,
-    "obscure language": 0.40,
-    "nonabusive": 1.5
-}
-
-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": 0.01
-}
-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)
-]
-DARVO_PATTERNS = [
-    "blame shifting",         # "You're the reason this happens"
-    "projection",             # "You're the abusive one"
-    "deflection",             # "This isn't about that"
-    "dismissiveness",         # "You're overreacting"
-    "insults",                # Personal attacks that redirect attention
-    "aggression",             # Escalates tone to destabilize
-    "recovery phase",         # Sudden affection following aggression
-    "contradictory statements" # “I never said that” immediately followed by a version of what they said
-]
-DARVO_MOTIFS = [
-    "I never said that.", "You’re imagining things.", "That never happened.",
-    "You’re making a big deal out of nothing.", "It was just a joke.", "You��re too sensitive.",
-    "I don’t know what you’re talking about.", "You’re overreacting.", "I didn’t mean it that way.",
-    "You’re twisting my words.", "You’re remembering it wrong.", "You’re always looking for something to complain about.",
-    "You’re just trying to start a fight.", "I was only trying to help.", "You’re making things up.",
-    "You’re blowing this out of proportion.", "You’re being paranoid.", "You’re too emotional.",
-    "You’re always so dramatic.", "You’re just trying to make me look bad.",
-
-    "You’re crazy.", "You’re the one with the problem.", "You’re always so negative.",
-    "You’re just trying to control me.", "You’re the abusive one.", "You’re trying to ruin my life.",
-    "You’re just jealous.", "You’re the one who needs help.", "You’re always playing the victim.",
-    "You’re the one causing all the problems.", "You’re just trying to make me feel guilty.",
-    "You’re the one who can’t let go of the past.", "You’re the one who’s always angry.",
-    "You’re the one who’s always complaining.", "You’re the one who’s always starting arguments.",
-    "You’re the one who’s always making things worse.", "You’re the one who’s always making me feel bad.",
-    "You’re the one who’s always making me look like the bad guy.",
-    "You’re the one who’s always making me feel like a failure.",
-    "You’re the one who’s always making me feel like I’m not good enough.",
-
-    "I can’t believe you’re doing this to me.", "You’re hurting me.",
-    "You’re making me feel like a terrible person.", "You’re always blaming me for everything.",
-    "You’re the one who’s abusive.", "You’re the one who’s controlling.", "You’re the one who’s manipulative.",
-    "You’re the one who’s toxic.", "You’re the one who’s gaslighting me.",
-    "You’re the one who’s always putting me down.", "You’re the one who’s always making me feel bad.",
-    "You’re the one who’s always making me feel like I’m not good enough.",
-    "You’re the one who’s always making me feel like I’m the problem.",
-    "You’re the one who’s always making me feel like I’m the bad guy.",
-    "You’re the one who’s always making me feel like I’m the villain.",
-    "You’re the one who’s always making me feel like I’m the one who needs to change.",
-    "You’re the one who’s always making me feel like I’m the one who’s wrong.",
-    "You’re the one who’s always making me feel like I’m the one who’s crazy.",
-    "You’re the one who’s always making me feel like I’m the one who’s abusive.",
-    "You’re the one who’s always making me feel like I’m the one who’s toxic."
-]
-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", "threat", "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
-def detect_contradiction(message):
-    patterns = [
-        (r"\b(i love you).{0,15}(i hate you|you ruin everything)", re.IGNORECASE),
-        (r"\b(i’m sorry).{0,15}(but you|if you hadn’t)", re.IGNORECASE),
-        (r"\b(i’m trying).{0,15}(you never|why do you)", re.IGNORECASE),
-        (r"\b(do what you want).{0,15}(you’ll regret it|i always give everything)", re.IGNORECASE),
-        (r"\b(i don’t care).{0,15}(you never think of me)", re.IGNORECASE),
-        (r"\b(i guess i’m just).{0,15}(the bad guy|worthless|never enough)", re.IGNORECASE)
-    ]
-    return any(re.search(p, message, flags) for p, flags in patterns)
-    
-def calculate_darvo_score(patterns, sentiment_before, sentiment_after, motifs_found, contradiction_flag=False):
-    # Count all detected DARVO-related patterns
-    pattern_hits = sum(1 for p in patterns if p.lower() in DARVO_PATTERNS)
-
-    # Sentiment delta
-    sentiment_shift_score = max(0.0, sentiment_after - sentiment_before)
-
-    # Match against DARVO motifs more loosely
-    motif_hits = sum(
-        any(phrase.lower() in motif.lower() or motif.lower() in phrase.lower()
-            for phrase in DARVO_MOTIFS)
-        for motif in motifs_found
-    )
-    motif_score = motif_hits / max(len(DARVO_MOTIFS), 1)
-
-    # Contradiction still binary
-    contradiction_score = 1.0 if contradiction_flag else 0.0
-
-    # Final DARVO score
-    return round(min(
-        0.3 * pattern_hits +
-        0.3 * sentiment_shift_score +
-        0.25 * motif_score +
-        0.15 * contradiction_score, 1.0
-    ), 3)
-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 "threat" in patterns or "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.",
-        "threat": "This message includes threatening language, which is a strong predictor of harm.",
-        "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
-
-    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.",
-        "threat": "This message includes threatening language, which is a strong predictor of harm.",
-        "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 indirect control 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
-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 = {
-        "threat": 70,
-        "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", "threat", "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()
-    }
-
-    contradiction_flag = detect_contradiction(text)
-
-    threshold_labels = [
-        label for label, score in zip(LABELS, scores)
-        if score > adjusted_thresholds[label]
-    ]
-    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, 0)
-    motifs = [phrase for _, phrase in matched_phrases]
-
-    darvo_score = calculate_darvo_score(
-        threshold_labels,
-        sentiment_before=0.0,
-        sentiment_after=sentiment_score,
-        motifs_found=motifs,
-        contradiction_flag=contradiction_flag
-    )
-
-    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 "threat" in threshold_labels or "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(f"Motifs: {motifs}")
-    print(f"Contradiction: {contradiction_flag}")
-    print("------------------\n")
-
-    return abuse_score, threshold_labels, top_patterns, {"label": sentiment}, stage, darvo_score, tone_tag
-
-def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
-    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 = None
-        risk_level = "unknown"
-    else:
-        escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a)
-
-    messages = [msg1, msg2, msg3]
-    dates = [date1, date2, date3]
-    active = [(m, d) for m, d in zip(messages, dates) if m.strip()]
-    if not active:
-        return "Please enter at least one message."
-
-    # Run model on messages
-    results = [(analyze_single_message(m, THRESHOLDS.copy()), d) for m, d in active]
-    # --- Combined Abuse Escalation Scoring ---
-
-# Extract predicted abuse labels from all messages
-    predicted_labels = [label for r in results for label, _ in r[0][2]]
-
-# Categorize by severity
-    high = {'control'}
-    moderate = {
-        'gaslighting', 'dismissiveness', 'obscure language',
-        'insults', 'contradictory statements', 'guilt tripping'
-    }
-    low = {'blame shifting', 'projection', 'recovery phase'}
-
-# Count severity types
-    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
-
-# Derive abuse_risk from combinations
-    if counts['high'] >= 2 and counts['moderate'] >= 2:
-        abuse_risk = 'Critical'
-    elif (counts['high'] >= 2 and counts['moderate'] >= 1) or (counts['moderate'] >= 3) or (counts['high'] >= 1 and counts['moderate'] >= 2):
-        abuse_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):
-        abuse_risk = 'Moderate'
-    else:
-        abuse_risk = 'Low'
-
-# Combine abuse_risk and checklist score into final risk_level
-    if escalation_score is not None:
-        if escalation_score >= 8 or abuse_risk == 'Critical':
-            risk_level = 'Critical'
-        elif escalation_score >= 5 or abuse_risk == 'High':
-            risk_level = 'High'
-        elif escalation_score >= 2 or abuse_risk == 'Moderate':
-            risk_level = 'Moderate'
-        else:
-            risk_level = 'Low'
-    abuse_scores = [r[0][0] for r in results]
-    top_labels = [r[0][1][0] if r[0][1] else r[0][2][0][0] for r in results]
-    top_scores = [r[0][2][0][1] for r in results]
-    sentiments = [r[0][3]['label'] 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] or "Undated" for r in results]  # Store dates for future mapping
-    # Calculate escalation bump *after* model results exist
-    escalation_bump = 0
-    for result, _ in results:
-        abuse_score, threshold_labels, top_patterns, 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
-
-    # Now we can safely calculate hybrid_score
-    hybrid_score = escalation_score + escalation_bump if escalation_score is not None else 0
-    risk_level = (
-        "High" if hybrid_score >= 16 else
-        "Moderate" if hybrid_score >= 8 else
-        "Low"
-    )
-
-    # Now compute scores and allow override
-    abuse_scores = [r[0][0] for r in results]
-    stages = [r[0][4] for r in results]
-
-    # Post-check override (e.g. stage 2 or high abuse score forces Moderate risk)
-    if any(score > 70 for score in abuse_scores) or any(stage == 2 for stage in stages):
-        if risk_level == "Low":
-            risk_level = "Moderate"
-
-    for result, date in results:
-        assert len(result) == 7, "Unexpected output from analyze_single_message"
-
-# --- Composite Abuse Score using compute_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]
-
-    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"
-
-# Save this line for later use at the 
-    if escalation_score is None:
-        escalation_text = "📉 Escalation Potential: Unknown (Checklist not completed)\n"
-        escalation_text += "⚠️ *This section was not completed. Escalation potential is unknown.*\n"
-        hybrid_score = 0  # ✅ fallback so it's defined for generate_risk_snippet
-    else:
-        escalation_text = f"🧨 **Escalation Potential: {risk_level} ({escalation_score}/{sum(w for _, w in ESCALATION_QUESTIONS)})**\n"
-        escalation_text += "This score comes directly from the safety checklist and functions as a standalone escalation risk score.\n"
-        escalation_text += "It indicates how many serious risk factors are present based on your answers to the safety checklist.\n"
-        # Derive top_label from the strongest top_patterns across all messages
-    top_label = None
-    if results:
-        sorted_patterns = sorted(
-            [(label, score) for r in results for label, score in r[0][2]],
-            key=lambda x: x[1],
-            reverse=True
-    )
-        if sorted_patterns:
-            top_label = f"{sorted_patterns[0][0]} – {int(round(sorted_patterns[0][1] * 100))}%"
-        if top_label is None:
-            top_label = "Unknown – 0%"
-    out += generate_risk_snippet(composite_abuse, top_label, hybrid_score if escalation_score is not None else 0, 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):
-        label = tone if tone else "none"
-        out += f"• Message {i+1}: *{label}*\n"
-    print(f"DEBUG: avg_darvo = {avg_darvo}")
-    pattern_labels = [r[0][2][0][0] for r in results]  # top label for each message
-    timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, pattern_labels)
-    out += "\n\n" + escalation_text
-    return out, timeline_image
-    
-message_date_pairs = [
-    (
-        gr.Textbox(label=f"Message {i+1}"),
-        gr.Textbox(label=f"Date {i+1} (optional)", placeholder="YYYY-MM-DD")
-    )
-    for i in range(3)
-]
-textbox_inputs = [item for pair in message_date_pairs for item in pair]
-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",
-    allow_flagging="manual"
-)
-
-if __name__ == "__main__":
-    iface.launch()