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Running
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Delete app.py
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app.py
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import gradio as gr
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import torch
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import numpy as np
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from transformers import pipeline, RobertaForSequenceClassification, RobertaTokenizer
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from motif_tagging import detect_motifs
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import re
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import matplotlib.pyplot as plt
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import io
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from PIL import Image
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from datetime import datetime
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from transformers import pipeline as hf_pipeline # prevent name collision with gradio pipeline
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def get_emotion_profile(text):
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emotions = emotion_pipeline(text)
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if isinstance(emotions, list) and isinstance(emotions[0], list):
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emotions = emotions[0]
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return {e['label'].lower(): round(e['score'], 3) for e in emotions}
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# Emotion model (no retraining needed)
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emotion_pipeline = hf_pipeline(
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"text-classification",
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model="j-hartmann/emotion-english-distilroberta-base",
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top_k=None,
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truncation=True
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)
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# --- Timeline Visualization Function ---
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def generate_abuse_score_chart(dates, scores, labels):
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import matplotlib.pyplot as plt
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import io
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from PIL import Image
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from datetime import datetime
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import re
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# Determine if all entries are valid dates
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if all(re.match(r"\d{4}-\d{2}-\d{2}", d) for d in dates):
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parsed_x = [datetime.strptime(d, "%Y-%m-%d") for d in dates]
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x_labels = [d.strftime("%Y-%m-%d") for d in parsed_x]
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else:
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parsed_x = list(range(1, len(dates) + 1))
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x_labels = [f"Message {i+1}" for i in range(len(dates))]
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fig, ax = plt.subplots(figsize=(8, 3))
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ax.plot(parsed_x, scores, marker='o', linestyle='-', color='darkred', linewidth=2)
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for x, y in zip(parsed_x, scores):
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ax.text(x, y + 2, f"{int(y)}%", ha='center', fontsize=8, color='black')
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ax.set_xticks(parsed_x)
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ax.set_xticklabels(x_labels)
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ax.set_xlabel("") # No axis label
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ax.set_ylabel("Abuse Score (%)")
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ax.set_ylim(0, 105)
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ax.grid(True)
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plt.tight_layout()
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buf = io.BytesIO()
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plt.savefig(buf, format='png')
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buf.seek(0)
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return Image.open(buf)
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# --- Abuse Model ---
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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model_name = "SamanthaStorm/tether-multilabel-v3"
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
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LABELS = [
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"recovery", "control", "gaslighting", "guilt tripping", "dismissiveness", "blame shifting",
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"nonabusive","projection", "insults", "contradictory statements", "obscure language"
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]
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THRESHOLDS = {
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"recovery": 0.55,
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"control": 0.25,
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"gaslighting": 0.50,
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"guilt tripping": .20,
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"dismissiveness": 0.15,
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"blame shifting": 0.30,
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"projection": 0.40,
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"insults": 0.30,
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"contradictory statements": 0.50,
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"obscure language": 0.40,
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"nonabusive": 1.5
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}
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PATTERN_WEIGHTS = {
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"recovery": 0.7,
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"control": 1.4,
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"gaslighting": 1.50,
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"guilt tripping": 1.2,
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"dismissiveness": 0.9,
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"blame shifting": 0.8,
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"projection": 0.5,
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"insults": 1.4,
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"contradictory statements": 1.0,
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"obscure language": 0.9,
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"nonabusive": 0.01
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}
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RISK_STAGE_LABELS = {
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1: "🌀 Risk Stage: Tension-Building\nThis message reflects rising emotional pressure or subtle control attempts.",
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2: "🔥 Risk Stage: Escalation\nThis message includes direct or aggressive patterns, suggesting active harm.",
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3: "🌧️ Risk Stage: Reconciliation\nThis message reflects a reset attempt—apologies or emotional repair without accountability.",
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4: "🌸 Risk Stage: Calm / Honeymoon\nThis message appears supportive but may follow prior harm, minimizing it."
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}
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ESCALATION_QUESTIONS = [
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("Partner has access to firearms or weapons", 4),
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("Partner threatened to kill you", 3),
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("Partner threatened you with a weapon", 3),
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("Partner has ever choked you, even if you considered it consensual at the time", 4),
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("Partner injured or threatened your pet(s)", 3),
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("Partner has broken your things, punched or kicked walls, or thrown things ", 2),
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("Partner forced or coerced you into unwanted sexual acts", 3),
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("Partner threatened to take away your children", 2),
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("Violence has increased in frequency or severity", 3),
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("Partner monitors your calls/GPS/social media", 2)
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]
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DARVO_PATTERNS = [
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"blame shifting", # "You're the reason this happens"
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"projection", # "You're the abusive one"
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"deflection", # "This isn't about that"
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"dismissiveness", # "You're overreacting"
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"insults", # Personal attacks that redirect attention
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"aggression", # Escalates tone to destabilize
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"recovery phase", # Sudden affection following aggression
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"contradictory statements" # “I never said that” immediately followed by a version of what they said
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]
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DARVO_MOTIFS = [
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"I never said that.", "You’re imagining things.", "That never happened.",
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"You’re making a big deal out of nothing.", "It was just a joke.", "You��re too sensitive.",
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"I don’t know what you’re talking about.", "You’re overreacting.", "I didn’t mean it that way.",
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"You’re twisting my words.", "You’re remembering it wrong.", "You’re always looking for something to complain about.",
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"You’re just trying to start a fight.", "I was only trying to help.", "You’re making things up.",
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"You’re blowing this out of proportion.", "You’re being paranoid.", "You’re too emotional.",
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"You’re always so dramatic.", "You’re just trying to make me look bad.",
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"You’re crazy.", "You’re the one with the problem.", "You’re always so negative.",
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"You’re just trying to control me.", "You’re the abusive one.", "You’re trying to ruin my life.",
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"You’re just jealous.", "You’re the one who needs help.", "You’re always playing the victim.",
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"You’re the one causing all the problems.", "You’re just trying to make me feel guilty.",
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"You’re the one who can’t let go of the past.", "You’re the one who’s always angry.",
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"You’re the one who’s always complaining.", "You’re the one who’s always starting arguments.",
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"You’re the one who’s always making things worse.", "You’re the one who’s always making me feel bad.",
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"You’re the one who’s always making me look like the bad guy.",
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"You’re the one who’s always making me feel like a failure.",
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"You’re the one who’s always making me feel like I’m not good enough.",
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"I can’t believe you’re doing this to me.", "You’re hurting me.",
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"You’re making me feel like a terrible person.", "You’re always blaming me for everything.",
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"You’re the one who’s abusive.", "You’re the one who’s controlling.", "You’re the one who’s manipulative.",
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"You’re the one who’s toxic.", "You’re the one who’s gaslighting me.",
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"You’re the one who’s always putting me down.", "You’re the one who’s always making me feel bad.",
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"You’re the one who’s always making me feel like I’m not good enough.",
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"You’re the one who’s always making me feel like I’m the problem.",
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"You’re the one who’s always making me feel like I’m the bad guy.",
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"You’re the one who’s always making me feel like I’m the villain.",
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"You’re the one who’s always making me feel like I’m the one who needs to change.",
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"You’re the one who’s always making me feel like I’m the one who’s wrong.",
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"You’re the one who’s always making me feel like I’m the one who’s crazy.",
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"You’re the one who’s always making me feel like I’m the one who’s abusive.",
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"You’re the one who’s always making me feel like I’m the one who’s toxic."
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]
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def get_emotional_tone_tag(emotions, sentiment, patterns, abuse_score):
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sadness = emotions.get("sadness", 0)
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joy = emotions.get("joy", 0)
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neutral = emotions.get("neutral", 0)
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disgust = emotions.get("disgust", 0)
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anger = emotions.get("anger", 0)
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fear = emotions.get("fear", 0)
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disgust = emotions.get("disgust", 0)
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# 1. Performative Regret
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if (
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sadness > 0.4 and
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any(p in patterns for p in ["blame shifting", "guilt tripping", "recovery phase"]) and
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(sentiment == "undermining" or abuse_score > 40)
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):
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return "performative regret"
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# 2. Coercive Warmth
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if (
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(joy > 0.3 or sadness > 0.4) and
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any(p in patterns for p in ["control", "gaslighting"]) and
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sentiment == "undermining"
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):
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return "coercive warmth"
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# 3. Cold Invalidation
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if (
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(neutral + disgust) > 0.5 and
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any(p in patterns for p in ["dismissiveness", "projection", "obscure language"]) and
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sentiment == "undermining"
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):
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return "cold invalidation"
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# 4. Genuine Vulnerability
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if (
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(sadness + fear) > 0.5 and
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sentiment == "supportive" and
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all(p in ["recovery phase"] for p in patterns)
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):
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return "genuine vulnerability"
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# 5. Emotional Threat
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if (
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(anger + disgust) > 0.5 and
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any(p in patterns for p in ["control", "threat", "insults", "dismissiveness"]) and
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sentiment == "undermining"
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):
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return "emotional threat"
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# 6. Weaponized Sadness
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if (
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sadness > 0.6 and
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any(p in patterns for p in ["guilt tripping", "projection"]) and
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sentiment == "undermining"
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):
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return "weaponized sadness"
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# 7. Toxic Resignation
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if (
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neutral > 0.5 and
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any(p in patterns for p in ["dismissiveness", "obscure language"]) and
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sentiment == "undermining"
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):
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return "toxic resignation"
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# 8. Aggressive Dismissal
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if (
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anger > 0.5 and
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any(p in patterns for p in ["aggression", "insults", "control"]) and
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sentiment == "undermining"
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):
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return "aggressive dismissal"
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# 9. Deflective Hostility
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if (
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(0.2 < anger < 0.7 or 0.2 < disgust < 0.7) and
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any(p in patterns for p in ["deflection", "projection"]) and
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sentiment == "undermining"
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):
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return "deflective hostility"
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# 10. Mocking Detachment
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if (
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(neutral + joy) > 0.5 and
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any(p in patterns for p in ["mockery", "insults", "projection"]) and
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sentiment == "undermining"
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):
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return "mocking detachment"
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# 11. Contradictory Gaslight
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if (
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(joy + anger + sadness) > 0.5 and
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any(p in patterns for p in ["gaslighting", "contradictory statements"]) and
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sentiment == "undermining"
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):
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return "contradictory gaslight"
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# 12. Calculated Neutrality
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if (
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neutral > 0.6 and
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any(p in patterns for p in ["obscure language", "deflection", "dismissiveness"]) and
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sentiment == "undermining"
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):
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return "calculated neutrality"
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# 13. Forced Accountability Flip
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if (
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(anger + disgust) > 0.5 and
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any(p in patterns for p in ["blame shifting", "manipulation", "projection"]) and
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sentiment == "undermining"
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):
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return "forced accountability flip"
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# 14. Conditional Affection
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if (
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joy > 0.4 and
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any(p in patterns for p in ["apology baiting", "control", "recovery phase"]) and
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sentiment == "undermining"
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):
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return "conditional affection"
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if (
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(anger + disgust) > 0.5 and
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any(p in patterns for p in ["blame shifting", "projection", "deflection"]) and
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sentiment == "undermining"
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):
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return "forced accountability flip"
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# Emotional Instability Fallback
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if (
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(anger + sadness + disgust) > 0.6 and
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sentiment == "undermining"
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):
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return "emotional instability"
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return None
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def detect_contradiction(message):
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patterns = [
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(r"\b(i love you).{0,15}(i hate you|you ruin everything)", re.IGNORECASE),
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(r"\b(i’m sorry).{0,15}(but you|if you hadn’t)", re.IGNORECASE),
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(r"\b(i’m trying).{0,15}(you never|why do you)", re.IGNORECASE),
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(r"\b(do what you want).{0,15}(you’ll regret it|i always give everything)", re.IGNORECASE),
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(r"\b(i don’t care).{0,15}(you never think of me)", re.IGNORECASE),
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(r"\b(i guess i’m just).{0,15}(the bad guy|worthless|never enough)", re.IGNORECASE)
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]
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return any(re.search(p, message, flags) for p, flags in patterns)
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def calculate_darvo_score(patterns, sentiment_before, sentiment_after, motifs_found, contradiction_flag=False):
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# Count all detected DARVO-related patterns
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pattern_hits = sum(1 for p in patterns if p.lower() in DARVO_PATTERNS)
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# Sentiment delta
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sentiment_shift_score = max(0.0, sentiment_after - sentiment_before)
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# Match against DARVO motifs more loosely
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motif_hits = sum(
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any(phrase.lower() in motif.lower() or motif.lower() in phrase.lower()
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for phrase in DARVO_MOTIFS)
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for motif in motifs_found
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)
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motif_score = motif_hits / max(len(DARVO_MOTIFS), 1)
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# Contradiction still binary
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contradiction_score = 1.0 if contradiction_flag else 0.0
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# Final DARVO score
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return round(min(
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0.3 * pattern_hits +
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0.3 * sentiment_shift_score +
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0.25 * motif_score +
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0.15 * contradiction_score, 1.0
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), 3)
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def detect_weapon_language(text):
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weapon_keywords = [
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"knife", "knives", "stab", "cut you", "cutting",
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"gun", "shoot", "rifle", "firearm", "pistol",
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"bomb", "blow up", "grenade", "explode",
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"weapon", "armed", "loaded", "kill you", "take you out"
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]
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text_lower = text.lower()
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return any(word in text_lower for word in weapon_keywords)
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def get_risk_stage(patterns, sentiment):
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if "threat" in patterns or "insults" in patterns:
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return 2
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elif "recovery phase" in patterns:
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return 3
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elif "control" in patterns or "guilt tripping" in patterns:
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return 1
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elif sentiment == "supportive" and any(p in patterns for p in ["projection", "dismissiveness"]):
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return 4
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return 1
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def generate_risk_snippet(abuse_score, top_label, escalation_score, stage):
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import re
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# Extract aggression score if aggression is detected
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if isinstance(top_label, str) and "aggression" in top_label.lower():
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try:
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match = re.search(r"\(?(\d+)\%?\)?", top_label)
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aggression_score = int(match.group(1)) / 100 if match else 0
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except:
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aggression_score = 0
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else:
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aggression_score = 0
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# Revised risk logic
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364 |
-
if abuse_score >= 85 or escalation_score >= 16:
|
365 |
-
risk_level = "high"
|
366 |
-
elif abuse_score >= 60 or escalation_score >= 8 or aggression_score >= 0.25:
|
367 |
-
risk_level = "moderate"
|
368 |
-
elif stage == 2 and abuse_score >= 40:
|
369 |
-
risk_level = "moderate"
|
370 |
-
else:
|
371 |
-
risk_level = "low"
|
372 |
-
|
373 |
-
if isinstance(top_label, str) and " – " in top_label:
|
374 |
-
pattern_label, pattern_score = top_label.split(" – ")
|
375 |
-
else:
|
376 |
-
pattern_label = str(top_label) if top_label is not None else "Unknown"
|
377 |
-
pattern_score = ""
|
378 |
-
|
379 |
-
WHY_FLAGGED = {
|
380 |
-
"control": "This message may reflect efforts to restrict someone’s autonomy, even if it's framed as concern or care.",
|
381 |
-
"gaslighting": "This message could be manipulating someone into questioning their perception or feelings.",
|
382 |
-
"dismissiveness": "This message may include belittling, invalidating, or ignoring the other person’s experience.",
|
383 |
-
"insults": "Direct insults often appear in escalating abusive dynamics and can erode emotional safety.",
|
384 |
-
"threat": "This message includes threatening language, which is a strong predictor of harm.",
|
385 |
-
"blame shifting": "This message may redirect responsibility to avoid accountability, especially during conflict.",
|
386 |
-
"guilt tripping": "This message may induce guilt in order to control or manipulate behavior.",
|
387 |
-
"recovery phase": "This message may be part of a tension-reset cycle, appearing kind but avoiding change.",
|
388 |
-
"projection": "This message may involve attributing the abuser’s own behaviors to the victim.",
|
389 |
-
"contradictory statements": "This message may contain internal contradictions used to confuse, destabilize, or deflect responsibility.",
|
390 |
-
"obscure language": "This message may use overly formal, vague, or complex language to obscure meaning or avoid accountability.",
|
391 |
-
"default": "This message contains language patterns that may affect safety, clarity, or emotional autonomy."
|
392 |
-
}
|
393 |
-
|
394 |
-
explanation = WHY_FLAGGED.get(pattern_label.lower(), WHY_FLAGGED["default"])
|
395 |
-
|
396 |
-
base = f"\n\n🛑 Risk Level: {risk_level.capitalize()}\n"
|
397 |
-
base += f"This message shows strong indicators of **{pattern_label}**. "
|
398 |
-
|
399 |
-
if risk_level == "high":
|
400 |
-
base += "The language may reflect patterns of emotional control, even when expressed in soft or caring terms.\n"
|
401 |
-
elif risk_level == "moderate":
|
402 |
-
base += "There are signs of emotional pressure or verbal aggression that may escalate if repeated.\n"
|
403 |
-
else:
|
404 |
-
base += "The message does not strongly indicate abuse, but it's important to monitor for patterns.\n"
|
405 |
-
|
406 |
-
base += f"\n💡 *Why this might be flagged:*\n{explanation}\n"
|
407 |
-
base += f"\nDetected Pattern: **{pattern_label} ({pattern_score})**\n"
|
408 |
-
base += "🧠 You can review the pattern in context. This tool highlights possible dynamics—not judgments."
|
409 |
-
return base
|
410 |
-
|
411 |
-
WHY_FLAGGED = {
|
412 |
-
"control": "This message may reflect efforts to restrict someone’s autonomy, even if it's framed as concern or care.",
|
413 |
-
"gaslighting": "This message could be manipulating someone into questioning their perception or feelings.",
|
414 |
-
"dismissiveness": "This message may include belittling, invalidating, or ignoring the other person’s experience.",
|
415 |
-
"insults": "Direct insults often appear in escalating abusive dynamics and can erode emotional safety.",
|
416 |
-
"threat": "This message includes threatening language, which is a strong predictor of harm.",
|
417 |
-
"blame shifting": "This message may redirect responsibility to avoid accountability, especially during conflict.",
|
418 |
-
"guilt tripping": "This message may induce guilt in order to control or manipulate behavior.",
|
419 |
-
"recovery phase": "This message may be part of a tension-reset cycle, appearing kind but avoiding change.",
|
420 |
-
"projection": "This message may involve attributing the abuser’s own behaviors to the victim.",
|
421 |
-
"contradictory statements": "This message may contain internal contradictions used to confuse, destabilize, or deflect responsibility.",
|
422 |
-
"obscure language": "This message may use overly formal, vague, or complex language to obscure meaning or avoid accountability.",
|
423 |
-
"default": "This message contains language patterns that may affect safety, clarity, or emotional autonomy."
|
424 |
-
}
|
425 |
-
explanation = WHY_FLAGGED.get(pattern_label.lower(), WHY_FLAGGED["default"])
|
426 |
-
|
427 |
-
base = f"\n\n🛑 Risk Level: {risk_level.capitalize()}\n"
|
428 |
-
base += f"This message shows strong indicators of **{pattern_label}**. "
|
429 |
-
|
430 |
-
if risk_level == "high":
|
431 |
-
base += "The language may reflect patterns of emotional control, even when expressed in soft or caring terms.\n"
|
432 |
-
elif risk_level == "moderate":
|
433 |
-
base += "There are signs of emotional pressure or indirect control that may escalate if repeated.\n"
|
434 |
-
else:
|
435 |
-
base += "The message does not strongly indicate abuse, but it's important to monitor for patterns.\n"
|
436 |
-
|
437 |
-
base += f"\n💡 *Why this might be flagged:*\n{explanation}\n"
|
438 |
-
base += f"\nDetected Pattern: **{pattern_label} ({pattern_score})**\n"
|
439 |
-
base += "🧠 You can review the pattern in context. This tool highlights possible dynamics—not judgments."
|
440 |
-
return base
|
441 |
-
def compute_abuse_score(matched_scores, sentiment):
|
442 |
-
if not matched_scores:
|
443 |
-
return 0
|
444 |
-
|
445 |
-
# Weighted average of passed patterns
|
446 |
-
weighted_total = sum(score * weight for _, score, weight in matched_scores)
|
447 |
-
weight_sum = sum(weight for _, _, weight in matched_scores)
|
448 |
-
base_score = (weighted_total / weight_sum) * 100
|
449 |
-
|
450 |
-
# Boost for pattern count
|
451 |
-
pattern_count = len(matched_scores)
|
452 |
-
scale = 1.0 + 0.25 * max(0, pattern_count - 1) # 1.25x for 2, 1.5x for 3+
|
453 |
-
scaled_score = base_score * scale
|
454 |
-
|
455 |
-
# Pattern floors
|
456 |
-
FLOORS = {
|
457 |
-
"threat": 70,
|
458 |
-
"control": 40,
|
459 |
-
"gaslighting": 30,
|
460 |
-
"insults": 25,
|
461 |
-
"aggression": 40
|
462 |
-
}
|
463 |
-
floor = max(FLOORS.get(label, 0) for label, _, _ in matched_scores)
|
464 |
-
adjusted_score = max(scaled_score, floor)
|
465 |
-
|
466 |
-
# Sentiment tweak
|
467 |
-
if sentiment == "undermining" and adjusted_score < 50:
|
468 |
-
adjusted_score += 10
|
469 |
-
|
470 |
-
return min(adjusted_score, 100)
|
471 |
-
|
472 |
-
|
473 |
-
def analyze_single_message(text, thresholds):
|
474 |
-
motif_hits, matched_phrases = detect_motifs(text)
|
475 |
-
|
476 |
-
# Get emotion profile
|
477 |
-
emotion_profile = get_emotion_profile(text)
|
478 |
-
sentiment_score = emotion_profile.get("anger", 0) + emotion_profile.get("disgust", 0)
|
479 |
-
|
480 |
-
# Get model scores
|
481 |
-
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
|
482 |
-
with torch.no_grad():
|
483 |
-
outputs = model(**inputs)
|
484 |
-
scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy()
|
485 |
-
|
486 |
-
# Sentiment override if neutral is high while critical thresholds are passed
|
487 |
-
if emotion_profile.get("neutral", 0) > 0.85 and any(
|
488 |
-
scores[LABELS.index(l)] > thresholds[l]
|
489 |
-
for l in ["control", "threat", "blame shifting"]
|
490 |
-
):
|
491 |
-
sentiment = "undermining"
|
492 |
-
else:
|
493 |
-
sentiment = "undermining" if sentiment_score > 0.25 else "supportive"
|
494 |
-
|
495 |
-
weapon_flag = detect_weapon_language(text)
|
496 |
-
|
497 |
-
adjusted_thresholds = {
|
498 |
-
k: v + 0.05 if sentiment == "supportive" else v
|
499 |
-
for k, v in thresholds.items()
|
500 |
-
}
|
501 |
-
|
502 |
-
contradiction_flag = detect_contradiction(text)
|
503 |
-
|
504 |
-
threshold_labels = [
|
505 |
-
label for label, score in zip(LABELS, scores)
|
506 |
-
if score > adjusted_thresholds[label]
|
507 |
-
]
|
508 |
-
tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, 0)
|
509 |
-
motifs = [phrase for _, phrase in matched_phrases]
|
510 |
-
|
511 |
-
darvo_score = calculate_darvo_score(
|
512 |
-
threshold_labels,
|
513 |
-
sentiment_before=0.0,
|
514 |
-
sentiment_after=sentiment_score,
|
515 |
-
motifs_found=motifs,
|
516 |
-
contradiction_flag=contradiction_flag
|
517 |
-
)
|
518 |
-
|
519 |
-
top_patterns = sorted(
|
520 |
-
[(label, score) for label, score in zip(LABELS, scores)],
|
521 |
-
key=lambda x: x[1],
|
522 |
-
reverse=True
|
523 |
-
)[:2]
|
524 |
-
# Post-threshold validation: strip recovery if it occurs with undermining sentiment
|
525 |
-
if "recovery" in threshold_labels and tone_tag == "forced accountability flip":
|
526 |
-
threshold_labels.remove("recovery")
|
527 |
-
top_patterns = [p for p in top_patterns if p[0] != "recovery"]
|
528 |
-
print("⚠️ Removing 'recovery' due to undermining sentiment (not genuine repair)")
|
529 |
-
|
530 |
-
matched_scores = [
|
531 |
-
(label, score, PATTERN_WEIGHTS.get(label, 1.0))
|
532 |
-
for label, score in zip(LABELS, scores)
|
533 |
-
if score > adjusted_thresholds[label]
|
534 |
-
]
|
535 |
-
|
536 |
-
abuse_score_raw = compute_abuse_score(matched_scores, sentiment)
|
537 |
-
abuse_score = abuse_score_raw
|
538 |
-
|
539 |
-
# Risk stage logic
|
540 |
-
stage = get_risk_stage(threshold_labels, sentiment) if threshold_labels else 1
|
541 |
-
if weapon_flag and stage < 2:
|
542 |
-
stage = 2
|
543 |
-
if weapon_flag:
|
544 |
-
abuse_score_raw = min(abuse_score_raw + 25, 100)
|
545 |
-
|
546 |
-
abuse_score = min(
|
547 |
-
abuse_score_raw,
|
548 |
-
100 if "threat" in threshold_labels or "control" in threshold_labels else 95
|
549 |
-
)
|
550 |
-
|
551 |
-
# Tag must happen after abuse score is finalized
|
552 |
-
tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score)
|
553 |
-
|
554 |
-
# ---- Profanity + Anger Override Logic ----
|
555 |
-
profane_words = {"fuck", "fucking", "bitch", "shit", "cunt", "ho", "asshole", "dick", "whore", "slut"}
|
556 |
-
tokens = set(text.lower().split())
|
557 |
-
has_profane = any(word in tokens for word in profane_words)
|
558 |
-
|
559 |
-
anger_score = emotion_profile.get("Anger", 0)
|
560 |
-
short_text = len(tokens) <= 10
|
561 |
-
insult_score = next((s for l, s in top_patterns if l == "insults"), 0)
|
562 |
-
|
563 |
-
if has_profane and anger_score > 0.75 and short_text:
|
564 |
-
print("⚠️ Profanity + Anger Override Triggered")
|
565 |
-
top_patterns = sorted(top_patterns, key=lambda x: x[1], reverse=True)
|
566 |
-
if top_patterns[0][0] != "insults":
|
567 |
-
top_patterns.insert(0, ("insults", insult_score))
|
568 |
-
if "insults" not in threshold_labels:
|
569 |
-
threshold_labels.append("insults")
|
570 |
-
top_patterns = [("insults", insult_score)] + [p for p in top_patterns if p[0] != "insults"]
|
571 |
-
# Debug
|
572 |
-
print(f"Emotional Tone Tag: {tone_tag}")
|
573 |
-
# Debug
|
574 |
-
print(f"Emotional Tone Tag: {tone_tag}")
|
575 |
-
print("Emotion Profile:")
|
576 |
-
for emotion, score in emotion_profile.items():
|
577 |
-
print(f" {emotion.capitalize():10}: {score}")
|
578 |
-
print("\n--- Debug Info ---")
|
579 |
-
print(f"Text: {text}")
|
580 |
-
print(f"Sentiment (via emotion): {sentiment} (score: {round(sentiment_score, 3)})")
|
581 |
-
print("Abuse Pattern Scores:")
|
582 |
-
for label, score in zip(LABELS, scores):
|
583 |
-
passed = "✅" if score > adjusted_thresholds[label] else "❌"
|
584 |
-
print(f" {label:25} → {score:.3f} {passed}")
|
585 |
-
print(f"Matched for score: {[(l, round(s, 3)) for l, s, _ in matched_scores]}")
|
586 |
-
print(f"Abuse Score Raw: {round(abuse_score_raw, 1)}")
|
587 |
-
print(f"Motifs: {motifs}")
|
588 |
-
print(f"Contradiction: {contradiction_flag}")
|
589 |
-
print("------------------\n")
|
590 |
-
|
591 |
-
return abuse_score, threshold_labels, top_patterns, {"label": sentiment}, stage, darvo_score, tone_tag
|
592 |
-
|
593 |
-
def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
|
594 |
-
none_selected_checked = answers_and_none[-1]
|
595 |
-
responses_checked = any(answers_and_none[:-1])
|
596 |
-
none_selected = not responses_checked and none_selected_checked
|
597 |
-
|
598 |
-
if none_selected:
|
599 |
-
escalation_score = None
|
600 |
-
risk_level = "unknown"
|
601 |
-
else:
|
602 |
-
escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a)
|
603 |
-
|
604 |
-
messages = [msg1, msg2, msg3]
|
605 |
-
dates = [date1, date2, date3]
|
606 |
-
active = [(m, d) for m, d in zip(messages, dates) if m.strip()]
|
607 |
-
if not active:
|
608 |
-
return "Please enter at least one message."
|
609 |
-
|
610 |
-
# Run model on messages
|
611 |
-
results = [(analyze_single_message(m, THRESHOLDS.copy()), d) for m, d in active]
|
612 |
-
# --- Combined Abuse Escalation Scoring ---
|
613 |
-
|
614 |
-
# Extract predicted abuse labels from all messages
|
615 |
-
predicted_labels = [label for r in results for label, _ in r[0][2]]
|
616 |
-
|
617 |
-
# Categorize by severity
|
618 |
-
high = {'control'}
|
619 |
-
moderate = {
|
620 |
-
'gaslighting', 'dismissiveness', 'obscure language',
|
621 |
-
'insults', 'contradictory statements', 'guilt tripping'
|
622 |
-
}
|
623 |
-
low = {'blame shifting', 'projection', 'recovery phase'}
|
624 |
-
|
625 |
-
# Count severity types
|
626 |
-
counts = {'high': 0, 'moderate': 0, 'low': 0}
|
627 |
-
for label in predicted_labels:
|
628 |
-
if label in high:
|
629 |
-
counts['high'] += 1
|
630 |
-
elif label in moderate:
|
631 |
-
counts['moderate'] += 1
|
632 |
-
elif label in low:
|
633 |
-
counts['low'] += 1
|
634 |
-
|
635 |
-
# Derive abuse_risk from combinations
|
636 |
-
if counts['high'] >= 2 and counts['moderate'] >= 2:
|
637 |
-
abuse_risk = 'Critical'
|
638 |
-
elif (counts['high'] >= 2 and counts['moderate'] >= 1) or (counts['moderate'] >= 3) or (counts['high'] >= 1 and counts['moderate'] >= 2):
|
639 |
-
abuse_risk = 'High'
|
640 |
-
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):
|
641 |
-
abuse_risk = 'Moderate'
|
642 |
-
else:
|
643 |
-
abuse_risk = 'Low'
|
644 |
-
|
645 |
-
# Combine abuse_risk and checklist score into final risk_level
|
646 |
-
if escalation_score is not None:
|
647 |
-
if escalation_score >= 8 or abuse_risk == 'Critical':
|
648 |
-
risk_level = 'Critical'
|
649 |
-
elif escalation_score >= 5 or abuse_risk == 'High':
|
650 |
-
risk_level = 'High'
|
651 |
-
elif escalation_score >= 2 or abuse_risk == 'Moderate':
|
652 |
-
risk_level = 'Moderate'
|
653 |
-
else:
|
654 |
-
risk_level = 'Low'
|
655 |
-
abuse_scores = [r[0][0] for r in results]
|
656 |
-
top_labels = [r[0][1][0] if r[0][1] else r[0][2][0][0] for r in results]
|
657 |
-
top_scores = [r[0][2][0][1] for r in results]
|
658 |
-
sentiments = [r[0][3]['label'] for r in results]
|
659 |
-
stages = [r[0][4] for r in results]
|
660 |
-
darvo_scores = [r[0][5] for r in results]
|
661 |
-
tone_tags= [r[0][6] for r in results]
|
662 |
-
dates_used = [r[1] or "Undated" for r in results] # Store dates for future mapping
|
663 |
-
# Calculate escalation bump *after* model results exist
|
664 |
-
escalation_bump = 0
|
665 |
-
for result, _ in results:
|
666 |
-
abuse_score, threshold_labels, top_patterns, sentiment, stage, darvo_score, tone_tag = result
|
667 |
-
if darvo_score > 0.65:
|
668 |
-
escalation_bump += 3
|
669 |
-
if tone_tag in ["forced accountability flip", "emotional threat"]:
|
670 |
-
escalation_bump += 2
|
671 |
-
if abuse_score > 80:
|
672 |
-
escalation_bump += 2
|
673 |
-
if stage == 2:
|
674 |
-
escalation_bump += 3
|
675 |
-
|
676 |
-
# Now we can safely calculate hybrid_score
|
677 |
-
hybrid_score = escalation_score + escalation_bump if escalation_score is not None else 0
|
678 |
-
risk_level = (
|
679 |
-
"High" if hybrid_score >= 16 else
|
680 |
-
"Moderate" if hybrid_score >= 8 else
|
681 |
-
"Low"
|
682 |
-
)
|
683 |
-
|
684 |
-
# Now compute scores and allow override
|
685 |
-
abuse_scores = [r[0][0] for r in results]
|
686 |
-
stages = [r[0][4] for r in results]
|
687 |
-
|
688 |
-
# Post-check override (e.g. stage 2 or high abuse score forces Moderate risk)
|
689 |
-
if any(score > 70 for score in abuse_scores) or any(stage == 2 for stage in stages):
|
690 |
-
if risk_level == "Low":
|
691 |
-
risk_level = "Moderate"
|
692 |
-
|
693 |
-
for result, date in results:
|
694 |
-
assert len(result) == 7, "Unexpected output from analyze_single_message"
|
695 |
-
|
696 |
-
# --- Composite Abuse Score using compute_abuse_score ---
|
697 |
-
composite_abuse_scores = []
|
698 |
-
|
699 |
-
for result, _ in results:
|
700 |
-
_, _, top_patterns, sentiment, _, _, _ = result
|
701 |
-
matched_scores = [(label, score, PATTERN_WEIGHTS.get(label, 1.0)) for label, score in top_patterns]
|
702 |
-
final_score = compute_abuse_score(matched_scores, sentiment["label"])
|
703 |
-
composite_abuse_scores.append(final_score)
|
704 |
-
|
705 |
-
composite_abuse = int(round(sum(composite_abuse_scores) / len(composite_abuse_scores)))
|
706 |
-
|
707 |
-
|
708 |
-
|
709 |
-
most_common_stage = max(set(stages), key=stages.count)
|
710 |
-
stage_text = RISK_STAGE_LABELS[most_common_stage]
|
711 |
-
|
712 |
-
avg_darvo = round(sum(darvo_scores) / len(darvo_scores), 3)
|
713 |
-
darvo_blurb = ""
|
714 |
-
if avg_darvo > 0.25:
|
715 |
-
level = "moderate" if avg_darvo < 0.65 else "high"
|
716 |
-
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."
|
717 |
-
|
718 |
-
out = f"Abuse Intensity: {composite_abuse}%\n"
|
719 |
-
out += "📊 This reflects the strength and severity of detected abuse patterns in the message(s).\n\n"
|
720 |
-
|
721 |
-
# Save this line for later use at the
|
722 |
-
if escalation_score is None:
|
723 |
-
escalation_text = "📉 Escalation Potential: Unknown (Checklist not completed)\n"
|
724 |
-
escalation_text += "⚠️ *This section was not completed. Escalation potential is unknown.*\n"
|
725 |
-
hybrid_score = 0 # ✅ fallback so it's defined for generate_risk_snippet
|
726 |
-
else:
|
727 |
-
escalation_text = f"🧨 **Escalation Potential: {risk_level} ({escalation_score}/{sum(w for _, w in ESCALATION_QUESTIONS)})**\n"
|
728 |
-
escalation_text += "This score comes directly from the safety checklist and functions as a standalone escalation risk score.\n"
|
729 |
-
escalation_text += "It indicates how many serious risk factors are present based on your answers to the safety checklist.\n"
|
730 |
-
# Derive top_label from the strongest top_patterns across all messages
|
731 |
-
top_label = None
|
732 |
-
if results:
|
733 |
-
sorted_patterns = sorted(
|
734 |
-
[(label, score) for r in results for label, score in r[0][2]],
|
735 |
-
key=lambda x: x[1],
|
736 |
-
reverse=True
|
737 |
-
)
|
738 |
-
if sorted_patterns:
|
739 |
-
top_label = f"{sorted_patterns[0][0]} – {int(round(sorted_patterns[0][1] * 100))}%"
|
740 |
-
if top_label is None:
|
741 |
-
top_label = "Unknown – 0%"
|
742 |
-
out += generate_risk_snippet(composite_abuse, top_label, hybrid_score if escalation_score is not None else 0, most_common_stage)
|
743 |
-
out += f"\n\n{stage_text}"
|
744 |
-
out += darvo_blurb
|
745 |
-
out += "\n\n🎭 **Emotional Tones Detected:**\n"
|
746 |
-
for i, tone in enumerate(tone_tags):
|
747 |
-
label = tone if tone else "none"
|
748 |
-
out += f"• Message {i+1}: *{label}*\n"
|
749 |
-
print(f"DEBUG: avg_darvo = {avg_darvo}")
|
750 |
-
pattern_labels = [r[0][2][0][0] for r in results] # top label for each message
|
751 |
-
timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, pattern_labels)
|
752 |
-
out += "\n\n" + escalation_text
|
753 |
-
return out, timeline_image
|
754 |
-
|
755 |
-
message_date_pairs = [
|
756 |
-
(
|
757 |
-
gr.Textbox(label=f"Message {i+1}"),
|
758 |
-
gr.Textbox(label=f"Date {i+1} (optional)", placeholder="YYYY-MM-DD")
|
759 |
-
)
|
760 |
-
for i in range(3)
|
761 |
-
]
|
762 |
-
textbox_inputs = [item for pair in message_date_pairs for item in pair]
|
763 |
-
quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS]
|
764 |
-
none_box = gr.Checkbox(label="None of the above")
|
765 |
-
|
766 |
-
iface = gr.Interface(
|
767 |
-
fn=analyze_composite,
|
768 |
-
inputs=textbox_inputs + quiz_boxes + [none_box],
|
769 |
-
outputs=[
|
770 |
-
gr.Textbox(label="Results"),
|
771 |
-
gr.Image(label="Abuse Score Timeline", type="pil")
|
772 |
-
],
|
773 |
-
title="Abuse Pattern Detector + Escalation Quiz",
|
774 |
-
allow_flagging="manual"
|
775 |
-
)
|
776 |
-
|
777 |
-
if __name__ == "__main__":
|
778 |
-
iface.launch()
|
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