Spaces:
Running
on
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Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -2,31 +2,58 @@ 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, AutoModelForSequenceClassification, AutoTokenizer
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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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#
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]
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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 has ever choked you", 4),
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("Partner injured or threatened your pet(s)", 3),
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("Partner has broken your things, punched walls, or thrown objects", 2),
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("Partner forced 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
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]
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def generate_abuse_score_chart(dates, scores, labels):
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try:
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except
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ax.set_title("Abuse Intensity Over Time")
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ax.set_xlabel("Date")
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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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with torch.no_grad():
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outputs = model(**inputs)
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logits = outputs.logits.squeeze(0)
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probs = torch.sigmoid(logits).numpy()
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detected_labels = [
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label for label, prob in zip(LABELS, probs)
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if prob > THRESHOLDS.get(label, 0.3)
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]
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abuse_score = (sum(probs[i] for i, label in enumerate(LABELS) if label in detected_labels) / len(LABELS)) * 100
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sentiment_result = sst_pipeline(text)[0]
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sentiment = "supportive" if sentiment_result['label'] == "POSITIVE" else "undermining"
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if "threat" in detected_labels or "insults" in detected_labels:
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stage = 2 # Escalation
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elif "control" in detected_labels or "guilt tripping" in detected_labels:
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stage = 1 # Tension building
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elif "recovery phase" in detected_labels:
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stage = 3 # Reconciliation
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else:
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stage = 1
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top_patterns = sorted(
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[(label, prob) for label, prob in zip(LABELS, probs)],
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key=lambda x: x[1],
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reverse=True
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)[:2]
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return {
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"abuse_score": int(abuse_score),
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"labels": detected_labels,
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"sentiment": sentiment,
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"stage": stage,
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"top_patterns": top_patterns,
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}
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def analyze_composite(msg1, date1, msg2, date2, msg3, date3, *answers_and_none):
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none_selected_checked = answers_and_none[-1]
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responses_checked = any(answers_and_none[:-1])
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none_selected = not responses_checked and none_selected_checked
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if none_selected:
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escalation_score = None
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risk_level = "unknown"
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else:
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escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a)
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risk_level = (
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"High" if escalation_score >= 16 else
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"Moderate" if escalation_score >= 8 else
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"Low"
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)
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messages = [msg1, msg2, msg3]
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dates = [date1, date2, date3]
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active = [(m, d) for m, d in zip(messages, dates) if m.strip()]
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if not active:
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return "Please enter at least one message."
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results = [(analyze_single_message(m), d) for m, d in active]
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abuse_scores = [r[0]["abuse_score"] for r in results]
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top_labels = [r[0]["top_patterns"][0][0] if r[0]["top_patterns"] else "None" for r in results]
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dates_used = [r[1] or "Undated" for r in results]
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stage_list = [r[0]["stage"] for r in results]
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most_common_stage = max(set(stage_list), key=stage_list.count)
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# compute the average abuse score across all active messages
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composite_abuse = int(round(sum(abuse_scores) / len(abuse_scores)))
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out = f"Abuse Intensity: {composite_abuse}%\n"
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if escalation_score is None:
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out += "Escalation Potential: Unknown (Checklist not completed)\n"
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else:
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out += f"Escalation Potential: {risk_level} ({escalation_score}/{sum(w for _, w in ESCALATION_QUESTIONS)})\n"
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timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, top_labels)
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return out, timeline_image
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# --- Gradio Interface ---
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message_date_pairs = [
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(
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gr.Textbox(label=f"Message {i+1}"),
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gr.Textbox(label=f"Date {i+1} (optional)", placeholder="YYYY-MM-DD")
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)
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for i in range(3)
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]
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quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS]
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none_box = gr.Checkbox(label="None of the above")
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iface = gr.Interface(
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fn=analyze_composite,
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inputs=textbox_inputs + quiz_boxes + [none_box],
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outputs=[
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gr.Textbox(label="Results"),
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gr.Image(label="Risk Stage Timeline", type="pil")
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],
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title="Tether Abuse Pattern Detector v2",
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allow_flagging="manual"
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)
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import torch
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import numpy as np
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from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
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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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# ——— DARVO & Risk Utilities ———
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DARVO_PATTERNS = {"blame shifting", "projection", "dismissiveness", "guilt tripping", "contradictory statements"}
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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.", "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.", "You’re making me feel like a terrible person.",
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"You’re always blaming me for everything.", "You’re the one who’s abusive.", "You’re the one who’s controlling.",
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"You’re the one who’s manipulative.", "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.", "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.", "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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PATTERN_WEIGHTS = {
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"gaslighting": 1.3,
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"control": 1.2,
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"dismissiveness": 0.8,
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"blame shifting": 0.8,
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"contradictory statements": 0.75,
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"threat": 1.5,
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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 has ever choked you", 4),
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("Partner injured or threatened your pet(s)", 3),
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("Partner has broken your things, punched walls, or thrown objects", 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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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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# ... other patterns ...
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]
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return any(re.search(pat, message, flags) for pat, 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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hits = len([p for p in patterns if p in DARVO_PATTERNS])
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p_score = hits / len(DARVO_PATTERNS)
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s_shift = max(0.0, sentiment_after - sentiment_before)
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m_hits = len([m for m in motifs_found if any(f.lower() in m.lower() for f in DARVO_MOTIFS)])
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m_score = m_hits / len(DARVO_MOTIFS)
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c_score = 1.0 if contradiction_flag else 0.0
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raw = 0.3*p_score + 0.3*s_shift + 0.25*m_score + 0.15*c_score
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return round(min(raw,1.0),3)
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def generate_risk_snippet(abuse_score, top_label, escalation_score, stage):
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label = top_label.split(" – ")[0]
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why = {
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"control": "This message may reflect efforts to restrict someone’s autonomy.",
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"gaslighting": "This message could be manipulating perception.",
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# ... other explanations ...
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}.get(label, "This message contains language patterns that may affect safety.")
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if abuse_score>=85 or escalation_score>=16:
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lvl="high"
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elif abuse_score>=60 or escalation_score>=8:
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lvl="moderate"
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else:
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lvl="low"
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return f"\n\n🛑 Risk Level: {lvl.capitalize()}\nThis message shows **{label}**.\n💡 Why: {why}\n"
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def detect_weapon_language(text):
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kws=["knife","gun","bomb","kill you","shoot","explode"]
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t=text.lower()
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return any(k in t for k in kws)
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def get_risk_stage(patterns, sentiment):
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if "threat" in patterns or "insults" in patterns: return 2
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if "control" in patterns or "guilt tripping" in patterns: return 1
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if "recovery phase" in patterns: return 3
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if sentiment=="supportive" and any(p in patterns for p in ["projection","dismissiveness"]): return 4
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return 1
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# --- Timeline Visualization ---
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def generate_abuse_score_chart(dates, scores, labels):
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try:
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parsed=[datetime.strptime(d,"%Y-%m-%d") for d in dates]
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except:
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parsed=list(range(len(dates)))
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fig,ax=plt.subplots(figsize=(8,3))
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ax.plot(parsed,scores,marker='o',linestyle='-',color='darkred',linewidth=2)
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for i,(x,y) in enumerate(zip(parsed,scores)):
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ax.text(x,y+2,f"{labels[i]}\n{int(y)}%",ha='center',fontsize=8)
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ax.set(title="Abuse Intensity Over Time",xlabel="Date",ylabel="Abuse Score (%)")
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ax.set_ylim(0,105);ax.grid(True);plt.tight_layout()
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buf=io.BytesIO();plt.savefig(buf,format='png');buf.seek(0)
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return Image.open(buf)
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# --- Load and initialize models ---
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model_name="SamanthaStorm/tether-multilabel-v2"
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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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healthy_detector=pipeline("text-classification",model="distilbert-base-uncased-finetuned-sst-2-english")
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sst_pipeline=pipeline("sentiment-analysis",model="distilbert-base-uncased-finetuned-sst-2-english")
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LABELS=[
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"blame shifting","contradictory statements","control","dismissiveness",
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"gaslighting","guilt tripping","insults","obscure language",
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"projection","recovery phase","threat"
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141 |
]
|
142 |
+
THRESHOLDS={l:THRESHOLDS.get(l,0.3) for l in LABELS}
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143 |
|
144 |
+
# --- Single Message Analysis ---
|
145 |
+
def analyze_single_message(text):
|
146 |
+
if healthy_detector(text)[0]['label']=="POSITIVE" and healthy_detector(text)[0]['score']>0.9:
|
147 |
+
return {"abuse_score":0,"labels":[],"sentiment":"supportive","stage":4,"darvo_score":0.0,"top_patterns":[]}
|
148 |
+
inputs=tokenizer(text,return_tensors='pt',padding=True,truncation=True)
|
149 |
+
with torch.no_grad(): outputs=model(**inputs).logits.squeeze(0)
|
150 |
+
probs=torch.sigmoid(outputs).numpy()
|
151 |
+
labels=[lab for lab,p in zip(LABELS,probs) if p>THRESHOLDS[lab]]
|
152 |
+
# weighted score
|
153 |
+
total_w=sum(PATTERN_WEIGHTS.get(l,1.0) for l in LABELS)
|
154 |
+
abuse_score=int(round(sum(probs[i]*PATTERN_WEIGHTS.get(l,1.0) for i,l in enumerate(LABELS))/total_w*100))
|
155 |
+
# sentiment
|
156 |
+
sst=sst_pipeline(text)[0]
|
157 |
+
sentiment='supportive' if sst['label']=='POSITIVE' else 'undermining'
|
158 |
+
sent_score=sst['score'] if sentiment=='undermining' else 0.0
|
159 |
+
# DARVO
|
160 |
+
motifs,matched=detect_motifs(text)
|
161 |
+
contradiction=detect_contradiction(text)
|
162 |
+
darvo=calculate_darvo_score(labels,0.0,sent_score,matched,contradiction)
|
163 |
+
# stage + weapon
|
164 |
+
stage=get_risk_stage(labels,sentiment)
|
165 |
+
if detect_weapon_language(text): abuse_score=min(abuse_score+25,100); stage=max(stage,2)
|
166 |
+
# top patterns
|
167 |
+
top_patterns=sorted(zip(LABELS,probs),key=lambda x:x[1],reverse=True)[:2]
|
168 |
+
return {"abuse_score":abuse_score,"labels":labels,"sentiment":sentiment,
|
169 |
+
"stage":stage,"darvo_score":darvo,"top_patterns":top_patterns}
|
170 |
+
|
171 |
+
# --- Composite Analysis ---
|
172 |
+
def analyze_composite(m1,d1,m2,d2,m3,d3,*answers):
|
173 |
+
none_sel=answers[-1] and not any(answers[:-1])
|
174 |
+
if none_sel: esc=None; risk='unknown'
|
175 |
+
else:
|
176 |
+
esc=sum(w for (_,w),a in zip(ESCALATION_QUESTIONS,answers[:-1]) if a)
|
177 |
+
risk='High' if esc>=16 else 'Moderate' if esc>=8 else 'Low'
|
178 |
+
msgs=[m1,m2,m3]; dates=[d1,d2,d3]
|
179 |
+
active=[(m,d) for m,d in zip(msgs,dates) if m.strip()]
|
180 |
+
if not active: return "Please enter at least one message."
|
181 |
+
results=[(analyze_single_message(m),d) for m,d in active]
|
182 |
+
abuse_scores=[r[0]['abuse_score'] for r in results]
|
183 |
+
top_lbls=[r[0]['top_patterns'][0][0] if r[0]['top_patterns'] else 'None' for r in results]
|
184 |
+
dates_used=[d or 'Undated' for (_,d) in results]
|
185 |
+
stage_list=[r[0]['stage'] for r,_ in results]
|
186 |
+
most_common_stage=max(set(stage_list),key=stage_list.count)
|
187 |
+
composite_abuse=int(round(sum(abuse_scores)/len(abuse_scores)))
|
188 |
+
out=f"Abuse Intensity: {composite_abuse}%\n"
|
189 |
+
if esc is None: out+="Escalation Potential: Unknown (Checklist not completed)\n"
|
190 |
+
else: out+=f"Escalation Potential: {risk} ({esc}/{sum(w for _,w in ESCALATION_QUESTIONS)})\n"
|
191 |
+
# DARVO summary
|
192 |
+
darvos=[r[0]['darvo_score'] for r,_ in results]
|
193 |
+
avg_d=sum(darvos)/len(darvos)
|
194 |
+
if avg_d>0.25: lvl='moderate' if avg_d<0.65 else 'high'; out+=f"\n🎭 DARVO Score: {round(avg_d,3)} ({lvl})\n"
|
195 |
+
out+=generate_risk_snippet(composite_abuse,f"{top_lbls[0]} – {int(top_patterns[0][1]*100)}%",esc or 0,most_common_stage)
|
196 |
+
img=generate_abuse_score_chart(dates_used,abuse_scores,top_lbls)
|
197 |
+
return out,img
|
198 |
+
|
199 |
+
# --- UI ---
|
200 |
+
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)]
|
201 |
+
quiz_boxes=[gr.Checkbox(label=q) for q,_ in ESCALATION_QUESTIONS]; none_box=gr.Checkbox(label="None of the above")
|
202 |
+
iface=gr.Interface(fn=analyze_composite,inputs=[item for pair in message_date_pairs for item in pair]+quiz_boxes+[none_box],outputs=[gr.Textbox(label="Results"),gr.Image(label="Risk Stage Timeline",type="pil")],
|
203 |
+
title="Tether Abuse Pattern Detector v2",allow_flagging="manual")
|
204 |
+
|
205 |
+
if __name__=="__main__": iface.launch()
|