import gradio as gr import spaces 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=6, truncation=True ) # --- Timeline Visualization Function --- def generate_abuse_score_chart(dates, scores, labels): import matplotlib.pyplot as plt import io from PIL import Image from datetime import datetime import re # Determine if all entries are valid dates if all(re.match(r"\d{4}-\d{2}-\d{2}", d) for d in dates): parsed_x = [datetime.strptime(d, "%Y-%m-%d") for d in dates] x_labels = [d.strftime("%Y-%m-%d") for d in parsed_x] else: parsed_x = list(range(1, len(dates) + 1)) x_labels = [f"Message {i+1}" for i in range(len(dates))] fig, ax = plt.subplots(figsize=(8, 3)) ax.plot(parsed_x, scores, marker='o', linestyle='-', color='darkred', linewidth=2) for x, y in zip(parsed_x, scores): ax.text(x, y + 2, f"{int(y)}%", ha='center', fontsize=8, color='black') ax.set_xticks(parsed_x) ax.set_xticklabels(x_labels) ax.set_xlabel("") # No axis label ax.set_ylabel("Abuse Score (%)") ax.set_ylim(0, 105) ax.grid(True) plt.tight_layout() buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) return Image.open(buf) # --- Abuse Model --- from transformers import AutoModelForSequenceClassification, AutoTokenizer model_name = "SamanthaStorm/tether-multilabel-v4" model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) LABELS = [ "recovery", "control", "gaslighting", "guilt tripping", "dismissiveness", "blame shifting", "nonabusive","projection", "insults", "contradictory statements", "obscure language" ] THRESHOLDS = { "recovery": 0.27, "control": 0.47, "gaslighting": 0.48, "guilt tripping": .56, "dismissiveness": 0.25, "blame shifting": 0.55, "projection": 0.59, "insults": 0.33, "contradictory statements": 0.27, "obscure language": 0.65, "nonabusive": 1.0 } PATTERN_WEIGHTS = { "recovery": 0.7, "control": 1.4, "gaslighting": 1.50, "guilt tripping": 0.9, "dismissiveness": 0.9, "blame shifting": 0.8, "projection": 0.5, "insults": 1.2, "contradictory statements": 1.0, "obscure language": 0.9, "nonabusive": 0.0 } ESCALATION_RISKS = { "blame shifting": "low", "contradictory statements": "moderate", "control": "high", "dismissiveness": "moderate", "gaslighting": "moderate", "guilt tripping": "moderate", "insults": "moderate", "obscure language": "low", "projection": "low", "recovery phase": "low" } RISK_STAGE_LABELS = { 1: "🌀 Risk Stage: Tension-Building\nThis message reflects rising emotional pressure or subtle control attempts.", 2: "🔥 Risk Stage: Escalation\nThis message includes direct or aggressive patterns, suggesting active harm.", 3: "🌧️ Risk Stage: Reconciliation\nThis message reflects a reset attempt—apologies or emotional repair without accountability.", 4: "🌸 Risk Stage: Calm / Honeymoon\nThis message appears supportive but may follow prior harm, minimizing it." } ESCALATION_QUESTIONS = [ ("Partner has access to firearms or weapons", 4), ("Partner threatened to kill you", 3), ("Partner threatened you with a weapon", 3), ("Partner has ever choked you, even if you considered it consensual at the time", 4), ("Partner injured or threatened your pet(s)", 3), ("Partner has broken your things, punched or kicked walls, or thrown things ", 2), ("Partner forced or coerced you into unwanted sexual acts", 3), ("Partner threatened to take away your children", 2), ("Violence has increased in frequency or severity", 3), ("Partner monitors your calls/GPS/social media", 2) ] def get_emotional_tone_tag(emotions, sentiment, patterns, abuse_score): sadness = emotions.get("sadness", 0) joy = emotions.get("joy", 0) neutral = emotions.get("neutral", 0) disgust = emotions.get("disgust", 0) anger = emotions.get("anger", 0) fear = emotions.get("fear", 0) disgust = emotions.get("disgust", 0) # 1. Performative Regret if ( sadness > 0.4 and any(p in patterns for p in ["blame shifting", "guilt tripping", "recovery phase"]) and (sentiment == "undermining" or abuse_score > 40) ): return "performative regret" # 2. Coercive Warmth if ( (joy > 0.3 or sadness > 0.4) and any(p in patterns for p in ["control", "gaslighting"]) and sentiment == "undermining" ): return "coercive warmth" # 3. Cold Invalidation if ( (neutral + disgust) > 0.5 and any(p in patterns for p in ["dismissiveness", "projection", "obscure language"]) and sentiment == "undermining" ): return "cold invalidation" # 4. Genuine Vulnerability if ( (sadness + fear) > 0.5 and sentiment == "supportive" and all(p in ["recovery phase"] for p in patterns) ): return "genuine vulnerability" # 5. Emotional Threat if ( (anger + disgust) > 0.5 and any(p in patterns for p in ["control", "insults", "dismissiveness"]) and sentiment == "undermining" ): return "emotional threat" # 6. Weaponized Sadness if ( sadness > 0.6 and any(p in patterns for p in ["guilt tripping", "projection"]) and sentiment == "undermining" ): return "weaponized sadness" # 7. Toxic Resignation if ( neutral > 0.5 and any(p in patterns for p in ["dismissiveness", "obscure language"]) and sentiment == "undermining" ): return "toxic resignation" # 8. Aggressive Dismissal if ( anger > 0.5 and any(p in patterns for p in ["aggression", "insults", "control"]) and sentiment == "undermining" ): return "aggressive dismissal" # 9. Deflective Hostility if ( (0.2 < anger < 0.7 or 0.2 < disgust < 0.7) and any(p in patterns for p in ["deflection", "projection"]) and sentiment == "undermining" ): return "deflective hostility" # 10. Mocking Detachment if ( (neutral + joy) > 0.5 and any(p in patterns for p in ["mockery", "insults", "projection"]) and sentiment == "undermining" ): return "mocking detachment" # 11. Contradictory Gaslight if ( (joy + anger + sadness) > 0.5 and any(p in patterns for p in ["gaslighting", "contradictory statements"]) and sentiment == "undermining" ): return "contradictory gaslight" # 12. Calculated Neutrality if ( neutral > 0.6 and any(p in patterns for p in ["obscure language", "deflection", "dismissiveness"]) and sentiment == "undermining" ): return "calculated neutrality" # 13. Forced Accountability Flip if ( (anger + disgust) > 0.5 and any(p in patterns for p in ["blame shifting", "manipulation", "projection"]) and sentiment == "undermining" ): return "forced accountability flip" # 14. Conditional Affection if ( joy > 0.4 and any(p in patterns for p in ["apology baiting", "control", "recovery phase"]) and sentiment == "undermining" ): return "conditional affection" if ( (anger + disgust) > 0.5 and any(p in patterns for p in ["blame shifting", "projection", "deflection"]) and sentiment == "undermining" ): return "forced accountability flip" # Emotional Instability Fallback if ( (anger + sadness + disgust) > 0.6 and sentiment == "undermining" ): return "emotional instability" return None # 🔄 New DARVO score model (regression-based) from torch.nn.functional import sigmoid import torch # Load your trained DARVO regressor from Hugging Face Hub darvo_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-darvo-regressor-v1") darvo_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-darvo-regressor-v1", use_fast=False) darvo_model.eval() def predict_darvo_score(text): inputs = darvo_tokenizer(text, return_tensors="pt", truncation=True, padding=True) with torch.no_grad(): logits = darvo_model(**inputs).logits score = sigmoid(logits).item() return round(score, 4) # Rounded for display/output def detect_weapon_language(text): weapon_keywords = [ "knife", "knives", "stab", "cut you", "cutting", "gun", "shoot", "rifle", "firearm", "pistol", "bomb", "blow up", "grenade", "explode", "weapon", "armed", "loaded", "kill you", "take you out" ] text_lower = text.lower() return any(word in text_lower for word in weapon_keywords) def get_risk_stage(patterns, sentiment): if "insults" in patterns: return 2 elif "recovery phase" in patterns: return 3 elif "control" in patterns or "guilt tripping" in patterns: return 1 elif sentiment == "supportive" and any(p in patterns for p in ["projection", "dismissiveness"]): return 4 return 1 def generate_risk_snippet(abuse_score, top_label, escalation_score, stage): import re # Extract aggression score if aggression is detected if isinstance(top_label, str) and "aggression" in top_label.lower(): try: match = re.search(r"\(?(\d+)\%?\)?", top_label) aggression_score = int(match.group(1)) / 100 if match else 0 except: aggression_score = 0 else: aggression_score = 0 # Revised risk logic if abuse_score >= 85 or escalation_score >= 16: risk_level = "high" elif abuse_score >= 60 or escalation_score >= 8 or aggression_score >= 0.25: risk_level = "moderate" elif stage == 2 and abuse_score >= 40: risk_level = "moderate" else: risk_level = "low" if isinstance(top_label, str) and " – " in top_label: pattern_label, pattern_score = top_label.split(" – ") else: pattern_label = str(top_label) if top_label is not None else "Unknown" pattern_score = "" WHY_FLAGGED = { "control": "This message may reflect efforts to restrict someone’s autonomy, even if it's framed as concern or care.", "gaslighting": "This message could be manipulating someone into questioning their perception or feelings.", "dismissiveness": "This message may include belittling, invalidating, or ignoring the other person’s experience.", "insults": "Direct insults often appear in escalating abusive dynamics and can erode emotional safety.", "blame shifting": "This message may redirect responsibility to avoid accountability, especially during conflict.", "guilt tripping": "This message may induce guilt in order to control or manipulate behavior.", "recovery phase": "This message may be part of a tension-reset cycle, appearing kind but avoiding change.", "projection": "This message may involve attributing the abuser’s own behaviors to the victim.", "contradictory statements": "This message may contain internal contradictions used to confuse, destabilize, or deflect responsibility.", "obscure language": "This message may use overly formal, vague, or complex language to obscure meaning or avoid accountability.", "default": "This message contains language patterns that may affect safety, clarity, or emotional autonomy." } explanation = WHY_FLAGGED.get(pattern_label.lower(), WHY_FLAGGED["default"]) base = f"\n\n🛑 Risk Level: {risk_level.capitalize()}\n" base += f"This message shows strong indicators of **{pattern_label}**. " if risk_level == "high": base += "The language may reflect patterns of emotional control, even when expressed in soft or caring terms.\n" elif risk_level == "moderate": base += "There are signs of emotional pressure or verbal aggression that may escalate if repeated.\n" else: base += "The message does not strongly indicate abuse, but it's important to monitor for patterns.\n" base += f"\n💡 *Why this might be flagged:*\n{explanation}\n" base += f"\nDetected Pattern: **{pattern_label} ({pattern_score})**\n" base += "🧠 You can review the pattern in context. This tool highlights possible dynamics—not judgments." return base # --- Step X: Detect Immediate Danger Threats --- THREAT_MOTIFS = [ "i'll kill you", "i’m going to hurt you", "you’re dead", "you won't survive this", "i’ll break your face", "i'll bash your head in", "i’ll snap your neck", "i’ll come over there and make you shut up", "i'll knock your teeth out", "you’re going to bleed", "you want me to hit you?", "i won’t hold back next time", "i swear to god i’ll beat you", "next time, i won’t miss", "i’ll make you scream", "i know where you live", "i'm outside", "i’ll be waiting", "i saw you with him", "you can’t hide from me", "i’m coming to get you", "i'll find you", "i know your schedule", "i watched you leave", "i followed you home", "you'll regret this", "you’ll be sorry", "you’re going to wish you hadn’t", "you brought this on yourself", "don’t push me", "you have no idea what i’m capable of", "you better watch yourself", "i don’t care what happens to you anymore", "i’ll make you suffer", "you’ll pay for this", "i’ll never let you go", "you’re nothing without me", "if you leave me, i’ll kill myself", "i'll ruin you", "i'll tell everyone what you did", "i’ll make sure everyone knows", "i’m going to destroy your name", "you’ll lose everyone", "i’ll expose you", "your friends will hate you", "i’ll post everything", "you’ll be cancelled", "you’ll lose everything", "i’ll take the house", "i’ll drain your account", "you’ll never see a dime", "you’ll be broke when i’m done", "i’ll make sure you lose your job", "i’ll take your kids", "i’ll make sure you have nothing", "you can’t afford to leave me", "don't make me do this", "you know what happens when i’m mad", "you’re forcing my hand", "if you just behaved, this wouldn’t happen", "this is your fault", "you’re making me hurt you", "i warned you", "you should have listened" ] 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 = { "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): print("⚡ ENTERED analyze_single_message") stage = 1 motif_hits, matched_phrases = detect_motifs(text) # Get emotion profile emotion_profile = get_emotion_profile(text) sentiment_score = emotion_profile.get("anger", 0) + emotion_profile.get("disgust", 0) # Get model scores inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) with torch.no_grad(): outputs = model(**inputs) scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy() # Sentiment override if emotion_profile.get("neutral", 0) > 0.85 and any( scores[LABELS.index(l)] > thresholds[l] for l in ["control", "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() } darvo_score = predict_darvo_score(text) threshold_labels = [ label for label, score in zip(LABELS, scores) if score > adjusted_thresholds[label] ] # Early exit if nothing passed if not threshold_labels: return 0.0, [], [], {"label": sentiment}, 1, 0.0, "supportive" top_patterns = sorted( [(label, score) for label, score in zip(LABELS, scores)], key=lambda x: x[1], reverse=True )[:2] matched_scores = [ (label, score, PATTERN_WEIGHTS.get(label, 1.0)) for label, score in zip(LABELS, scores) if score > adjusted_thresholds[label] ] # Determine insult subtype insults_score = next((score for label, score, _ in matched_scores if label == "insults"), 0) insult_label_display = None if insults_score > 0.9 and (emotion_profile.get("anger", 0) > 0.1 or emotion_profile.get("disgust", 0) > 0.1): insult_label_display = "Direct Insult" elif 0.5 < insults_score <= 0.9 and emotion_profile.get("neutral", 0) > 0.85: insult_label_display = "Subtle Undermining" # Abuse score abuse_score_raw = compute_abuse_score(matched_scores, sentiment) # Weapon adjustment if weapon_flag: abuse_score_raw = min(abuse_score_raw + 25, 100) if stage < 2: stage = 2 abuse_score = min(abuse_score_raw, 100 if "control" in threshold_labels else 95) # Tone tag tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score) # Remove recovery tag if tone is fake if "recovery" in threshold_labels and tone_tag == "forced accountability flip": threshold_labels.remove("recovery") top_patterns = [p for p in top_patterns if p[0] != "recovery"] print("⚠️ Removing 'recovery' due to undermining sentiment (not genuine repair)") # Override profanity/anger for short texts profane_words = {"fuck", "fucking", "bitch", "shit", "cunt", "ho", "asshole", "dick", "whore", "slut"} tokens = set(text.lower().split()) has_profane = any(word in tokens for word in profane_words) short_text = len(tokens) <= 10 anger_score = emotion_profile.get("anger", 0) if has_profane and anger_score > 0.75 and short_text: print("⚠️ Profanity + Anger Override Triggered") insult_score = next((s for l, s in top_patterns if l == "insults"), 0) if ("insults", insult_score) not in top_patterns: top_patterns = [("insults", insult_score)] + top_patterns if "insults" not in threshold_labels: threshold_labels.append("insults") # Replace 'insults' with descriptive label in output if insult_label_display and "insults" in threshold_labels: threshold_labels = [ insult_label_display if label == "insults" else label for label in threshold_labels ] # Debug print(f"Emotional Tone Tag: {tone_tag}") print("Emotion Profile:") for emotion, score in emotion_profile.items(): print(f" {emotion.capitalize():10}: {score}") print("\n--- Debug Info ---") print(f"Text: {text}") print(f"Sentiment (via emotion): {sentiment} (score: {round(sentiment_score, 3)})") print("Abuse Pattern Scores:") for label, score in zip(LABELS, scores): passed = "✅" if score > adjusted_thresholds[label] else "❌" print(f" {label:25} → {score:.3f} {passed}") print(f"Matched for score: {[(l, round(s, 3)) for l, s, _ in matched_scores]}") print(f"Abuse Score Raw: {round(abuse_score_raw, 1)}") print("------------------\n") return abuse_score, threshold_labels, top_patterns, {"label": sentiment}, stage, darvo_score, tone_tag import spaces @spaces.GPU def analyze_composite(msg1, msg2, msg3, *answers_and_none): from collections import Counter none_selected_checked = answers_and_none[-1] responses_checked = any(answers_and_none[:-1]) none_selected = not responses_checked and none_selected_checked if none_selected: escalation_score = 0 escalation_note = "Checklist completed: no danger items reported." escalation_completed = True elif responses_checked: escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a) escalation_note = "Checklist completed." escalation_completed = True else: escalation_score = None escalation_note = "Checklist not completed." escalation_completed = False messages = [msg1, msg2, msg3] active = [(m, f"Message {i+1}") for i, m in enumerate(messages) if m.strip()] if not active: return "Please enter at least one message.", None # Flag any threat phrases present in the messages import re def normalize(text): import unicodedata text = text.lower().strip() text = unicodedata.normalize("NFKD", text) # handles curly quotes text = text.replace("’", "'") # smart to straight return re.sub(r"[^a-z0-9 ]", "", text) def detect_threat_motifs(message, motif_list): norm_msg = normalize(message) return [ motif for motif in motif_list if normalize(motif) in norm_msg ] # Collect matches per message immediate_threats = [detect_threat_motifs(m, THREAT_MOTIFS) for m, _ in active] flat_threats = [t for sublist in immediate_threats for t in sublist] threat_risk = "Yes" if flat_threats else "No" results = [(analyze_single_message(m, THRESHOLDS.copy()), d) for m, d in active] abuse_scores = [r[0][0] for r in results] stages = [r[0][4] for r in results] darvo_scores = [r[0][5] for r in results] tone_tags = [r[0][6] for r in results] dates_used = [r[1] for r in results] predicted_labels = [label for r in results for label, _ in r[0][2]] high = {'control'} moderate = {'gaslighting', 'dismissiveness', 'obscure language', 'insults', 'contradictory statements', 'guilt tripping'} low = {'blame shifting', 'projection', 'recovery phase'} counts = {'high': 0, 'moderate': 0, 'low': 0} for label in predicted_labels: if label in high: counts['high'] += 1 elif label in moderate: counts['moderate'] += 1 elif label in low: counts['low'] += 1 # Pattern escalation logic pattern_escalation_risk = "Low" if counts['high'] >= 2 and counts['moderate'] >= 2: pattern_escalation_risk = "Critical" elif (counts['high'] >= 2 and counts['moderate'] >= 1) or (counts['moderate'] >= 3) or (counts['high'] >= 1 and counts['moderate'] >= 2): pattern_escalation_risk = "High" elif (counts['moderate'] == 2) or (counts['high'] == 1 and counts['moderate'] == 1) or (counts['moderate'] == 1 and counts['low'] >= 2) or (counts['high'] == 1 and sum(counts.values()) == 1): pattern_escalation_risk = "Moderate" checklist_escalation_risk = "Unknown" if escalation_score is None else ( "Critical" if escalation_score >= 20 else "Moderate" if escalation_score >= 10 else "Low" ) escalation_bump = 0 for result, _ in results: abuse_score, _, _, sentiment, stage, darvo_score, tone_tag = result if darvo_score > 0.65: escalation_bump += 3 if tone_tag in ["forced accountability flip", "emotional threat"]: escalation_bump += 2 if abuse_score > 80: escalation_bump += 2 if stage == 2: escalation_bump += 3 def rank(label): return {"Low": 0, "Moderate": 1, "High": 2, "Critical": 3, "Unknown": 0}.get(label, 0) combined_score = rank(pattern_escalation_risk) + rank(checklist_escalation_risk) + escalation_bump escalation_risk = ( "Critical" if combined_score >= 6 else "High" if combined_score >= 4 else "Moderate" if combined_score >= 2 else "Low" ) none_selected_checked = answers_and_none[-1] responses_checked = any(answers_and_none[:-1]) none_selected = not responses_checked and none_selected_checked # Determine escalation_score if none_selected: escalation_score = 0 escalation_completed = True elif responses_checked: escalation_score = sum( w for (_, w), a in zip(ESCALATION_QUESTIONS, answers_and_none[:-1]) if a ) escalation_completed = True else: escalation_score = None escalation_completed = False # Build escalation_text and hybrid_score if escalation_score is None: escalation_text = ( "🚫 **Escalation Potential: Unknown** (Checklist not completed)\n" "⚠️ This section was not completed. Escalation potential is estimated using message data only.\n" ) hybrid_score = 0 elif escalation_score == 0: escalation_text = ( "✅ **Escalation Checklist Completed:** No danger items reported.\n" "🧭 **Escalation potential estimated from detected message patterns only.**\n" f"• Pattern Risk: {pattern_escalation_risk}\n" f"• Checklist Risk: None reported\n" f"• Escalation Bump: +{escalation_bump} (from DARVO, tone, intensity, etc.)" ) hybrid_score = escalation_bump else: hybrid_score = escalation_score + escalation_bump escalation_text = ( f"📈 **Escalation Potential: {escalation_risk} ({hybrid_score}/29)**\n" "📋 This score combines your safety checklist answers *and* detected high-risk behavior.\n" f"• Pattern Risk: {pattern_escalation_risk}\n" f"• Checklist Risk: {checklist_escalation_risk}\n" f"• Escalation Bump: +{escalation_bump} (from DARVO, tone, intensity, etc.)" ) # Composite Abuse Score 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] # Derive top label list for each message # safe derive top_labels top_labels = [] for result, _ in results: threshold_labels = result[1] top_patterns = result[2] if threshold_labels: top_labels.append(threshold_labels[0]) elif top_patterns: top_labels.append(top_patterns[0][0]) else: top_labels.append("none") # or whatever default you prefer avg_darvo = round(sum(darvo_scores) / len(darvo_scores), 3) darvo_blurb = "" if avg_darvo > 0.25: level = "moderate" if avg_darvo < 0.65 else "high" darvo_blurb = f"\n\n🎭 **DARVO Score: {avg_darvo}** → This indicates a **{level} likelihood** of narrative reversal (DARVO), where the speaker may be denying, attacking, or reversing blame." out = f"Abuse Intensity: {composite_abuse}%\n" out += "📊 This reflects the strength and severity of detected abuse patterns in the message(s).\n\n" out += generate_risk_snippet(composite_abuse, top_labels[0], hybrid_score, most_common_stage) out += f"\n\n{stage_text}" out += darvo_blurb out += "\n\n🎭 **Emotional Tones Detected:**\n" for i, tone in enumerate(tone_tags): out += f"• Message {i+1}: *{tone or 'none'}*\n" # --- Add Immediate Danger Threats section if flat_threats: out += "\n\n🚨 **Immediate Danger Threats Detected:**\n" for t in set(flat_threats): out += f"• \"{t}\"\n" out += "\n⚠️ These phrases may indicate an imminent risk to physical safety." else: out += "\n\n🧩 **Immediate Danger Threats:** None explicitly detected.\n" out += "This does *not* rule out risk, but no direct threat phrases were matched." pattern_labels = [ pats[0][0] if (pats := r[0][2]) else "none" for r in results ] timeline_image = generate_abuse_score_chart(dates_used, abuse_scores, top_labels) out += "\n\n" + escalation_text return out, timeline_image textbox_inputs = [gr.Textbox(label=f"Message {i+1}") for i in range(3)] quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS] none_box = gr.Checkbox(label="None of the above") # ─── FINAL “FORCE LAUNCH” (no guards) ──────────────────────── demo = gr.Interface( fn=analyze_composite, inputs=textbox_inputs + quiz_boxes + [none_box], outputs=[ gr.Textbox(label="Results"), gr.Image(label="Abuse Score Timeline", type="pil") ], title="Abuse Pattern Detector + Escalation Quiz", description=( "Enter up to three messages that concern you. " "For the most accurate results, include messages from a recent emotionally intense period." ), flagging_mode="manual" ) # This single call will start the server and block, # keeping the container alive on Spaces. demo.launch()