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Running
on
Zero
import gradio as gr | |
import torch | |
import numpy as np | |
from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
from transformers import RobertaForSequenceClassification, RobertaTokenizer | |
from motif_tagging import detect_motifs | |
from abuse_type_mapping import determine_abuse_type | |
# custom fine-tuned sentiment model | |
sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment") | |
sentiment_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-sentiment") | |
# Load abuse pattern model | |
model_name = "SamanthaStorm/abuse-pattern-detector-v2" | |
model = RobertaForSequenceClassification.from_pretrained(model_name, trust_remote_code=True) | |
tokenizer = RobertaTokenizer.from_pretrained(model_name, trust_remote_code=True) | |
LABELS = [ | |
"gaslighting", "mockery", "dismissiveness", "control", "guilt_tripping", "apology_baiting", "blame_shifting", "projection", | |
"contradictory_statements", "manipulation", "deflection", "insults", "obscure_formal", "recovery_phase", "non_abusive", | |
"suicidal_threat", "physical_threat", "extreme_control" | |
] | |
THRESHOLDS = { | |
"gaslighting": 0.25, "mockery": 0.15, "dismissiveness": 0.30, "control": 0.43, "guilt_tripping": 0.19, | |
"apology_baiting": 0.45, "blame_shifting": 0.23, "projection": 0.50, "contradictory_statements": 0.25, | |
"manipulation": 0.25, "deflection": 0.30, "insults": 0.34, "obscure_formal": 0.25, "recovery_phase": 0.25, | |
"non_abusive": 2.0, "suicidal_threat": 0.45, "physical_threat": 0.02, "extreme_control": 0.36 | |
} | |
PATTERN_LABELS = LABELS[:15] | |
DANGER_LABELS = LABELS[15:18] | |
EXPLANATIONS = { | |
"gaslighting": "Gaslighting involves making someone question their own reality or perceptions...", | |
"blame_shifting": "Blame-shifting is when one person redirects the responsibility...", | |
"projection": "Projection involves accusing the victim of behaviors the abuser exhibits.", | |
"dismissiveness": "Dismissiveness is belittling or disregarding another person’s feelings.", | |
"mockery": "Mockery ridicules someone in a hurtful, humiliating way.", | |
"recovery_phase": "Recovery phase dismisses someone's emotional healing process.", | |
"insults": "Insults are derogatory remarks aimed at degrading someone.", | |
"apology_baiting": "Apology-baiting manipulates victims into apologizing for abuser's behavior.", | |
"deflection": "Deflection avoids accountability by redirecting blame.", | |
"control": "Control restricts autonomy through manipulation or coercion.", | |
"extreme_control": "Extreme control dominates decisions and behaviors entirely.", | |
"physical_threat": "Physical threats signal risk of bodily harm.", | |
"suicidal_threat": "Suicidal threats manipulate others using self-harm threats.", | |
"guilt_tripping": "Guilt-tripping uses guilt to manipulate someone’s actions.", | |
"manipulation": "Manipulation deceives to influence or control outcomes.", | |
"non_abusive": "Non-abusive language is respectful and free of coercion.", | |
"obscure_formal": "Obscure/formal language manipulates through confusion or superiority." | |
} | |
def custom_sentiment(text): | |
inputs = sentiment_tokenizer(text, return_tensors="pt", truncation=True, padding=True) | |
with torch.no_grad(): | |
outputs = sentiment_model(**inputs) | |
probs = torch.nn.functional.softmax(outputs.logits, dim=1) | |
label_idx = torch.argmax(probs).item() | |
label_map = {0: "supportive", 1: "undermining"} | |
label = label_map[label_idx] | |
score = probs[0][label_idx].item() | |
return {"label": label, "score": score} | |
def calculate_abuse_level(scores, thresholds, motif_hits=None): | |
triggered_scores = [ | |
score for label, score in zip(LABELS, scores) if score > thresholds[label] | |
] | |
base_score = round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0 | |
motif_hits = motif_hits or [] | |
if any(label in motif_hits for label in {"physical_threat", "suicidal_threat", "extreme_control"}): | |
base_score = max(base_score, 75.0) | |
return base_score | |
def interpret_abuse_level(score): | |
if score > 80: | |
return "Extreme / High Risk" | |
elif score > 60: | |
return "Severe / Harmful Pattern Present" | |
elif score > 40: | |
return "Likely Abuse" | |
elif score > 20: | |
return "Mild Concern" | |
return "Very Low / Likely Safe" | |
def analyze_messages(input_text, risk_flags): | |
input_text = input_text.strip() | |
if not input_text: | |
return "Please enter a message for analysis." | |
motif_flags, matched_phrases = detect_motifs(input_text) | |
risk_flags = list(set(risk_flags + motif_flags)) if risk_flags else motif_flags | |
sentiment = custom_sentiment(input_text) | |
sentiment_label = sentiment['label'] | |
sentiment_score = sentiment['score'] | |
adjusted_thresholds = {k: v * 0.8 for k, v in THRESHOLDS.items()} if sentiment_label == "undermining" else THRESHOLDS.copy() | |
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True) | |
with torch.no_grad(): | |
outputs = model(**inputs) | |
scores = torch.sigmoid(outputs.logits.squeeze(0)).numpy() | |
pattern_labels_used = list(set( | |
[label for label, score in zip(PATTERN_LABELS, scores[:15]) if score > adjusted_thresholds[label]] + | |
[label for label, _ in matched_phrases] | |
)) | |
abuse_level = calculate_abuse_level(scores, adjusted_thresholds, motif_hits=[label for label, _ in matched_phrases]) | |
abuse_description = interpret_abuse_level(abuse_level) | |
abuse_type, abuser_profile, advice = determine_abuse_type(pattern_labels_used) | |
danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[15:18])) | |
contextual_flags = risk_flags if risk_flags else [] | |
if len(contextual_flags) >= 2: | |
danger_flag_count += 1 | |
critical_flags = ["They've threatened harm", "They monitor/follow me", "I feel unsafe when alone with them"] | |
high_risk_context = any(flag in contextual_flags for flag in critical_flags) | |
non_abusive_score = scores[LABELS.index('non_abusive')] | |
non_abusive_confident = non_abusive_score > adjusted_thresholds['non_abusive'] | |
if non_abusive_confident and danger_flag_count == 0 and not matched_phrases: | |
return "This message is classified as non-abusive." | |
scored_patterns = [ | |
(label, score) for label, score in zip(PATTERN_LABELS, scores[:15]) if label != "non_abusive" | |
] | |
override_labels = {"physical_threat", "suicidal_threat", "extreme_control"} | |
override_matches = [label for label, _ in matched_phrases if label in override_labels] | |
if override_matches: | |
top_patterns = [(label, 1.0) for label in override_matches] | |
else: | |
top_patterns = sorted(scored_patterns, key=lambda x: x[1], reverse=True)[:2] | |
top_pattern_explanations = "\n".join([ | |
f"• {label.replace('_', ' ').title()}: {EXPLANATIONS.get(label, 'No explanation available.')}" | |
for label, _ in top_patterns | |
]) | |
resources = "Immediate assistance recommended. Please seek professional help or contact emergency services." if danger_flag_count >= 2 else "For more information on abuse patterns, consider reaching out to support groups or professional counselors." | |
result = f"Abuse Risk Score: {abuse_level}% – {abuse_description}\n\n" | |
if abuse_level >= 15: | |
result += f"Most Likely Patterns:\n{top_pattern_explanations}\n\n" | |
result += f"⚠️ Critical Danger Flags Detected: {danger_flag_count} of 3\n" | |
result += f"Resources: {resources}\n" | |
result += f"🧠 Sentiment: {sentiment_label.title()} (Confidence: {sentiment_score*100:.2f}%)\n" | |
if contextual_flags: | |
result += "\n\n⚠️ You indicated the following:\n" + "\n".join([f"• {flag.replace('_', ' ').title()}" for flag in contextual_flags]) | |
if high_risk_context: | |
result += "\n\n🚨 These responses suggest a high-risk situation. Consider seeking immediate help or safety planning resources." | |
if matched_phrases: | |
result += "\n\n🚨 Detected High-Risk Phrases:\n" | |
for label, phrase in matched_phrases: | |
phrase_clean = phrase.replace('"', "'").strip() | |
result += f"• {label.replace('_', ' ').title()}: “{phrase_clean}”\n" | |
if abuse_type: | |
result += f"\n\n🧠 Likely Abuse Type: {abuse_type}" | |
result += f"\n🧠 Abuser Profile: {abuser_profile}" | |
result += f"\n📘 Safety Tip: {advice}" | |
return result | |
iface = gr.Interface( | |
fn=analyze_messages, | |
inputs=[ | |
gr.Textbox(lines=10, placeholder="Enter message here..."), | |
gr.CheckboxGroup(label="Do any of these apply to your situation?", choices=[ | |
"They've threatened harm", "They isolate me", "I’ve changed my behavior out of fear", | |
"They monitor/follow me", "I feel unsafe when alone with them" | |
]) | |
], | |
outputs=[gr.Textbox(label="Analysis Result")], | |
title="Abuse Pattern Detector", | |
live=True | |
) | |
if __name__ == "__main__": | |
iface.queue().launch() | |