Update app.py

app.py

@@ -5,50 +5,48 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 from transformers import RobertaForSequenceClassification, RobertaTokenizer
 
 # 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"
+    "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
+    "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."
+    "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):
@@ -68,74 +66,78 @@ def calculate_abuse_level(scores, thresholds):
     return round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0
 
 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"
+    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:
+    if not input_text:
         return "Please enter a message for analysis."
 
-sentiment = custom_sentiment(input_text)
-sentiment_label = sentiment['label']
-sentiment_score = sentiment['score']
+    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()
+    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()
+    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_count = sum(score > adjusted_thresholds[label] for label, score in zip(PATTERN_LABELS, scores[:15]))
-danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[15:18]))
+    pattern_count = sum(score > adjusted_thresholds[label] for label, score in zip(PATTERN_LABELS, scores[:15]))
+    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
+    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)
+    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')]
-if non_abusive_score > adjusted_thresholds['non_abusive']:
-    return "This message is classified as non-abusive."
+    non_abusive_score = scores[LABELS.index('non_abusive')]
+    if non_abusive_score > adjusted_thresholds['non_abusive']:
+        return "This message is classified as non-abusive."
 
-abuse_level = calculate_abuse_level(scores, adjusted_thresholds)
-abuse_description = interpret_abuse_level(abuse_level)
+    abuse_level = calculate_abuse_level(scores, adjusted_thresholds)
+    abuse_description = interpret_abuse_level(abuse_level)
 
-if danger_flag_count >= 2:
-    resources = "Immediate assistance recommended. Please seek professional help or contact emergency services."
-else:
-    resources = "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
+    if danger_flag_count >= 2:
+        resources = "Immediate assistance recommended. Please seek professional help or contact emergency services."
+    else:
+        resources = "For more information on abuse patterns, consider reaching out to support groups or professional counselors."
 
-scored_patterns = [
-    (label, score) for label, score in zip(PATTERN_LABELS, scores[:15]) if label != "non_abusive"
-]
-top_patterns = sorted(scored_patterns, key=lambda x: x[1], reverse=True)[:2]
-
-top_pattern_explanations = "\n".join([
-    f"\u2022 {label.replace('_', ' ').title()}: {EXPLANATIONS.get(label, 'No explanation available.')}"
-    for label, _ in top_patterns
-])
-
-result = (
-    f"Abuse Risk Score: {abuse_level}% – {abuse_description}\n\n"
-    f"Most Likely Patterns:\n{top_pattern_explanations}\n\n"
-    f"⚠️ Critical Danger Flags Detected: {danger_flag_count} of 3\n"
-    "Resources: " + resources + "\n\n"
-    f"Sentiment: {sentiment_label.title()} (Confidence: {sentiment_score*100:.2f}%)"
-)
+    scored_patterns = [
+        (label, score) for label, score in zip(PATTERN_LABELS, scores[:15]) if label != "non_abusive"
+    ]
+    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
+    ])
+
+    result = (
+        f"Abuse Risk Score: {abuse_level}% – {abuse_description}\n\n"
+        f"Most Likely Patterns:\n{top_pattern_explanations}\n\n"
+        f"⚠️ Critical Danger Flags Detected: {danger_flag_count} of 3\n"
+        "Resources: " + resources + "\n\n"
+        f"Sentiment: {sentiment_label.title()} (Confidence: {sentiment_score*100:.2f}%)"
+    )
 
-if contextual_flags:
-    result += "\n\n⚠️ You indicated the following:\n" + "\n".join([f"• {flag}" 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 contextual_flags:
+        result += "\n\n⚠️ You indicated the following:\n" + "\n".join([f"• {flag}" 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."
 
-return result
+    return result
 
 iface = gr.Interface(
     fn=analyze_messages,
@@ -148,8 +150,8 @@ iface = gr.Interface(
     ],
     outputs=[gr.Textbox(label="Analysis Result")],
     title="Abuse Pattern Detector",
-    live=True  # ← 🔥 this is the missing key for .queue().launch() to work
+    live=True
 )
 
-if name == "main":
+if __name__ == "__main__":
     iface.queue().launch()