Update app.py

app.py

@@ -4,151 +4,153 @@ import numpy as np
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 from transformers import RobertaForSequenceClassification, RobertaTokenizer
 
-# Load custom fine-tuned sentiment model
-sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment")
-sentiment_tokenizer = AutoTokenizer.from_pretrained("SamanthaStorm/tether-sentiment")
+Load custom fine-tuned sentiment model
 
-# Load abuse pattern model
-model_name = "SamanthaStorm/abuse-pattern-detector-v2"
+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):
-    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}
+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):
-    triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
-    return round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0
+triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
+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"
-    return "Very Low / Likely Safe"
+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."
-
-    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_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
-
-    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)
-    if high_risk_context:
-        danger_flag_count += 1
-
-    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)
-
-    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}%)"
-    )
-
-    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
+input_text = input_text.strip()
+if not input_text:
+return “Please enter a message for analysis.”
+
+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_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
+
+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."
+
+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."
+
+scored_patterns = [
+    (label, score) for label, score in zip(LABELS, scores)
+    if label != "non_abusive" and score > adjusted_thresholds[label]
+]
+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}%)"
+)
+
+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
 
 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"
+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”
 )
 
-if __name__ == "__main__":
-    iface.launch()
+if name == “main”:
+iface.launch()