Update app.py

app.py

@@ -1,28 +1,27 @@
 import gradio as gr
 import torch
-from transformers import RobertaForSequenceClassification, RobertaTokenizer, pipeline
+from transformers import RobertaForSequenceClassification, RobertaTokenizer
 import numpy as np
+from transformers import pipeline
 
-# Load sentiment model
+# Load sentiment analysis model
 sentiment_analyzer = pipeline("sentiment-analysis")
 
-# Load abuse pattern model
+# Load model and tokenizer
 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
+# Define labels (18 total)
 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"
+    "obscure_formal", "recovery_phase", "non_abusive", "suicidal_threat", "physical_threat",
+    "extreme_control"
 ]
-PATTERN_LABELS = LABELS[:15]
-DANGER_LABELS = LABELS[15:]
 
-# Thresholds
+# Custom thresholds for each label
 THRESHOLDS = {
     "gaslighting": 0.25,
     "mockery": 0.15,
@@ -44,7 +43,9 @@ THRESHOLDS = {
     "extreme_control": 0.36
 }
 
-# Explanations
+PATTERN_LABELS = LABELS[:15]
+DANGER_LABELS = LABELS[15:18]
+
 EXPLANATIONS = {
     "gaslighting": "Gaslighting involves making someone question their own reality or perceptions, often causing them to feel confused or insecure.",
     "blame_shifting": "Blame-shifting is when one person redirects the responsibility for an issue onto someone else, avoiding accountability.",
@@ -60,11 +61,20 @@ EXPLANATIONS = {
     "physical_threat": "Physical threats involve any indication or direct mention of harm to someone’s physical well-being, often used to intimidate or control.",
     "suicidal_threat": "Suicidal threats are statements made to manipulate or control someone by making them feel responsible for the abuser’s well-being.",
     "guilt_tripping": "Guilt-tripping involves making someone feel guilty or responsible for things they didn’t do, often to manipulate their behavior.",
+    "emotional_manipulation": "Emotional manipulation is using guilt, fear, or emotional dependency to control another person’s thoughts, feelings, or actions.",
     "manipulation": "Manipulation refers to using deceptive tactics to control or influence someone’s emotions, decisions, or behavior to serve the manipulator’s own interests.",
-    "non_abusive": "Non-abusive language is communication that is respectful, empathetic, and free of harmful behaviors or manipulation."
+    "non_abusive": "Non-abusive language is communication that is respectful, empathetic, and free of harmful behaviors or manipulation.",
+    "obscure_formal": "Obscure or overly formal language used manipulatively to create confusion, avoid responsibility, or assert superiority."
 }
 
-# Abuse level interpretation
+
+def calculate_abuse_level(scores, thresholds):
+    triggered_scores = [score for label, score in zip(LABELS, scores) if score > thresholds[label]]
+    if not triggered_scores:
+        return 0.0
+    return round(np.mean(triggered_scores) * 100, 2)
+
+
 def interpret_abuse_level(score):
     if score > 80:
         return "Extreme / High Risk"
@@ -77,60 +87,56 @@ def interpret_abuse_level(score):
     else:
         return "Very Low / Likely Safe"
 
-# Main analysis
-def analyze_messages(input_text, context_flags):
+
+def analyze_messages(input_text, risk_flags):
     input_text = input_text.strip()
     if not input_text:
         return "Please enter a message for analysis."
 
-    # Sentiment
     sentiment = sentiment_analyzer(input_text)[0]
     sentiment_label = sentiment['label']
     sentiment_score = sentiment['score']
 
-    # Adjust thresholds if negative tone
     adjusted_thresholds = THRESHOLDS.copy()
-    if sentiment_label.upper() == "NEGATIVE":
-        adjusted_thresholds = {k: v * 0.8 for k, v in THRESHOLDS.items()}
+    if sentiment_label == "NEGATIVE":
+        adjusted_thresholds = {key: val * 0.8 for key, val in THRESHOLDS.items()}
 
-    # Run model
     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 & danger from model
     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:]))
+    danger_flag_count = sum(score > adjusted_thresholds[label] for label, score in zip(DANGER_LABELS, scores[15:18]))
 
-    # Add checkbox context flags
-    if context_flags and len(context_flags) >= 2:
+    contextual_flags = risk_flags if risk_flags else []
+    contextual_risk_score = len(contextual_flags)
+    if contextual_risk_score >= 2:
         danger_flag_count += 1
 
-    # Override if non-abusive
+    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 score
-    triggered_scores = [score for label, score in zip(LABELS, scores) if score > adjusted_thresholds[label]]
-    abuse_level = round(np.mean(triggered_scores) * 100, 2) if triggered_scores else 0.0
+    abuse_level = calculate_abuse_level(scores, THRESHOLDS)
     abuse_description = interpret_abuse_level(abuse_level)
 
-    # Top patterns
-    scored_patterns = [(label, score) for label, score in zip(PATTERN_LABELS, scores[:15])]
-    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
     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."
 
-    # Result
+    scored_patterns = [(label, score) for label, score in zip(PATTERN_LABELS, scores[:15])]
+    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"
@@ -141,21 +147,30 @@ def analyze_messages(input_text, context_flags):
         f"Resources: {resources} \n\n"
         f"Sentiment: {sentiment_label} (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
 
-# Interface
+
 iface = gr.Interface(
     fn=analyze_messages,
     inputs=[
-        gr.Textbox(lines=10, placeholder="Enter message here...", label="input_text"),
-        gr.CheckboxGroup(
-            ["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"],
-            label="Do any of these apply to your situation?",
-            type="value"
-        )
+        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"),
     ],
-    outputs=gr.Textbox(label="Analysis Result"),
     title="Abuse Pattern Detector"
 )