Update app.py

app.py

@@ -103,30 +103,32 @@ ESCALATION_QUESTIONS = [
     ("Partner has access to firearms or weapons", 4),
     ("Partner threatened to kill you", 3),
     ("Partner threatened you with a weapon", 3),
-    ("Partner ever choked or strangled you", 4),
+    ("Partner has ever choked you, even if you considered it consensual at the time", 4),
     ("Partner injured or threatened your pet(s)", 3),
-    ("Partner destroyed property to intimidate you", 2),
-    ("Partner forced you into unwanted sexual acts", 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)
 ]
 
-# --- Core Analysis Functions (unchanged) ---
-# ... (analyze_single_message, calculate_darvo_score, etc.)
+def analyze_single_message(text, thresholds, motif_flags):
+    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()
+    threshold_labels = [label for label, score in zip(LABELS, scores) if score > thresholds[label]]
+    top_patterns = sorted([(label, score) for label, score in zip(LABELS, scores)], key=lambda x: x[1], reverse=True)[:2]
+    return (np.mean([score for _, score in top_patterns]) * 100, threshold_labels, top_patterns, 0.0, {"label": "supportive"})
 
 # --- Composite Analysis with Escalation Quiz ---
 def analyze_composite(msg1, msg2, msg3, *answers_and_none):
-    # split args: first len(ESCALATION_QUESTIONS) are checkboxes, last is none_of_above
     responses = answers_and_none[:len(ESCALATION_QUESTIONS)]
     none_selected = answers_and_none[-1]
-
-    # compute escalation score
     if none_selected:
         escalation_score = 0
     else:
         escalation_score = sum(w for (_, w), a in zip(ESCALATION_QUESTIONS, responses) if a)
-    # bucket
     if escalation_score >= 16:
         escalation_level = "High"
     elif escalation_score >= 8:
@@ -134,7 +136,6 @@ def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     else:
         escalation_level = "Low"
 
-    # existing abuse analysis
     thresholds = THRESHOLDS.copy()
     messages = [msg1, msg2, msg3]
     active = [m for m in messages if m.strip()]
@@ -146,21 +147,17 @@ def analyze_composite(msg1, msg2, msg3, *answers_and_none):
     top_pattern = max({label for r in results for label in r[2]}, key=lambda l: abuse_scores[0])
     composite_abuse = round(sum(abuse_scores)/len(abuse_scores),2)
 
-    # build output
     out = f"Abuse Intensity: {composite_abuse}%\n"
     out += f"Escalation Potential: {escalation_level} ({escalation_score}/{sum(w for _,w in ESCALATION_QUESTIONS)})"
-    # abuse snippet
     out += generate_risk_snippet(composite_abuse, top_pattern)
     return out
 
-# --- Gradio Interface ---
 textbox_inputs = [
     gr.Textbox(label="Message 1"),
     gr.Textbox(label="Message 2"),
     gr.Textbox(label="Message 3")
 ]
 
-# Escalation quiz inputs
 quiz_boxes = [gr.Checkbox(label=q) for q, _ in ESCALATION_QUESTIONS]
 none_box = gr.Checkbox(label="None of the above")
 
@@ -173,4 +170,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()