Update app.py

app.py

@@ -3,6 +3,7 @@ import torch
 import numpy as np
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 from transformers import RobertaForSequenceClassification, RobertaTokenizer
+from motif_tagging import tag_motifs
 
 # custom fine-tuned sentiment model
 sentiment_model = AutoModelForSequenceClassification.from_pretrained("SamanthaStorm/tether-sentiment")
@@ -78,9 +79,12 @@ def interpret_abuse_level(score):
 
 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."
 
+    motif_tags, matched_phrases = tag_motifs(input_text)
+    contextual_flags = risk_flags + motif_tags  # Combine checkbox and motif flags
+
     sentiment = custom_sentiment(input_text)
     sentiment_label = sentiment['label']
     sentiment_score = sentiment['score']
@@ -136,7 +140,10 @@ def analyze_messages(input_text, risk_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 matched_phrases:
+        result += "\n\n🧠 Detected High-Risk Language:\n"
+        for label, pattern in matched_phrases:
+            result += f"• {label.replace('_', ' ').title()} phrase matched\n"
     return result
 
 iface = gr.Interface(