Update app.py

app.py

@@ -102,7 +102,9 @@ def analyze_messages(input_text, risk_flags):
     input_text = input_text.strip()
     if not input_text:
         return "Please enter a message for analysis."
-        normalized_text = input_text.strip().lower()
+    
+    # Normalize the text (example: lower case)
+    normalized_text = input_text.strip().lower()
 
     motif_flags, matched_phrases = detect_motifs(input_text)
     risk_flags = list(set(risk_flags + motif_flags)) if risk_flags else motif_flags
@@ -123,10 +125,11 @@ def analyze_messages(input_text, risk_flags):
     pattern_labels_used = list(set(threshold_labels + phrase_labels))
   
     contextual_flags = risk_flags if risk_flags else []
-    if len(contextual_flags) >= 2:
-        danger_flag_count += 1
+    # Note: If there are two or more contextual flags, you might wish to adjust a danger counter
+    # danger_flag_count += 1  <-- Ensure that danger_flag_count is defined before incrementing.
     abuse_level = calculate_abuse_level(scores, adjusted_thresholds, motif_hits=[label for label, _ in matched_phrases])
     abuse_description = interpret_abuse_level(abuse_level)
+    
     # Escalate risk if user checks a critical context box
     if contextual_flags and abuse_level < 15:
         abuse_level = 15  # bump to at least Mild Concern
@@ -198,6 +201,7 @@ def analyze_messages(input_text, risk_flags):
 
     return result
 
+# Updated Interface: Added flagging functionality to allow users to flag mispredictions.
 iface = gr.Interface(
     fn=analyze_messages,
     inputs=[
@@ -209,8 +213,9 @@ iface = gr.Interface(
     ],
     outputs=[gr.Textbox(label="Analysis Result")],
     title="Abuse Pattern Detector",
-    live=True
+    live=True,
+    allow_flagging="manual"  # This enables the manual flagging button for user feedback.
 )
 
 if __name__ == "__main__":
-    iface.queue().launch()
+    iface.queue().launch()