Update app.py

app.py

@@ -5,23 +5,38 @@ import pandas as pd
 # Load model once
 model = Detoxify('original')
 
+# Threshold for flagging a comment as risky
+TOXICITY_THRESHOLD = 0.7
+
 def classify_multiple(comments):
-    # Split input by newlines and clean
+    # Split input into lines
     comment_list = [c.strip() for c in comments.split('\n') if c.strip()]
     if not comment_list:
         return "Please enter at least one valid comment."
 
-    results = model.predict(comment_list)  # Returns a dict of lists
-
+    # Predict toxicity scores
+    results = model.predict(comment_list)
     df = pd.DataFrame(results, index=comment_list).round(4)
+
+    # Capitalize headers
+    df.columns = [col.replace("_", " ").title().replace(" ", "_") for col in df.columns]
+    df.columns = [col.replace("_", " ") for col in df.columns]
+
+    # Add warning column
+    def check_warning(row):
+        return "⚠️ High Risk" if any(score > TOXICITY_THRESHOLD for score in row) else "✅ Safe"
+
+    df["⚠️ Warning"] = df.apply(check_warning, axis=1)
+
     return df
 
+# UI setup
 iface = gr.Interface(
     fn=classify_multiple,
     inputs=gr.Textbox(lines=8, placeholder="Enter one or more comments, each on a new line..."),
-    outputs=gr.Dataframe(label="Toxicity Predictions"),
-    title="💬 Toxic Comment Classifier (Multi-Comment)",
-    description="Paste one or more comments. Each will be scored for toxicity, severe toxicity, insult, threat, obscene, and identity hate using Detoxify."
+    outputs=gr.Dataframe(label="Toxicity Predictions with Warnings"),
+    title="💬 Toxic Comment Classifier (Multi-Comment, with Warnings)",
+    description="Paste one or more comments. Each comment is scored for toxicity, and flagged as ⚠️ if any label exceeds 0.7."
 )
 
 if __name__ == "__main__":