Update app.py

app.py

@@ -2,7 +2,7 @@ from transformers import pipeline
 import gradio as gr
 import re
 
-# Load models once
+# Load models once for speed
 spam_pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model")
 zero_shot = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
@@ -11,18 +11,19 @@ def is_gibberish(text: str) -> bool:
     return len(text) == 0 or (letters / len(text) < 0.6)
 
 def detect(text: str) -> dict:
-    # Ad/spam check
+    # 1️⃣ Ad/spam check
     spam_flag = spam_pipe(text)[0]["label"] != "LABEL_0"
-    # Irrelevant check
-    top = zero_shot(text, candidate_labels=["relevant", "irrelevant"])["labels"][0]
-    irrelevant_flag = top == "irrelevant"
-    # Gibberish check
+
+    # 2️⃣ Relevance check (anything not a complaint counts as spam)
+    zero_result = zero_shot(text, candidate_labels=["complaint", "not complaint"])
+    not_complaint_flag = zero_result["labels"][0] == "not complaint"
+
+    # 3️⃣ Gibberish check
     gibberish_flag = is_gibberish(text)
 
-    # Final decision: if any of the three is true → spam
-    spam = spam_flag or irrelevant_flag or gibberish_flag
+    # ✅ Final decision
+    spam = spam_flag or not_complaint_flag or gibberish_flag
 
-    # Minimal output
     return {
         "input": text,
         "spam": spam
@@ -33,7 +34,7 @@ demo = gr.Interface(
     inputs=gr.Textbox(label="Enter complaint text"),
     outputs=gr.JSON(label="Result"),
     title="Spam Detector",
-    description="Returns only whether the input is spam or not."
+    description="Returns only the input and spam status (True/False)."
 )
 
 if __name__ == "__main__":