Update app.py

app.py

@@ -2,54 +2,38 @@ from transformers import pipeline
 import gradio as gr
 import re
 
-# ---------- Load models once (faster) ----------
+# Load models once
 spam_pipe = pipeline("text-classification", model="Titeiiko/OTIS-Official-Spam-Model")
 zero_shot = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
-# ---------- Helper ----------
 def is_gibberish(text: str) -> bool:
     letters = len(re.findall(r"[a-zA-Z]", text))
-    total = len(text)
-    if total == 0:
-        return True
-    return letters / total < 0.6   # more than 40% non-letters → gibberish
+    return len(text) == 0 or (letters / len(text) < 0.6)
 
-# ---------- Core Detection ----------
 def detect(text: str) -> dict:
-    """
-    Returns a JSON-like dictionary with individual flags and final decision
-    """
-    # Ad/spam detection
-    spam_result = spam_pipe(text)[0]
-    spam_flag = spam_result["label"] != "LABEL_0"
-
-    # Off-topic / irrelevant detection
-    zero_result = zero_shot(text, candidate_labels=["relevant", "irrelevant"])
-    # zero-shot gives scores sorted by confidence
-    top_label = zero_result["labels"][0]
-    irrelevant_flag = top_label == "irrelevant"
-
-    # Gibberish detection
+    # 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
     gibberish_flag = is_gibberish(text)
 
+    # Final decision: if any of the three is true → spam
+    spam = spam_flag or irrelevant_flag or gibberish_flag
+
+    # Minimal output
     return {
         "input": text,
-        "spam_flag": spam_flag,
-        "irrelevant_flag": irrelevant_flag,
-        "gibberish_flag": gibberish_flag,
-        "overall_flag": spam_flag or irrelevant_flag or gibberish_flag,
-        "spam_model_confidence": float(spam_result["score"]),
-        "zero_shot_top_label": top_label,
-        "zero_shot_confidence": float(zero_result["scores"][0])
+        "spam": spam
     }
 
-# ---------- Gradio Interface ----------
 demo = gr.Interface(
     fn=detect,
-    inputs=gr.Textbox(label="Enter text to classify"),
-    outputs=gr.JSON(label="Detection Result"),
-    title="Spam / Irrelevance / Gibberish Detector",
-    description="Combines spam detection, off-topic classification, and gibberish check."
+    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."
 )
 
 if __name__ == "__main__":