Update app.py

app.py

@@ -1,35 +1,26 @@
 from transformers import pipeline
 import gradio as gr
 
-# Load spam detection model
-classifier = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-sms-spam-detection")
+classifier = pipeline("text-classification", model="j-hartmann/sms-spam-detection-model")
 
-# Map model labels to readable output
-label_map = {
-    "LABEL_0": "HAM (Not Spam)",
-    "LABEL_1": "SPAM"
-}
-
-# Define prediction function
 def predict_spam(text):
     result = classifier(text)[0]
-    raw_label = result['label']
-    label = label_map.get(raw_label, raw_label)  # default to raw_label if not in map
+    label = result['label'] 
     confidence = round(result['score'] * 100, 2)
-    return f" Result: {label}\n Confidence: {confidence}%"
+    return f" Result: {label.upper()}\n Confidence: {confidence}%"
 
-# Gradio interface
 gr.Interface(
     fn=predict_spam,
-    inputs=gr.Textbox(lines=4, placeholder="Enter a message..."),
+    inputs=gr.Textbox(lines=4, placeholder="Enter an SMS message..."),
     outputs="text",
-    title=" Spam Detector",
-    description="Enter a message to check if it's spam or not using a pretrained BERT model.",
+    title=" Spam Detector (Improved)",
+    description="Enter a message to check if it's spam or not using a stronger BERT model.",
     examples=[
-        "Win a free iPhone now!",
-        "Hey, are we still on for dinner tonight?",
-        "Get your free prize by clicking here",
-        "Let's submit the assignment before 11pm"
+        "Congratulations! You’ve won a free cruise to the Bahamas. Reply YES to claim.",
+        "Hey, are you coming to class today?",
+        "Click here to get your free gift card now!",
+        "We need to submit the project by tonight."
     ]
 ).launch()
 
+