update

app.py

@@ -2,29 +2,31 @@ import gradio as gr
 from transformers import pipeline
 import pandas as pd
 
-# Load dataset
-DATASET_PATH = "spam.csv"
-df = pd.read_csv(DATASET_PATH, encoding="latin1")
+# Load dataset from Hugging Face Hub
+dataset_path = "hf://datasets/ucirvine/sms_spam/plain_text/train-00000-of-00001.parquet"
+df = pd.read_parquet(dataset_path)
 
 # Load a spam classification model
 classifier = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-sms-spam-detection")
 
 def spam_detector(text):
+    """Detect if a message is spam or not."""
     result = classifier(text)
-    return "Spam" if result[0]['label'].lower() == "Spam" else "Not Spam"
+    label = result[0]['label'].lower()
+    return "Spam" if label == "spam" else "Not Spam"
 
-# Create Gradio UI
+# Create Gradio UI with enhanced styling
 app = gr.Interface(
     fn=spam_detector,
-    inputs=gr.Textbox(label="Enter a message"),
+    inputs=gr.Textbox(label="Enter a message", placeholder="Type your message here..."),
     outputs=gr.Textbox(label="Prediction"),
-    title="Spam Detector",
-    description="Enter a message to check if it's spam or not."
+    title="AI-Powered Spam Detector",
+    description="Enter a message to check if it's spam or not, using a fine-tuned BERT model.",
+    theme="huggingface"
 )
 
 # Run the app
 if __name__ == "__main__":
     print("Loaded dataset preview:")
     print(df.head())
-    app.launch()
-
+    app.launch(server_name="0.0.0.0", server_port=7860, share=True)