Update app.py

app.py

@@ -5,9 +5,9 @@ from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.naive_bayes import MultinomialNB
 
 # Load and clean the dataset
-data = pd.read_csv(r"spam.csv")
+data = pd.read_csv("spam.csv")
 data.drop_duplicates(inplace=True)
-data['Category'] = data['Category'].replace(['ham', 'spam'], ['Not spam', 'spam'])
+data['Category'] = data['Category'].replace(['ham', 'spam'], ['Not spam', 'Spam'])
 
 # Prepare data
 X = data['Message']
@@ -29,13 +29,37 @@ model.fit(X_train_features, y_train)
 def predict_spam(message):
     message_features = vectorizer.transform([message])
     prediction = model.predict(message_features)[0]
-    return f"Prediction: {prediction}"
-
-# Launch Gradio interface
-gr.Interface(
-    fn=predict_spam,
-    inputs="text",
-    outputs="text",
-    title="📩 Spam Detection with Gradio",
-    description="Enter a message and the model will predict whether it's spam or not."
-).launch()
+    return prediction
+
+# Build better UI
+with gr.Blocks(theme=gr.themes.Default()) as demo:
+    gr.Markdown("## 📩 Spam Detector\nEnter any message below to check if it's spam or not.")
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            message_input = gr.Textbox(
+                label="Your Message",
+                placeholder="e.g. Congratulations! You've won a prize...",
+                lines=4
+            )
+            submit_btn = gr.Button("Detect Spam")
+        with gr.Column(scale=2):
+            result_output = gr.Label(label="Prediction")
+    
+    examples = [
+        ["Congratulations! You have been selected for a free cruise!"],
+        ["Hey, what time is class tomorrow?"],
+        ["Win cash now!!! Click here"],
+        ["Lunch at 1 PM?"],
+    ]
+    
+    gr.Examples(
+        examples=examples,
+        inputs=message_input
+    )
+    
+    submit_btn.click(fn=predict_spam, inputs=message_input, outputs=result_output)
+
+# Launch app
+if __name__ == "__main__":
+    demo.launch()