Create app.py

app.py

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+import gradio as gr
+import pickle
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.preprocessing import LabelEncoder
+from xgboost import XGBClassifier
+
+# Load the model, label encoder, and vectorizer
+with open('xgb_model.pkl', 'rb') as model_file:
+    model = pickle.load(model_file)
+
+with open('label_encoder.pkl', 'rb') as encoder_file:
+    label_encoder = pickle.load(encoder_file)
+
+with open('vectorizer.pkl', 'rb') as vectorizer_file:
+    vectorizer = pickle.load(vectorizer_file)
+
+# Define the prediction function
+def predict(text):
+    try:
+        text_vector = vectorizer.transform([text])
+        prediction = model.predict(text_vector)
+        label = label_encoder.inverse_transform(prediction)[0]
+        return {"prediction": label}
+    except Exception as e:
+        return {"error": str(e)}
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a message..."),
+    outputs="json",
+    title="Spam Detector",
+    description="Enter a message to determine if it is Phishing or Legitimate."
+)
+
+# Launch the Gradio app
+interface.launch()