Update app.py

app.py

@@ -1,27 +1,41 @@
-
-import streamlit as st
+import gradio as gr
 import joblib
 
 # Load vectorizer and models
 vectorizer = joblib.load("tfidf_vectorizer.pkl")
-models = {
-    "Logistic Regression": joblib.load("logistic_model.pkl"),
-    "Naive Bayes": joblib.load("naive_bayes_model.pkl"),
-    "SVM": joblib.load("svm_model.pkl")
-}
+log_model = joblib.load("logistic_model.pkl")
+nb_model = joblib.load("naive_bayes_model.pkl")
+svm_model = joblib.load("svm_model.pkl")
 
-st.title("📧 Spam Detection App")
-st.write("Choose a model and enter a message to check if it's spam or ham.")
+# Prediction function
+def predict_spam(message, model_name):
+    if not message.strip():
+        return "⚠️ Please enter a message."
+    
+    X_input = vectorizer.transform([message])
+    
+    model = {
+        "Logistic Regression": log_model,
+        "Naive Bayes": nb_model,
+        "SVM": svm_model
+    }[model_name]
+    
+    prediction = model.predict(X_input)[0]
+    
+    return "🟢 Ham" if prediction == 0 else "🔴 Spam"
 
-model_choice = st.selectbox("Select Model", list(models.keys()))
-message = st.text_area("Enter your message", "")
+# Create Gradio Interface
+app = gr.Interface(
+    fn=predict_spam,
+    inputs=[
+        gr.Textbox(label="Enter your message"),
+        gr.Radio(["Logistic Regression", "Naive Bayes", "SVM"], label="Choose a Model")
+    ],
+    outputs="text",
+    title="📧 Spam Message Detector",
+    description="Classify text messages as spam or ham using ML models"
+)
 
-if st.button("Predict"):
-    if message.strip() == "":
-        st.warning("Please enter a message.")
-    else:
-        model = models[model_choice]
-        X_input = vectorizer.transform([message])
-        prediction = model.predict(X_input)[0]
-        label = "🟢 Ham" if prediction == 0 else "🔴 Spam"
-        st.success(f"Prediction ({model_choice}): {label}")
+# Run the app
+if __name__ == "__main__":
+    app.launch()