Update app.py

app.py

@@ -60,6 +60,13 @@ preprocessor = joblib.load("preprocessor.pkl")
 scaler = joblib.load("scaler.pkl")
 label_encoders = joblib.load("label_encoders.pkl")
 
+def predict_lifecycle(category, product_name, price, rating, num_reviews, stock_quantity, discount):
+    input_df = pd.DataFrame([[category, product_name, price, rating, num_reviews, stock_quantity, discount]],
+                            columns=["Category", "ProductName", "Price", "Rating", "NumReviews", "StockQuantity", "Discount"])
+    processed_input = preprocessor.transform(input_df)
+    prediction = product_model.predict(processed_input)[0]
+    return f"Predicted Product Lifecycle: {round(prediction, 2)} years"
+
 def recommend_products(category):
     recommended = recommendation_knn.kneighbors([[category]], return_distance=False)
     return recommended.tolist()