Update app.py

app.py

@@ -8,6 +8,11 @@ warnings.simplefilter("ignore", UserWarning)
 MODEL = pickle.load(open('IF_model_anomaly.pkl','rb'))
 
 st.title("Retail Anomaly")
+st.write(""" Anomaly detection (or outlier detection) is the identification of rare items, events or observations which raise suspicions by
+differing significantly from the majority of the data. Typically, anomalous data can be connected to some kind of problem or rare event such
+as e.g. bank fraud, medical problems, structural defects, malfunctioning equipment etc. This connection makes it very interesting to be able 
+to pick out which data points can be considered anomalies, as identifying these events are typically very interesting from a business perspective.
+""")
 
 def prediction(sales,model):
     sales = np.float64(sales)
@@ -23,3 +28,4 @@ def fun():
 if st.button("Predict"):
     fun()
 
+st.write("For detail description visit https://huggingface.co/spaces/ThirdEyeData/Retail-Anomaly/blob/main/README.md")