Create app.py

app.py

@@ -0,0 +1,36 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+def detect_anomalies(df, threshold):
+    """
+    Detects users with login attempts exceeding the threshold.
+    Args:
+    - df: DataFrame containing user activity data.
+    - threshold: Number of login attempts to consider as anomalous.
+    
+    Returns:
+    - DataFrame of detected anomalies.
+    """
+    anomalies = df[df['login_attempts'] > threshold]
+    return anomalies
+
+# Streamlit app layout
+st.title('User and Entity Behavior Analytics (UEBA)')
+
+# File uploader
+uploaded_file = st.file_uploader("Upload your CSV file", type="csv")
+
+# Threshold input
+threshold = st.slider("Set anomaly detection threshold", min_value=1, max_value=10, value=3)
+
+if uploaded_file is not None:
+    df = pd.read_csv(uploaded_file)
+    
+    if st.button('Detect Anomalies'):
+        anomalies = detect_anomalies(df, threshold)
+        if not anomalies.empty:
+            st.write("Detected Anomalies:")
+            st.dataframe(anomalies)
+        else:
+            st.write("No anomalies detected with the current threshold.")