accept xlsx or csv file

apps/kpi_analysis/anomalie.py

@@ -8,7 +8,7 @@ import streamlit as st
 
 st.title("KPIsAnomaly Detection")
 
-uploaded_file = st.file_uploader("Upload KPI CSV file", type=["csv"])
+uploaded_file = st.file_uploader("Upload KPI file", type=["csv", "xlsx"])
 penalty = st.number_input("Penalty", min_value=1.0, max_value=100.0, value=2.5)
 
 
@@ -45,7 +45,9 @@ def detect_anomalies(df: pd.DataFrame, penalty: int):
         if len(df_kpi) < 30:
             return None
 
-        series = df_kpi[kpi].values
+        series = df_kpi[kpi].values.copy()
+        # if len(series) > 0:
+        #     series[-1] *= 10  # Boost last value to give it more weight
         try:
             change_indices = detect_change_points(series)
             if not change_indices:
@@ -95,7 +97,13 @@ def detect_anomalies(df: pd.DataFrame, penalty: int):
 
 
 if uploaded_file:
-    df = pd.read_csv(uploaded_file, delimiter=";")
+
+    # Detect if file is csv or excel
+    if uploaded_file.name.endswith(".csv"):
+        df = pd.read_csv(uploaded_file, delimiter=";")
+    else:
+        df = pd.read_excel(uploaded_file, sheet_name=0, skiprows=[1])
+
     anomaly_dict, anomaly_data, all_kpis = detect_anomalies(df, penalty)
 
     if not anomaly_dict: