changed password and username

pages/admin.py

@@ -3,11 +3,10 @@ import pandas as pd
 import plotly.graph_objects as go
 import streamlit as st
 from sklearn.metrics import mean_squared_error
-
 from src.data_api_calls import get_combined_data
 
-USERNAME = "dragonkiller"
-PASSWORD = "donkey"
+USERNAME = "admin"
+PASSWORD = "password"
 
 st.title("Admin Panel")
 
@@ -41,23 +40,31 @@ else:
 
     # Display message based on whether data is complete
     if missing_values.values.any():
-    # Warning message if there are missing values
-        st.markdown("<h4 style='color: #E68B0A;'>Warning: Some data is missing!</h4>", unsafe_allow_html=True)
+        # Warning message if there are missing values
+        st.markdown(
+            "<h4 style='color: #E68B0A;'>Warning: Some data is missing!</h4>",
+            unsafe_allow_html=True,
+        )
 
-    # Identify columns with missing values
+        # Identify columns with missing values
         missing_columns = table_data.columns[missing_values.any()].tolist()
 
-    # Identify rows (dates) with missing values
-        missing_rows = table_data[missing_values.any(axis=1)]['Date'].tolist()
+        # Identify rows (dates) with missing values
+        missing_rows = table_data[missing_values.any(axis=1)]["Date"].tolist()
 
-    # Display additional information about missing columns and rows
+        # Display additional information about missing columns and rows
         if missing_columns:
             st.markdown(f"**Columns with missing data:** {', '.join(missing_columns)}")
         if missing_rows:
-            st.markdown(f"**Rows with missing data (dates):** {', '.join(missing_rows)}")
+            st.markdown(
+                f"**Rows with missing data (dates):** {', '.join(missing_rows)}"
+            )
     else:
-    # Success message if no data is missing
-        st.markdown("<h4 style='color: #77C124;'>All data is complete!</h4>", unsafe_allow_html=True)
+        # Success message if no data is missing
+        st.markdown(
+            "<h4 style='color: #77C124;'>All data is complete!</h4>",
+            unsafe_allow_html=True,
+        )
     st.dataframe(table_data)
     # Actual data vs 1,2,3 days ahead predictions
     actual_data = pd.read_csv("pollution_data.csv")