Update app.py

app.py

@@ -1,43 +1,59 @@
-import streamlit as st
-import seaborn as sns
 import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import streamlit as st
+
+# -------------------------
+# Streamlit App — Tipping 
+# -------------------------
 
-# Load data
-data = sns.load_dataset("tips")
+# Load dataset
+tips = sns.load_dataset("tips")
+tips["tip_pct"] = tips["tip"] / tips["total_bill"] * 100
 
-# Page config
-st.set_page_config(page_title='Tips Data Analysis', layout='wide')
+# User question
+st.title("💡 Do people tip more on certain days of the week?")
+st.subheader("Explore average tip percentages by day, time.")
 
-# Title and description
-st.title('Tips Data Analysis with Streamlit')
-st.write('Tips rise with total bill, but the tip/total_bill ratio goes down as total bill increases.')
-st.write('---')
+# Short problem statement
+st.write("This app helps answer whether tipping behavior changes depending on the day of the week. "
+         "Use the filters to explore differences by day and time(Lunch/Dinner) of day.")
 
-# Sidebar widgets
+# Sidebar filters
 with st.sidebar:
-    st.subheader('Filter Options')
-    threshold = st.slider('Threshold for Total Bill', min_value=0, max_value=100, value=10, step=1)
-    day_selector = st.multiselect('Select Day(s)', options=data['day'].unique(), default=data['day'].unique())
-
-# Filter data
-filtered_data = data[data['total_bill'] > threshold]
-filtered_data = filtered_data[filtered_data['day'].isin(day_selector)]
-
-# Main content
-st.subheader('Total Bill vs Tip Scatter Plot')
-
-if not filtered_data.empty:
-    current_selected_day = ', '.join(filtered_data['day'].unique())
-    st.write(f"Scatter plot for total bill vs tip for {current_selected_day} with total bill above {threshold}.")
-    st.write(
-        f"You can see current average tip {filtered_data['tip'].mean():.2f} for selected days. "
-        f"But tip/total_bill ratio is {(filtered_data['tip']/filtered_data['total_bill']).mean():.2%}"
-    )
-    st.scatter_chart(data=filtered_data, x='total_bill', y='tip', color='day')
-    st.dataframe(filtered_data)
+    st.subheader("Filters")
+    # Day filter
+    all_days = sorted(tips["day"].dropna().unique().tolist())
+    selected_days = st.multiselect("Days to show", options=all_days, default=all_days)
+    
+    # Time filter
+    all_times = tips["time"].dropna().unique().tolist()
+    selected_time = st.selectbox("Select time of day", options=["All"] + all_times)
+    
+
+# Apply filters
+filtered = tips[tips["day"].isin(selected_days)]
+if selected_time != "All":
+    filtered = filtered[filtered["time"] == selected_time]
+
+
+# KPI: average tip percentage
+avg_tip = filtered["tip_pct"].mean()
+st.metric("For selected days and time , Average Tip %", f"{avg_tip:.2f}%")
+
+# Visualization
+if not filtered.empty:
+    plt.figure(figsize=(6,4))
+    sns.boxplot(x="day", y="tip_pct", data=filtered, order=all_days)
+    plt.title("Tip Percentage by Day")
+    st.pyplot(plt.gcf())
+    plt.close()
+    
+    # Dynamic insight
+    best_day = filtered.groupby("day")["tip_pct"].mean().idxmax()
+    best_value = filtered.groupby("day")["tip_pct"].mean().max()
+    st.success(f"💡 Insight: On average, for {selected_time}, {best_day} has the highest tip percentage at {best_value:.2f}%")
 else:
-    st.warning("No data available for the selected filters.")
-
-st.write('---')
+    st.info("No data available for the selected filters.")