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165af41 180fd0b 165af41 180fd0b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import streamlit as st
import joblib
import pandas as pd
import numpy as np
MODEL_PATH = 'src/r_tips.joblib'
SCALER_PATH = 'src/scaler_tips.joblib'
FEATURES = [
'total_bill', 'size', 'bill_per_person',
'sex_Male', 'smoker_Yes',
'day_Sat', 'day_Sun', 'day_Thur',
'time_Lunch'
]
NUMERICAL_COLS = ['total_bill', 'size', 'bill_per_person']
@st.cache_resource
def load_assets():
try:
model = joblib.load(MODEL_PATH)
scaler = joblib.load(SCALER_PATH)
return model, scaler
except Exception as e:
st.error(f"Error loading assets. Ensure '{MODEL_PATH}' and '{SCALER_PATH}' are uploaded. Error: {e}")
return None, None
def preprocess_and_predict(model, scaler, input_data):
df_input = pd.DataFrame([input_data])
df_processed = pd.get_dummies(df_input, columns=["sex", "smoker", "day", "time"], drop_first=True)
for col in FEATURES:
if col not in df_processed.columns:
df_processed[col] = 0
final_features = df_processed[FEATURES]
numerical_part = final_features[NUMERICAL_COLS]
scaled_numerical = scaler.transform(numerical_part)
final_input_array = final_features.values.copy()
final_input_array[:, 0:len(NUMERICAL_COLS)] = scaled_numerical
prediction = model.predict(final_input_array)
return float(prediction[0])
# --- Streamlit Interface ---
st.set_page_config(page_title="Waiter Tip Predictor", layout="centered")
st.title("💰 Waiter Tip Prediction")
st.markdown("Enter bill details and dining context to predict the tip amount ($).")
model, scaler = load_assets()
if model is not None and scaler is not None:
st.sidebar.header("Dining Details")
total_bill = st.sidebar.number_input("Total Bill ($):", min_value=1.0, value=25.0, step=0.5)
size = st.sidebar.number_input("Party Size:", min_value=1, max_value=10, value=3)
bill_per_person = total_bill / size if size > 0 else 0
sex = st.sidebar.selectbox("Server/Diner Sex:", options=["Female", "Male"])
smoker = st.sidebar.selectbox("Smoker at Table?", options=["No", "Yes"])
day = st.sidebar.selectbox("Day of the Week:", options=["Thur", "Fri", "Sat", "Sun"])
time = st.sidebar.selectbox("Time of Day:", options=["Lunch", "Dinner"])
input_data = {
'total_bill': total_bill,
'size': size,
'bill_per_person': bill_per_person,
'sex': sex,
'smoker': smoker,
'day': day,
'time': time
}
st.subheader("Input Summary:")
st.dataframe(pd.DataFrame([input_data]), hide_index=True)
if st.button("Predict Tip Amount"):
with st.spinner('Calculating prediction...'):
predicted_tip = preprocess_and_predict(model, scaler, input_data)
st.success("Prediction Successful!")
st.markdown("### Predicted Tip:")
st.markdown(f"**${predicted_tip:,.2f}**")
st.info(f"The predicted tip is approximately {predicted_tip/total_bill:.1%} of the total bill.") |