Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,92 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 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)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    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.")