Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,50 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# 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])),
-    ))
+import pandas as pd
+import numpy as np
+import joblib
+
+st.set_page_config(page_title="Tip Prediction", layout="centered")
+# Load the trained model
+model = joblib.load('taximodel.pkl')
+
+#Streamlit UI configuration - Must be the first Streamlit command in your script
+#st.set_page_config(page_title="Tip Prediction, layout = 'centered')
+
+# Title of the app
+st.title("Tip Predictor")
+st.write("Enter the details of your taxi ride to predict the tip amount.")
+
+# Streamlit UI to take inputs
+with st.form("tip_form"):
+    total_bill = st.slider("Total Bill ($)", min_value=0.0, max_value=500.0, value=20.0)
+    sex = st.selectbox("Sex", ["Male", "Female"])
+    smoker = st.selectbox("Smoker", ["Yes", "No"])
+    day = st.selectbox("Day of the Week", ["Thur", "Fri", "Sat", "Sun"])
+    time = st.selectbox("Time of Day", ["Lunch", "Dinner"])
+    size = st.number_input("Party Size", min_value=1, value=2)
+
+    # Submit button
+    submitted = st.form_submit_button("Predict Tip")
+
+#Prediction on form submission
+if submitted:
+    input_df = pd.DataFrame([{
+        'total_bill': total_bill,
+        'sex': sex,
+        'smoker': smoker,
+        'day': day,
+        'time': time,
+        'size': size
+    }])
+
+    try:
+        # predict the tip
+        prediction = model.predict(input_df)
+
+        #Ensure the output is a scaler value
+        predicted_tip = prediction[0] if isinstance(prediction,(list, np.ndarray)) else prediction
+
+        # Display the predicted tip
+        st.success(f"Predicted Tip Amount: **${predicted_tip:.2f}**")
+    except Exception as e:
+        st.error(f"Error: {str(e)}")