Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,30 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import joblib
+import numpy as np
 
-"""
-# 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:
-"""
+# Load the saved model and scaler
+model = joblib.load("medical_model.pkl")
+scaler = joblib.load("scaler.pkl")
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+st.title("Medical Insurance Cost Predictor")
+st.write("Enter your details to estimate insurance charges.")
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+# User Inputs
+age = st.number_input("Age", min_value=18, max_value=100, value=25)
+bmi = st.number_input("BMI", min_value=10.0, max_value=60.0, value=22.0)
+children = st.slider("Number of Children", 0, 5, 0)
+smoker = st.selectbox("Do you smoke?", ["Yes", "No"])
+sex = st.selectbox("Gender", ["Male", "Female"])
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+# Map inputs to match the model's training format
+smoker_val = 1 if smoker == "Yes" else 0
+sex_val = 1 if sex == "Male" else 0
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+# (Note: For simplicity, we assume region 'Southwest' as default here)
+# For full accuracy, you'd add region radio buttons matching your get_dummies columns.
+features = np.array([[age, sex_val, bmi, children, smoker_val, 0, 0, 0]])
+features_scaled = scaler.transform(features)
 
-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 Charges"):
+    prediction = model.predict(features_scaled)
+    st.success(f"Estimated Insurance Cost: ${prediction[0]:,.2f}")