Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,41 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import joblib
+
+model = joblib.load("src/kmeans_model.pkl")
+scaler = joblib.load("src/scaler.pkl")
+
+st.title("Mall Customer Segmentation")
+
+st.write(
+    "Customer segmentation using KMeans Clustering"
+)
+
+income = st.slider(
+    "Annual Income (k$)",
+    0,
+    150,
+    50
+)
+
+spending = st.slider(
+    "Spending Score",
+    1,
+    100,
+    50
+)
+
+input_data = pd.DataFrame({
+    "Annual Income (k$)": [income],
+    "Spending Score (1-100)": [spending]
+})
 
-"""
-# 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
+scaled_data = scaler.transform(input_data)
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+prediction = model.predict(scaled_data)[0]
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+if st.button("Predict Cluster"):
 
-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])),
-    ))
+    st.success(
+        f"Customer belongs to Cluster {prediction}"
+    )