Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,77 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import joblib
+
+# --- Configuration ---
+MODEL_PATH = 'src/customer_model.joblib'
+SCALER_PATH = 'src/scaler.joblib' 
+
+FEATURES = ['Income', 'Seniority', 'Spending'] 
+
+@st.cache_resource
+def load_assets():
+    try:
+        model = joblib.load(MODEL_PATH)
+        scaler = joblib.load(SCALER_PATH)
+        return model, scaler
+    except FileNotFoundError:
+        st.error(f"Error: Model or Scaler file not found. Ensure both '{MODEL_PATH}' and '{SCALER_PATH}' are uploaded to the Space.")
+        return None, None
+    except Exception as e:
+        st.error(f"Error loading assets: {e}")
+        return None, None
+
+def predict_cluster(model, scaler, input_data):
+    input_df = pd.DataFrame([input_data])
+    
+    scaled_data = scaler.transform(input_df[FEATURES])
+    
+    prediction = model.predict(scaled_data) 
+    
+    return prediction[0]
+
+# --- Streamlit Interface ---
+st.set_page_config(page_title="Customer Clustering App", layout="wide")
+st.title("🥇 Customer Personality Cluster Prediction")
+st.markdown("Use the sidebar to input customer features and predict their cluster.")
+
+model, scaler = load_assets()
+
+if model is not None and scaler is not None:
+    st.sidebar.header("Input Customer Features")
+
+    income = st.sidebar.slider("Income ($):", min_value=1000, max_value=200000, value=50000)
+    seniority = st.sidebar.slider("Seniority (Years as customer):", min_value=0, max_value=50, value=10)
+    spending = st.sidebar.slider("Total Spending ($):", min_value=0, max_value=3000, value=500)
+    
+    input_data = {
+        'Income': income,
+        'Seniority': seniority,
+        'Spending': spending
+    }
+
+    st.subheader("Current Input Data:")
+    st.write(pd.DataFrame([input_data]))
+
+    if st.button("Predict Cluster"):
+        with st.spinner('Predicting...'):
+            cluster_id = predict_cluster(model, scaler, input_data)
+            
+            cluster_descriptions = {
+                0: "👑 **Cluster 0: High Potential**",
+                1: "🚨 **Cluster 1: Need Attention**",
+                2: "⏳ **Cluster 2: Leaky Bucket**",
+                3: "⭐ **Cluster 3: Stars**",
+                # Add more cluster IDs and descriptions here if needed
+            }
+            
+            description = cluster_descriptions.get(cluster_id, f"🔍 Cluster ID **{cluster_id}** (Undefined)")
+            
+            st.success(f"Prediction Complete! The customer belongs to:")
+            st.markdown(f"## {description}")
+
 
-"""
-# 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])),
-    ))
+                0: '',
+    1: '',
+    2: '',
+    3: ''})