Update src/streamlit_app.py

src/streamlit_app.py

@@ -4,16 +4,30 @@ import pandas as pd
 import streamlit as st
 import os
 
+st.set_page_config(
+    page_title="Employee Attrition Prediction",
+    page_icon="👩‍💼",
+    layout="centered"
+)
+
+st.title("👩‍💼 Employee Attrition Prediction (HF Safe Version)")
+st.write("This app predicts whether an employee is likely to leave the company.")
+
+# ---------------------------
+# Load lightweight JSON model
+# ---------------------------
 if os.path.exists("lightweight_model.json"):
+    st.success("✅ Model loaded successfully!")
     light_model = json.load(open("lightweight_model.json"))
+
     means = light_model["feature_means"]
     stds = light_model["feature_stds"]
+
 else:
     st.warning("⚠️ lightweight_model.json not found. Using fallback model.")
 
-    # Define default averages (safe fallback)
     means = {
-        "Age": 35, 
+        "Age": 35,
         "MonthlyIncome": 6500,
         "JobSatisfaction": 3,
         "WorkLifeBalance": 3,
@@ -21,4 +35,57 @@ else:
         "OverTime": 0.2
     }
 
-    stds = {k: 1 for k in means}  # avoid divide-by-zero
+    # Avoid divide-by-zero
+    stds = {k: 1 for k in means}
+
+# ---------------------------
+# Prediction Logic (Simple Logistic)
+# ---------------------------
+def simple_predict(df):
+    # Normalize input
+    for col in df.columns:
+        df[col] = (df[col] - means[col]) / (stds[col] + 1e-6)
+
+    score = df.sum(axis=1).values[0]
+    probability = 1 / (1 + np.exp(-score))
+    return probability
+
+# ---------------------------
+# Input Form
+# ---------------------------
+st.header("🔮 Enter Employee Details")
+
+age = st.number_input("Age", min_value=18, max_value=60, value=30)
+income = st.number_input("Monthly Income", min_value=1000, max_value=20000, value=5000)
+job_sat = st.slider("Job Satisfaction (1–4)", 1, 4, 3)
+wlb = st.slider("Work-Life Balance (1–4)", 1, 4, 3)
+years = st.number_input("Years at Company", min_value=0, max_value=40, value=5)
+overtime = st.selectbox("OverTime", ["Yes", "No"])
+overtime_val = 1 if overtime == "Yes" else 0
+
+# Prepare DataFrame
+input_df = pd.DataFrame([{
+    "Age": age,
+    "MonthlyIncome": income,
+    "JobSatisfaction": job_sat,
+    "WorkLifeBalance": wlb,
+    "YearsAtCompany": years,
+    "OverTime": overtime_val
+}])
+
+# ---------------------------
+# Predict
+# ---------------------------
+if st.button("Predict Attrition"):
+    prob = simple_predict(input_df)
+
+    if prob > 0.5:
+        st.error(f"⚠️ Employee likely to leave the company. (Confidence: {prob:.2f})")
+    else:
+        st.success(f"✅ Employee likely to stay. (Confidence: {1 - prob:.2f})")
+
+# ---------------------------
+# Footer
+# ---------------------------
+st.markdown("---")
+st.caption("Built with ❤️ using Streamlit — Safe for HuggingFace Spaces")