Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,56 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import json
+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:
-"""
-
-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])),
-    ))
+# Load lightweight JSON model
+light_model = json.load(open("lightweight_model.json"))
+means = light_model["feature_means"]
+stds = light_model["feature_stds"]
+
+st.set_page_config(page_title="Employee Attrition Predictor", page_icon="👩‍💼")
+
+st.title("👩‍💼 Employee Attrition Prediction (HF-Safe Version)")
+st.write("This version uses a *lightweight*, non-pickle model for safe deployment.")
+
+# Input Fields
+age = st.number_input("Age", 18, 60, 30)
+monthly_income = st.number_input("Monthly Income", 1000, 20000, 5000)
+job_satisfaction = st.slider("Job Satisfaction (1–4)", 1, 4, 3)
+work_life_balance = st.slider("Work-Life Balance (1–4)", 1, 4, 3)
+years_at_company = st.number_input("Years at Company", 0, 40, 5)
+overtime = st.selectbox("OverTime", ["Yes", "No"])
+
+overtime_value = 1 if overtime == "Yes" else 0
+
+# Create DataFrame
+input_data = pd.DataFrame([{
+    "Age": age,
+    "MonthlyIncome": monthly_income,
+    "JobSatisfaction": job_satisfaction,
+    "WorkLifeBalance": work_life_balance,
+    "YearsAtCompany": years_at_company,
+    "OverTime": overtime_value
+}])
+
+# Basic logistic-like probability calculation
+def simple_predict(df):
+    # Normalize
+    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
+
+if st.button("Predict Attrition"):
+    prob = simple_predict(input_data)
+
+    if prob > 0.5:
+        st.error(f"⚠️ Employee likely to leave (confidence {prob:.2f})")
+    else:
+        st.success(f"✅ Employee likely to stay (confidence {1-prob:.2f})")
+
+st.markdown("---")
+st.caption("HF-Safe: No pickle, no joblib, lightweight model used.")