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fe2cf90 d9b43c1 f5cc3f8 d9b43c1 a795bfc a13da1c d9b43c1 fe2cf90 d9b43c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | import streamlit as st
import joblib
import pandas as pd
import os
# Debug: List files in current directory to see what's available
st.write("Current directory files:", os.listdir("."))
# Load model and columns from the same src folder
model = joblib.load("churn_predictor_xgb.pkl")
columns = joblib.load("churn_model_columns.pkl")
st.set_page_config(page_title="RUCTURO", page_icon="🟣", layout="centered")
# Premium futuristic CSS
st.markdown("""
<style>
.main {
background: linear-gradient(to bottom, #0F172A, #1E293B);
color: #F1F5F9;
}
h1, h2, h3 {
color: #6D28D9;
text-shadow: 0 0 10px #22D3EE;
font-family: 'Orbitron', sans-serif;
}
.stButton>button {
background: linear-gradient(to right, #6D28D9, #22D3EE);
color: white;
border: none;
border-radius: 12px;
padding: 12px 24px;
font-size: 18px;
box-shadow: 0 0 20px #22D3EE;
transition: all 0.3s;
}
.stButton>button:hover {
transform: scale(1.05);
box-shadow: 0 0 30px #6D28D9;
}
.risk-high { color: #F87171; font-size: 36px; font-weight: bold; text-shadow: 0 0 10px #F87171; }
.risk-medium { color: #FBBF24; font-size: 32px; font-weight: bold; }
.risk-low { color: #10B981; font-size: 32px; font-weight: bold; }
@import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@700&display=swap');
</style>
""", unsafe_allow_html=True)
st.title("🟣 RUCTURO")
st.markdown("### Premium AI-Powered Customer Churn Predictor for SaaS Tools")
st.markdown("Predict churn risk instantly and get actionable retention insights.")
# Input form
with st.form("churn_form"):
col1, col2 = st.columns(2)
with col1:
tenure = st.slider("Tenure (months)", 0, 72, 12)
monthly = st.slider("Monthly Charges ($)", 18, 120, 70)
senior = st.radio("Senior Citizen", [0, 1], format_func=lambda x: "Yes" if x else "No")
contract = st.selectbox("Contract Type", ["Month-to-month", "One year", "Two year"])
internet = st.selectbox("Internet Service", ["DSL", "Fiber optic", "No"])
tech_support = st.selectbox("Tech Support", ["Yes", "No"])
with col2:
online_security = st.selectbox("Online Security", ["Yes", "No"])
payment = st.selectbox("Payment Method", ["Electronic check", "Mailed check", "Bank transfer (automatic)", "Credit card (automatic)"])
paperless = st.selectbox("Paperless Billing", ["Yes", "No"])
num_services = st.slider("Number of Services (approx)", 0, 10, 5)
has_internet = st.radio("Has Internet", [0, 1], format_func=lambda x: "Yes" if x else "No")
submitted = st.form_submit_button("Predict Churn Risk")
if submitted:
data = {
'tenure': tenure,
'MonthlyCharges': monthly,
'TotalCharges': monthly * (tenure + 1),
'SeniorCitizen': senior,
'Num_Services': num_services,
'Has_Internet': has_internet,
'TotalCharges_per_Tenure': monthly,
'Charges_Increase': 0,
'Is_Month_to_Month': 1 if contract == "Month-to-month" else 0,
'Is_Fiber_Optic': 1 if internet == "Fiber optic" else 0,
'Has_No_TechSupport': 1 if tech_support == "No" else 0,
'PaperlessBilling_Yes': 1 if paperless == "Yes" else 0,
}
df = pd.DataFrame([data])
df = pd.get_dummies(df, columns=['Contract', 'InternetService', 'TechSupport', 'OnlineSecurity', 'PaymentMethod'])
df = df.reindex(columns=columns, fill_value=0)
prob = model.predict_proba(df)[0, 1]
st.markdown("---")
st.markdown(f"### Churn Probability: **{prob:.1%}**")
if prob >= 0.4:
st.markdown('<p class="risk-high">🟥 HIGH RISK – Immediate Action Needed</p>', unsafe_allow_html=True)
st.warning("• Short tenure + month-to-month contract\n• Fiber optic service\n• No tech support\n• Electronic check payment\n**Recommendation**: Offer discount, upgrade, or dedicated support")
elif prob >= 0.2:
st.markdown('<p class="risk-medium">🟧 Medium Risk</p>', unsafe_allow_html=True)
st.info("Monitor closely – consider proactive engagement")
else:
st.markdown('<p class="risk-low">🟩 Low Risk – Strong Retention</p>', unsafe_allow_html=True)
st.success("Excellent loyalty signals – keep up the great service!")
st.markdown("---")
st.markdown("Powered by XGBoost • Built for SaaS teams • Premium futuristic design • © 2025") |