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import streamlit as st
import requests
import pandas as pd
import plotly.express as px
# Function to create status box
def status_box(status, text):
if status == "good":
return f'<div class="status-box status-good">{text}</div>'
elif status == "ok":
return f'<div class="status-box status-ok">{text}</div>'
else:
return f'<div class="status-box status-bad">{text}</div>'
# Function to create a metric with progress bar
def metric_with_progress(label, value, description, status, progress_percent):
progress_class = "progress-good" if status == "good" else "progress-ok" if status == "ok" else "progress-bad"
return f"""
<div class="metric-label">{label}</div>
<div class="metric-value">{value}</div>
<div class="metric-desc">{description}</div>
<div class="progress-container">
<div class="progress-bar {progress_class}" style="width: {progress_percent}%"></div>
</div>
"""
# Custom CSS for styling
st.markdown("""
<style>
.status-box {
display: inline-block;
padding: 6px 0px;
border-radius: 4px;
text-align: center;
font-weight: bold;
width: 100%;
white-space: nowrap;
font-size: 14px;
}
.status-good {
background-color: #0e5814;
color: white;
}
.status-ok {
background-color: #9c6a00;
color: white;
}
.status-bad {
background-color: #8b0000;
color: white;
}
.metric-label {
font-size: 18px;
font-weight: bold;
margin-bottom: 5px;
}
.metric-value {
font-size: 28px;
font-weight: bold;
}
.metric-desc {
font-size: 14px;
color: #ccc;
margin-top: 5px;
}
.progress-container {
width: 100%;
background-color: #333;
border-radius: 5px;
margin-top: 10px;
}
.progress-bar {
height: 10px;
border-radius: 5px;
}
.progress-good {
background-color: #0e5814;
}
.progress-ok {
background-color: #9c6a00;
}
.progress-bad {
background-color: #8b0000;
}
</style>
""", unsafe_allow_html=True)
def show_attrition_prediction():
st.title("Employee Attrition Prediction")
# Create sidebar for inputs
with st.sidebar:
st.header("Employee Details")
# Personal Information
st.subheader("Personal Information")
age = st.slider("Age", 18, 65, 30)
# Job Information
st.subheader("Job Information")
distance_from_home = st.slider("Distance From Home (km)", 1, 30, 5)
job_level = st.slider("Job Level", 1, 5, 2)
# Work Experience
st.subheader("Work Experience")
total_working_years = st.slider("Total Working Years", 0, 40, 5)
years_at_company = st.slider("Years at Company", 0, 40, 3)
# Compensation
st.subheader("Compensation")
monthly_income = st.number_input("Monthly Income", min_value=1000, max_value=20000, value=5000, step=500)
# Work Environment & Satisfaction
st.subheader("Work Environment & Satisfaction")
environment_satisfaction = st.slider("Environment Satisfaction", 1, 4, 3,
help="1=Low, 2=Medium, 3=High, 4=Very High")
job_satisfaction = st.slider("Job Satisfaction", 1, 4, 3,
help="1=Low, 2=Medium, 3=High, 4=Very High")
work_life_balance = st.slider("Work Life Balance", 1, 4, 3,
help="1=Low, 2=Medium, 3=High, 4=Very High")
overtime = st.selectbox("Overtime", ["Yes", "No"])
# Submit button
predict_button = st.button("Predict Attrition Risk")
# Main content area
if predict_button:
# Prepare data for API
data = {
"model_type": "attrition",
"features": {
# Top 10 most important features
"Age": age,
"DistanceFromHome": distance_from_home,
"EnvironmentSatisfaction": environment_satisfaction,
"JobLevel": job_level,
"JobSatisfaction": job_satisfaction,
"MonthlyIncome": monthly_income,
"OverTime": overtime,
"TotalWorkingYears": total_working_years,
"WorkLifeBalance": work_life_balance,
"YearsAtCompany": years_at_company
}
}
# Call API
with st.spinner("Predicting..."):
try:
response = requests.post("http://127.0.0.1:8000/predict", json=data)
if response.status_code == 200:
result = response.json()
# Display results
col1, col2 = st.columns([3, 2])
with col1:
# Prediction result
if result["prediction"] == 0:
st.success("### Low Attrition Risk ✅")
st.markdown("This employee is likely to stay with the company.")
else:
st.error("### High Attrition Risk ⚠️")
st.markdown("This employee may be at risk of leaving.")
# Enhanced explanation
st.info("### Key Factors")
# Feature importance visualization - Updated with actual top 10 features
st.subheader("Factors Affecting Attrition Risk")
feature_importance = {
"Monthly Income": 0.20,
"Years at Company": 0.15,
"Overtime": 0.12,
"Job Satisfaction": 0.11,
"Distance From Home": 0.10,
"Work Life Balance": 0.09,
"Age": 0.08,
"Job Level": 0.06,
"Total Working Years": 0.05,
"Environment Satisfaction": 0.04
}
feature_df = pd.DataFrame({
"feature": list(feature_importance.keys()),
"importance": list(feature_importance.values())
})
fig = px.bar(
feature_df,
x="importance",
y="feature",
orientation='h',
title="Impact of Different Factors on Attrition Risk",
labels={"importance": "Impact", "feature": "Factor"},
height=400
)
st.plotly_chart(fig, use_container_width=True)
with col2:
# Add container for better styling
with st.container():
# Probability gauge
st.subheader("Attrition Probability")
attrition_prob = result["probability"]
st.metric(
label="Risk Level",
value=f"{attrition_prob:.1%}"
)
st.markdown("---")
# Employee Profile Summary
st.subheader("Employee Profile")
# Job Metrics
metrics_col1, metrics_col2 = st.columns(2)
with metrics_col1:
# Job Level
level_status = "good" if job_level >= 3 else "ok"
level_desc = "Senior Position" if job_level >= 3 else "Junior/Mid-level Position"
level_percent = (job_level / 5) * 100
st.markdown(
metric_with_progress("Job Level", str(job_level), level_desc, level_status, level_percent),
unsafe_allow_html=True
)
with metrics_col2:
# Years at Company
tenure_status = "good" if years_at_company >= 5 else "ok"
tenure_desc = "Experienced Employee" if years_at_company >= 5 else "Growing Experience"
tenure_percent = min((years_at_company / 10) * 100, 100)
st.markdown(
metric_with_progress("Tenure", f"{years_at_company} years", tenure_desc, tenure_status, tenure_percent),
unsafe_allow_html=True
)
# Satisfaction Metrics
st.markdown("### Satisfaction Levels")
sat_col1, sat_col2 = st.columns(2)
with sat_col1:
# Job Satisfaction
job_sat_status = "good" if job_satisfaction >= 3 else "ok" if job_satisfaction >= 2 else "bad"
job_sat_desc = "High Satisfaction" if job_satisfaction >= 3 else "Moderate Satisfaction" if job_satisfaction >= 2 else "Low Satisfaction"
job_sat_percent = (job_satisfaction / 4) * 100
st.markdown(
metric_with_progress("Job Satisfaction", f"{job_satisfaction}/4", job_sat_desc, job_sat_status, job_sat_percent),
unsafe_allow_html=True
)
with sat_col2:
# Work Life Balance
wlb_status = "good" if work_life_balance >= 3 else "ok" if work_life_balance >= 2 else "bad"
wlb_desc = "Good Balance" if work_life_balance >= 3 else "Moderate Balance" if work_life_balance >= 2 else "Poor Balance"
wlb_percent = (work_life_balance / 4) * 100
st.markdown(
metric_with_progress("Work-Life Balance", f"{work_life_balance}/4", wlb_desc, wlb_status, wlb_percent),
unsafe_allow_html=True
)
# Compensation Analysis
st.markdown("### Compensation Analysis")
# Monthly Income vs Experience
income_per_year = monthly_income * 12
expected_income = 3000 * (1 + total_working_years * 0.1) * job_level # Simple model for expected income
income_ratio = income_per_year / expected_income
income_status = "good" if income_ratio >= 1 else "ok" if income_ratio >= 0.8 else "bad"
income_desc = f"{'Above' if income_ratio >= 1 else 'Near' if income_ratio >= 0.8 else 'Below'} expected for experience level"
income_percent = min(income_ratio * 100, 100)
st.markdown(
metric_with_progress(
"Annual Income",
f"₹{income_per_year:,.0f}",
income_desc,
income_status,
income_percent
),
unsafe_allow_html=True
)
# If high attrition risk, show recommendations
if result["prediction"] == 1:
st.markdown("### 🚨 Retention Recommendations")
recommendations = []
if monthly_income < 5000:
recommendations.append("Consider a compensation review")
if job_satisfaction < 3:
recommendations.append("Schedule a job satisfaction discussion")
if work_life_balance < 3:
recommendations.append("Review workload and scheduling")
if overtime == "Yes":
recommendations.append("Evaluate workload distribution and overtime requirements")
if environment_satisfaction < 3:
recommendations.append("Assess work environment concerns")
for rec in recommendations:
st.markdown(f"• {rec}")
else:
st.error(f"Error: API returned status code {response.status_code}")
try:
error_detail = response.json()
st.error(f"Error details: {error_detail}")
except:
st.code(response.text)
except Exception as e:
st.error(f"Error connecting to API: {str(e)}")
st.error("Make sure the FastAPI backend is running with: python -m uvicorn src.api.main:app --reload") |