Both the backend and frontend are currently running:
- Frontend: http://localhost:8082
- Backend API: http://localhost:8000
Open your browser and navigate to:
http://localhost:8082
The form includes the following fields:
Applicant Profile:
- Age (18-100)
- Number of Dependents (0-10)
Financial Stability:
- Annual Income ($)
- Debt-to-Income Ratio (0-1, e.g., 0.45 for 45%)
Credit History:
- Revolving Utilization (0-1, e.g., 0.6 for 60%)
- Open Credit Lines (number of open accounts)
- Delinquencies in Last 2 Years
- FICO Score (300-850)
Loan Details:
- Loan Amount ($)
- Employment Length (years)
- Click "Analyze Credit Risk"
- Wait for the prediction (should take 1-2 seconds)
- View the results:
- Default Probability: The likelihood of loan default (0-100%)
- Risk Label: LOW, MEDIUM, or HIGH
- Top Risk Factors: The features that most influence the prediction
Try these sample applicants:
Age: 45
Annual Income: $85,000
Debt-to-Income Ratio: 0.25
Revolving Utilization: 0.30
Open Credit Lines: 8
Delinquencies (2 yrs): 0
Dependents: 2
FICO Score: 780
Loan Amount: $15,000
Employment Length: 10 years
Age: 28
Annual Income: $35,000
Debt-to-Income Ratio: 0.65
Revolving Utilization: 0.95
Open Credit Lines: 3
Delinquencies (2 yrs): 4
Dependents: 0
FICO Score: 580
Loan Amount: $30,000
Employment Length: 1 year
If you prefer to test via command line:
curl -X POST http://localhost:8000/api/predict \
-H "Content-Type: application/json" \
-d '{
"age": 35,
"annual_income": 60000,
"debt_to_income_ratio": 0.45,
"revolving_utilization": 0.6,
"open_credit_lines": 5,
"delinquencies_2yrs": 2,
"dependents": 1,
"fico_score": 720,
"loan_amount": 25000,
"employment_length": 5
}'# Find the npm process
ps aux | grep "npm run dev"
# Kill it using the PID
kill <PID># Find the uvicorn process
ps aux | grep uvicorn
# Kill it using the PID
kill <PID>Or simply press Ctrl+C in the terminal where each service is running.
cd backend
python -m uvicorn app.main:app --host 0.0.0.0 --port 8000 --reloadnpm run dev- 0-33%: LOW risk - Applicant likely to repay
- 33-66%: MEDIUM risk - Moderate default risk
- 66-100%: HIGH risk - Significant default risk
The application shows the features that most influenced the prediction:
- Increases Risk (red): Features pushing the prediction towards default
- Decreases Risk (green): Features reducing default probability
Each factor includes a human-readable explanation of why it matters.
- Check if backend is running:
curl http://localhost:8000/api/health - If not, restart the backend (see above)
- Ensure all required fields are filled
- Check that values are within valid ranges
- Open browser DevTools (F12) and check Console for errors
- Check backend logs for errors
- Ensure model artifacts exist in
backend/artifacts/ - Try restarting the backend
- Check
PROJECT_STATUS.mdfor detailed project information - Review
backend/README.mdfor backend-specific documentation - Check browser console (F12) for frontend errors
- Check terminal logs for backend errors
Happy Testing! 🎉
Your AI-powered credit risk analyzer is ready to evaluate loan applications with explainable predictions!