import streamlit as st import requests import uuid from datetime import datetime # Page configuration st.set_page_config( page_title="RiskGuard AI: Credit Risk Modelling", page_icon="🛡ī¸", layout="wide" ) # Initialize session storage if "chat_history" not in st.session_state: st.session_state.chat_history = [] if "thread_id" not in st.session_state: st.session_state.thread_id = str(uuid.uuid4()) if "analysis_done" not in st.session_state: st.session_state.analysis_done = False if "show_info" not in st.session_state: st.session_state.show_info = False # ========================= ENHANCED UI STYLING (UPDATED) ========================= st.markdown(""" """, unsafe_allow_html=True) # ========================= HEADER ========================= st.markdown("""

🛡ī¸ RiskGuard AI

Advanced AI-Powered Credit Risk Assessment Platform

""", unsafe_allow_html=True) # Alert Banner st.markdown("""
⚠ī¸ Note: First request may take up to 20 seconds (API cold start).
""", unsafe_allow_html=True) # Info Toggle if st.button("ℹī¸ How It Works"): st.session_state.show_info = not st.session_state.show_info if st.session_state.show_info: st.markdown("""

📊 About RiskGuard AI

What we analyze:

Our AI provides:

""", unsafe_allow_html=True) # ========================= INPUT FORM ========================= col_left, col_right = st.columns([1, 1], gap="large") with col_left: st.markdown('
', unsafe_allow_html=True) st.markdown('
👤 Personal & Loan Information
', unsafe_allow_html=True) age = st.number_input("Age", 18, 100, 28, help="Applicant's age in years") income = st.number_input("Annual Income (₹)", 0, 50000000, 1200000, step=50000, help="Total annual income") loan_amount = st.number_input("Loan Amount (₹)", 0, 50000000, 2500000, step=100000, help="Requested loan amount") loan_tenure_months = st.number_input("Loan Tenure (months)", 0, 480, 36, help="Loan repayment period") loan_purpose = st.selectbox("Loan Purpose", ["Education", "Home", "Auto", "Personal"]) residence_type = st.selectbox("Residence Type", ["Owned", "Rented", "Mortgage"]) loan_type = st.selectbox("Loan Type", ["Secured", "Unsecured"]) st.markdown('
', unsafe_allow_html=True) loan_to_income_ratio = loan_amount / income if income > 0 else 0 st.markdown(f"""
Loan-to-Income Ratio
{loan_to_income_ratio:.2f}x
""", unsafe_allow_html=True) with col_right: st.markdown('
', unsafe_allow_html=True) st.markdown('
đŸ’ŗ Credit Profile
', unsafe_allow_html=True) avg_dpd_per_delinquency = st.number_input("Average Days Past Due", 0, 200, 20, help="Average days past due per delinquency") delinquency_ratio = st.number_input("Delinquency Ratio (%)", 0, 100, 30, help="Percentage of delinquent accounts") credit_utilization_ratio = st.number_input("Credit Utilization (%)", 0, 100, 30, help="Percentage of available credit used") num_open_accounts = st.number_input("Open Loan Accounts", 0, 20, 2, help="Number of currently active loan accounts") st.markdown('
', unsafe_allow_html=True) # ========================= ANALYSIS BUTTON ========================= st.markdown("
", unsafe_allow_html=True) if st.button("🔍 Analyze Credit Risk", use_container_width=True): API_URL = st.secrets["API_URL"] payload = { "age": age, "income": income, "loan_amount": loan_amount, "loan_tenure_months": loan_tenure_months, "avg_dpd_per_delinquency": avg_dpd_per_delinquency, "delinquency_ratio": delinquency_ratio, "credit_utilization_ratio": credit_utilization_ratio, "num_open_accounts": num_open_accounts, "residence_type": residence_type, "loan_purpose": loan_purpose, "loan_type": loan_type } with st.spinner("🤖 Analyzing your credit profile..."): try: r = requests.post(API_URL, json=payload, timeout=30) if r.status_code == 200: result = r.json() # Display Results st.markdown('
', unsafe_allow_html=True) st.markdown('

📊 Assessment Results

', unsafe_allow_html=True) st.markdown(f"""
Default Probability
{result['probability']:.2%}
Credit Score
{result['credit_score']}
Risk Rating
{result['rating']}
""", unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) if result.get("advisor_response"): st.markdown(f"""

🤖 AI Credit Advisor Insights

{result['advisor_response']}

""", unsafe_allow_html=True) st.session_state.probability = result["probability"] st.session_state.credit_score = result["credit_score"] st.session_state.rating = result["rating"] st.session_state.advisor_reply = result["advisor_response"] st.session_state.analysis_done = True st.success("✅ Analysis complete! You can now chat with our AI assistant below.") else: st.error(f"❌ API Error: {r.status_code} - {r.text}") except requests.exceptions.Timeout: st.error("⏱ī¸ Request timed out. Please try again.") except Exception as e: st.error(f"❌ Request failed: {e}") # ========================= CHATBOT ========================= if st.session_state.analysis_done: st.markdown("

", unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) st.markdown('
đŸ’Ŧ Interactive Loan Chat Assistant
', unsafe_allow_html=True) if st.session_state.chat_history: st.markdown('
', unsafe_allow_html=True) for role, msg in st.session_state.chat_history: bubble = "chat-bubble-user" if role == "user" else "chat-bubble-bot" prefix = "You: " if role == "user" else "🤖 Assistant: " st.markdown(f"
{prefix}{msg}
", unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) user_query = st.text_input("Ask a question about your credit assessment:", placeholder="e.g., How can I improve my credit score?") col_send, col_clear = st.columns([3, 1]) with col_send: send_button = st.button("📤 Send Message", use_container_width=True) with col_clear: if st.button("🗑ī¸ Clear Chat", use_container_width=True): st.session_state.chat_history = [] st.session_state.thread_id = str(uuid.uuid4()) st.experimental_rerun() if send_button and user_query.strip(): CHAT_URL = st.secrets["CHAT_URL"] payload = { "thread_id": st.session_state.thread_id, "message": user_query, "probability": st.session_state.probability, "credit_score": st.session_state.credit_score, "rating": st.session_state.rating, "advisor_reply": st.session_state.advisor_reply } with st.spinner("🤖 Thinking..."): try: r = requests.post(CHAT_URL, json=payload, timeout=30) if r.status_code == 200: reply = r.json()["response"] st.session_state.chat_history.append(("user", user_query)) st.session_state.chat_history.append(("bot", reply)) st.experimental_rerun() else: st.error(f"❌ Chat server error: {r.status_code}") except Exception as e: st.error(f"❌ Chat failed: {e}") st.markdown('
', unsafe_allow_html=True) # Footer st.markdown("

", unsafe_allow_html=True) st.markdown("""

🛡ī¸ RiskGuard AI Š 2025 | Powered by Advanced Machine Learning

For demonstration purposes only. Not financial advice.

""", unsafe_allow_html=True)