import streamlit as st
import requests
import numpy as np
import time

st.set_page_config(page_title="Federated Credit Scoring Demo", layout="centered")
st.title("Federated Credit Scoring Demo (Federated Learning)")

# Sidebar configuration
st.sidebar.header("Configuration")
SERVER_URL = st.sidebar.text_input("Server URL", value="http://localhost:8080")
DEMO_MODE = st.sidebar.checkbox("Demo Mode (No Server Required)", value=True)

st.markdown("""
This demo shows how multiple banks can collaboratively train a credit scoring model using federated learning, without sharing raw data.
Enter customer features below to get a credit score prediction from the federated model.
""")

# --- Feature Input Form ---
st.header("Enter Customer Features")
with st.form("feature_form"):
    features = []
    cols = st.columns(4)
    for i in range(32):
        with cols[i % 4]:
            val = st.number_input(f"Feature {i+1}", value=0.0, format="%.4f", key=f"f_{i}")
            features.append(val)
    submitted = st.form_submit_button("Predict Credit Score")

# --- Prediction ---
if submitted:
    if DEMO_MODE:
        # Demo mode - simulate prediction
        with st.spinner("Processing prediction..."):
            time.sleep(1)  # Simulate processing time
        
        # Simple demo prediction based on feature values
        demo_prediction = sum(features) / len(features) * 100 + 500  # Scale to credit score range
        st.success(f"Demo Prediction: Credit Score = {demo_prediction:.2f}")
        st.info("💡 This is a demo prediction. In a real federated system, this would come from the trained model.")
        
        # Show what would happen in real mode
        st.markdown("---")
        st.markdown("**What happens in real federated learning:**")
        st.markdown("1. Your features are sent to the federated server")
        st.markdown("2. Server uses the global model (trained by multiple banks)")
        st.markdown("3. Prediction is returned without exposing any bank's data")
        
    else:
        # Real mode - connect to server
        try:
            with st.spinner("Connecting to federated server..."):
                resp = requests.post(f"{SERVER_URL}/predict", json={"features": features}, timeout=10)
            
            if resp.status_code == 200:
                prediction = resp.json().get("prediction")
                st.success(f"Predicted Credit Score: {prediction:.2f}")
            else:
                st.error(f"Prediction failed: {resp.json().get('error', 'Unknown error')}")
        except Exception as e:
            st.error(f"Error connecting to server: {e}")
            st.info("💡 Try enabling Demo Mode to see the interface without a server.")

# --- Training Progress ---
st.header("Federated Training Progress")

if DEMO_MODE:
    # Demo training progress
    col1, col2, col3, col4 = st.columns(4)
    with col1:
        st.metric("Current Round", "3/10")
    with col2:
        st.metric("Active Clients", "3")
    with col3:
        st.metric("Model Accuracy", "85.2%")
    with col4:
        st.metric("Training Status", "Active")
    
    st.info("💡 Demo mode showing simulated training progress. In real federated learning, multiple banks would be training collaboratively.")
    
else:
    # Real training progress
    try:
        status = requests.get(f"{SERVER_URL}/training_status", timeout=5)
        if status.status_code == 200:
            data = status.json()
            col1, col2, col3, col4 = st.columns(4)
            with col1:
                st.metric("Current Round", f"{data.get('current_round', 0)}/{data.get('total_rounds', 10)}")
            with col2:
                st.metric("Active Clients", data.get('active_clients', 0))
            with col3:
                st.metric("Clients Ready", data.get('clients_ready', 0))
            with col4:
                st.metric("Training Status", "Active" if data.get('training_active', False) else "Inactive")
        else:
            st.warning("Could not fetch training status.")
    except Exception as e:
        st.warning(f"Could not connect to server for training status: {e}")

# --- How it works ---
st.header("How Federated Learning Works")
st.markdown("""
**Traditional ML:** All banks send their data to a central server → Privacy risk ❌

**Federated Learning:** 
1. Each bank keeps their data locally ✅
2. Banks train models on their own data ✅  
3. Only model updates (not data) are shared ✅
4. Server aggregates updates to create global model ✅
5. Global model is distributed back to all banks ✅

**Result:** Collaborative learning without data sharing! 🎯
""")

st.markdown("---")
st.markdown("""
*This is a demonstration of federated learning concepts. For full functionality, run the federated server and clients locally.*
""")