# interview_demo.py - Streamlit app for interviews
import streamlit as st
import cv2
import tempfile
import numpy as np
from PIL import Image

def interview_demo_app():
    st.set_page_config(page_title="Live Demo", layout="wide")
    
    st.title("🎯 Live Demo - AI Attendance System")
    
    st.info("""
    **How to demonstrate:**
    1. Click 'Start Camera' below
    2. Show your face to the camera
    3. See real-time detection and recognition
    4. Watch attendance log update automatically
    """)
    
    # Sidebar with status
    with st.sidebar:
        st.header("🔴 System Status")
        st.success("✅ Camera Ready")
        st.success("✅ Model Loaded")
        st.success("✅ Database Connected")
        
        st.header("📊 Session Stats")
        if 'detections' not in st.session_state:
            st.session_state.detections = []
        
        st.metric("Faces Detected", len(st.session_state.detections))
    
    # Camera feed
    col1, col2 = st.columns([2, 1])
    
    with col1:
        st.subheader("📹 Live Feed")
        camera_placeholder = st.empty()
        
        # Start camera button
        if st.button("🚀 Start Camera", use_container_width=True):
            cap = cv2.VideoCapture(0)
            
            if not cap.isOpened():
                st.error("⚠️ Could not access camera")
            else:
                # Process frames
                frame_count = 0
                while True:
                    ret, frame = cap.read()
                    if not ret:
                        break
                    
                    # Your detection code here
                    # For demo, just show frame with overlay
                    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                    frame = cv2.putText(frame, "🟢 Live Demo", (10, 30),
                                      cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
                    
                    camera_placeholder.image(frame, use_container_width=True)
                    
                    # Simulate detections
                    if frame_count % 30 == 0:
                        st.session_state.detections.append({
                            'time': datetime.now(),
                            'name': 'Demo User'
                        })
                    
                    frame_count += 1
    
    with col2:
        st.subheader("📋 Live Attendance")
        attendance_table = st.empty()
        
        # Show attendance log
        if st.session_state.detections:
            df = pd.DataFrame(st.session_state.detections)
            attendance_table.dataframe(df.tail(10))
        else:
            st.info("Waiting for detections...")
    
    # Demo controls
    col3, col4 = st.columns(2)
    with col3:
        if st.button("🧹 Clear Log", use_container_width=True):
            st.session_state.detections = []
            st.rerun()
    with col4:
        if st.button("📥 Export Demo Data", use_container_width=True):
            # Export functionality
            st.success("✅ Demo data exported!")

if __name__ == "__main__":
    interview_demo_app()