app.py

import numpy as np
import cv2
import face_recognition
import sqlite3
import streamlit as st
from datetime import datetime
from PIL import Image

# Step 1: Initialize Face Recognition
# Load known faces and names
known_face_encodings = []
known_face_names = []

# Example: Add known faces
try:
    image_person1 = face_recognition.load_image_file("person1.jpg")
    encoding_person1 = face_recognition.face_encodings(image_person1)[0]
    known_face_encodings.append(encoding_person1)
    known_face_names.append("Person 1")
except Exception as e:
    st.error(f"Error loading known faces: {e}")

# Step 2: Set Up Database
connection = sqlite3.connect("attendance.db", check_same_thread=False)
cursor = connection.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS Attendance (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    name TEXT,
    timestamp TEXT
)
''')

# Function to log attendance
def log_attendance(name):
    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    cursor.execute("INSERT INTO Attendance (name, timestamp) VALUES (?, ?)", (name, timestamp))
    connection.commit()

# Step 3: Detect and Recognize Faces
def detect_and_mark_attendance(uploaded_image):
    image = np.array(uploaded_image)
    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    face_locations = face_recognition.face_locations(rgb_image)
    face_encodings = face_recognition.face_encodings(rgb_image, face_locations)
    
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
        name = "Unknown"

        if True in matches:
            match_index = matches.index(True)
            name = known_face_names[match_index]
            log_attendance(name)  # Log to database

        # Draw a rectangle around the face
        cv2.rectangle(image, (left, top), (right, bottom), (255, 0, 0), 3)
        cv2.putText(image, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

    return image

# Step 4: Fetch Attendance Logs
def fetch_attendance():
    cursor.execute("SELECT * FROM Attendance")
    rows = cursor.fetchall()
    return rows

# Streamlit Interface
st.title("Face Recognition and Attendance System")
st.write("Upload an image to detect faces and mark attendance.")

# Image upload
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])

if uploaded_image is not None:
    image = Image.open(uploaded_image)
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Process image
    image_np = np.array(image)
    processed_image = detect_and_mark_attendance(image_np)
    st.image(processed_image, caption="Processed Image with Attendance Marked", use_column_width=True)

# Display attendance logs
st.subheader("Attendance Logs")
attendance_data = fetch_attendance()
if attendance_data:
    st.table(attendance_data)
else:
    st.write("No attendance records found.")