utils.py

import cv2
import os
import numpy as np
import joblib
import streamlit as st
from datetime import datetime
from PIL import Image
import pandas as pd  # Import pandas untuk menangani data absensi

# 1. Load Model yang Sudah Dilatih
model_path = "face_recognition_model.pkl"
model = joblib.load(model_path)

# Path Dataset untuk Referensi
dataset_dir = "dataset"
labels = [folder for folder in os.listdir(dataset_dir) if os.path.isdir(os.path.join(dataset_dir, folder))]

# Fungsi untuk mengenali wajah
def recognize_face(image, model, labels):
    image = cv2.resize(image, (100, 100)).flatten()  # Resize ke ukuran sama seperti saat training
    prediction = model.predict([image])[0]
    confidence = max(model.predict_proba([image])[0])  # Confidence score
    return prediction, confidence

# 2. Membuka Kamera di Streamlit
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# 3. File Absensi
absensi_file = "absensi.csv"

# Inisialisasi absensi
if not os.path.exists(absensi_file):
    with open(absensi_file, "w") as f:
        f.write("Waktu,Nama\n")

# Memeriksa apakah file absensi kosong, jika ya, buat DataFrame kosong
if os.stat(absensi_file).st_size == 0:
    absensi_df = pd.DataFrame(columns=["Waktu", "Nama"])
else:
    # Membaca absensi ke dalam DataFrame
    absensi_df = pd.read_csv(absensi_file)

# Streamlit layout
st.title("Sistem Absensi Berbasis Pengenalan Wajah")

col1, col2 = st.columns(2)

with col1:
    st.header("Scan Wajah")
    scan_placeholder = st.empty()

with col2:
    st.header("Daftar Absensi")
    absensi_placeholder = st.empty()

# Mulai kamera untuk scanning wajah
while True:
    ret, frame = cap.read()
    if not ret:
        st.warning("Tidak dapat mengakses kamera")
        break

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
        face = gray[y:y + h, x:x + w]

        # Prediksi wajah
        prediction, confidence = recognize_face(face, model, labels)
        
        if confidence > 0.8:  # Ambang batas confidence
            name = prediction
            if name not in absensi_df['Nama'].values:
                # Catat kehadiran
                waktu = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
                new_entry = pd.DataFrame([[waktu, name]], columns=["Waktu", "Nama"])
                absensi_df = pd.concat([absensi_df, new_entry], ignore_index=True)
                absensi_df.to_csv(absensi_file, index=False)  # Simpan ke file
                st.success(f"Absen berhasil untuk {name} pada {waktu}")

            cv2.putText(frame, f"{name} ({confidence:.2f})", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
        else:
            cv2.putText(frame, "Unknown", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

    # Konversi frame menjadi format yang dapat ditampilkan di Streamlit
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    img = Image.fromarray(frame_rgb)
    scan_placeholder.image(img)

    # Menampilkan daftar absensi dalam bentuk tabel
    absensi_placeholder.table(absensi_df)

cap.release()
cv2.destroyAllWindows()