app.py

import streamlit as st
from PIL import Image
import face_recognition
import cv2
import numpy as np
import os
import sqlite3
from datetime import datetime
from huggingface_hub import Repository
import pandas as pd

# Establish connection to SQLite database
conn = sqlite3.connect('attendance.db')
c = conn.cursor()

# Create a table for storing attendance if it doesn't exist
c.execute('''CREATE TABLE IF NOT EXISTS attendance 
             (id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT, time TEXT)''')

st.title("AIMLJan24 - Face Recognition")

# Load images for face recognition
Images = []   
classnames = []  
directory = "photos"
myList = os.listdir(directory)
current_datetime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

st.write("Photographs found in folder : ")
for cls in myList:
    if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]:
        img_path = os.path.join(directory, cls)
        curImg = cv2.imread(img_path)
        Images.append(curImg)
        st.write(os.path.splitext(cls)[0])
        classnames.append(os.path.splitext(cls)[0])

# Load images for face recognition
encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]

# camera to take photo of user in question
file_name = st.camera_input("Upload image")

def add_attendance(name):
    username = name
    c.execute("INSERT INTO attendance (name, time) VALUES (?, ?)", (username, current_datetime))
    conn.commit()

def addAttendance(name):
    directory_name = "my_directory"

    # Check if the repository exists, if not, create it
    if not hf_repo_exists(directory_name):
        hf_repo_create(directory_name)
        print(f"Repository '{directory_name}' created successfully!")
    else:
        repo = Repository()
    
    # Create the directory
    repo.create_directory(directory_name)
    
    # Define recognized results (example)
    recognized_results = [
        {"Name": "{name}", "Status": "Present"},
        {"Name": "Alice", "Status": "Absent"},
        {"Name": "Bob", "Status": "Present"}
    ]
    
    # Convert recognized results to DataFrame
    df = pd.DataFrame(recognized_results)
    
    # Define the path to save the Excel file
    excel_file_path = "recognized_results.xlsx"
    
    # Save DataFrame to Excel file
    df.to_excel(excel_file_path, index=False)
    
    # Define the destination path within the directory
    destination_path = os.path.join(directory_name, excel_file_path)
    
    # Move the Excel file to the destination path
    os.system(f"mv {excel_file_path} {destination_path}")
    
    print(f"Excel file '{excel_file_path}' added to directory '{directory_name}' successfully!")

if file_name is not None:
    col1, col2 = st.columns(2)

    test_image = Image.open(file_name)
    image = np.asarray(test_image)

    imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)
    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
    facesCurFrame   = face_recognition.face_locations(imgS)
    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)

    # List to store recognized names for all faces in the image
    recognized_names = []

    # Checking if faces are detected
    if len(encodesCurFrame) > 0:
        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
            # Assuming that encodeListknown is defined and populated in your code
            matches = face_recognition.compare_faces(encodeListknown, encodeFace)
            faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
            
            # Initialize name as Unknown
            name = "Unknown"
        
            # Check if there's a match with known faces
            if True in matches:
                matchIndex = np.argmin(faceDis)
                name = classnames[matchIndex].upper()
                
            # Append recognized name to the list
            recognized_names.append(name)
        
            # Draw rectangle around the face
            y1, x2, y2, x1 = faceLoc
            y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4)
            image = image.copy()
            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
            
            # Store attendance in SQLite database
            addAttendance(name)
            
        # Display the image with recognized faces
        st.image(image, use_column_width=True, output_format="PNG")

        # Display recognized names
        st.write("Recognized Names:")
        for i, name in enumerate(recognized_names):
            st.write(f"Face {i+1}: {name}")
    else:
        st.warning("No faces detected in the image. Face recognition failed.")