app.py

import streamlit as st
from PIL import Image
import face_recognition

st.title("Face Detection")

# Load the jpg file into a numpy array
input_image = st.file_uploader("Upload a candidate image",type=['jpg','png','jpeg'],accept_multiple_files=False)
if input_image is not None:
    image = face_recognition.load_image_file(input_image)
    st.image(image)

    # Find all the faces in the image using the default HOG-based model.
    # This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
    # See also: find_faces_in_picture_cnn.py
    face_locations = face_recognition.face_locations(image)
    data_base = []
    st.write("I found {} face(s) in this photograph.".format(len(face_locations)))
    
    cols= st.columns(len(face_locations))
    for i in range(len(face_locations)):
        col = cols[i]
        face = face_locations[i]
        # display faces
        with col:
            st.header("Face {}".format(i))
            # Print the location of each face in this image
            top, right, bottom, left = face
            # You can access the actual face itself like this:
            face_image = image[top:bottom, left:right]
            pil_image = Image.fromarray(face_image)
            st.image(pil_image)
            face_name = st.text_input('Specify name', "This is a placeholder")
            st.write(face_name)
            data_base.append(face_name)
else:
    st.write("Please upload an image to proceed.")