import gradio as gr
#import cv2
from deepface import DeepFace

# Define functions for each functionality
def verify_images(image1, image2):
    result = DeepFace.verify(image1, image2)
    return result

def identify_image(image, database):
    #results = []
    #for db_image in database:
    result = DeepFace.find(image, database)
    final = result[0]['identity'].tolist()
        #if result:
           # results.append(db_image)
    return final

'''#def real_time_face_identification(video_source):
    cap = cv2.VideoCapture(video_source)
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        # Perform face identification (example code)
        faces = DeepFace.detectFace(frame)
        # Display results
        cv2.imshow('Face Identification', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()'''

# Create the Gradio interface
iface = gr.Interface(
    fn=
        verify_images
        # identify_image
        #real_time_face_identification
    ,
    inputs=[
        gr.Image(type="filepath"), gr.Image(type="filepath")
        #gr.Image(type="filepath"), gr.Textbox(label="Paste database path here")
        #gr.Video()
    ],
    outputs=[
        gr.Textbox(label="Verification Result")
        #gr.Gallery(label="Identified Images"),
        #None
    ]
)

iface2 = gr.Interface(
    fn=
        identify_image
    ,
    inputs=[
        gr.Image(type="filepath"), gr.Textbox(label="Paste database path here")
    ],
    outputs=[
        gr.Gallery(label="Identified Images" , show_label=False, elem_id="gallery"
    , columns=[3], rows=[2], object_fit="contain", height="auto")
    
        
    ] )
demo = gr.TabbedInterface([iface, iface2], ["Face Verification", "Face Identification"])


demo.launch()