import gradio as gr
import pathlib
from deepface import DeepFace

#db_path='https://huggingface.co/spaces/ipvikas/ImageProcessing/blob/main/MyPhotos'

#db_path='https://huggingface.co/spaces/ipvikas/ImageProcessing/commit/c65e002550d4c148da1bb94c114373b2272f4d88#d2h-994579/'
db_path= [[path.as_posix()] for path in sorted(pathlib.Path('Image_DATA').rglob('*.j*g'))]

#from datasets import load_dataset
#db_path= load_dataset("imagefolder", data_files=db_path)


import pandas as pd
def get_deepface(image):
    df = DeepFace.find(img_path=image, db_path=db_path)
    d = DeepFace.analyze(img_path=image)
    #new_list = zip(d.keys(), d.values()) 
    #new_list = list(new_list)
    return d
 
description = "Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib."

facial_attribute_demo = gr.Interface(
    fn=get_deepface,
    inputs="image",
    outputs=['text'],
    title="face recognition and facial attribute analysis",
    description=description,
    enable_queue=True,
    examples=[["10Jan_1.jpeg"]],
    cache_examples=False)

#facial_attribute_demo.launch()