Update app.py

app.py

@@ -1,27 +1,17 @@
-#IMPORTANT PART ONLY
-#!pip install deepface
 import gradio as gr
-
 from deepface import DeepFace
-#from google.colab import drive
-#drive.mount('/content/drive')
-#img1_path = '/content/drive/My Drive/Colab Notebooks/DeepLearning/FaceRecognition/PhotoDataSet/10Jan_1.jpeg'
-#import zipfile
-#with zipfile.ZipFile("PhotoDataSet.zip","r") as zip_ref:
-    #zip_ref.extractall("db_path")
 
+#db_path='https://github.com/ipvikas/MyPhotos'
 
+from datasets import load_dataset
+db_path= load_dataset("imagefolder", data_files="https://huggingface.co/spaces/ipvikas/ImageProcessing/blob/main/PhotoDataSet.zip")
 
-db_path='https://github.com/ipvikas/MyPhotos'
 
 import pandas as pd
 def get_deepface(image):
-
-    df = DeepFace.find(img_path=image, db_path='https://github.com/ipvikas/MyPhotos')
-    #print(df.head())
+    df = DeepFace.find(img_path=image, db_path=db_path)
     return DeepFace.analyze(img_path=image)
 
-
 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(
@@ -30,7 +20,6 @@ facial_attribute_demo = gr.Interface(
     outputs=['text'],
     title="face recognition and facial attribute analysis",
     description=description,
-    
     enable_queue=True,
     examples=[["10Jan_1.jpeg"]],
     cache_examples=False)