Update app.py

app.py

@@ -1,34 +1,22 @@
+#ALL as a whole
+import os
 import gradio as gr
-import pathlib
 from deepface import DeepFace
+import matplotlib.pyplot as plt
 
-#db_path='https://huggingface.co/spaces/ipvikas/ImageProcessing/blob/main/MyPhotos'
+def get_deepface_verify(img1_detect, img2_detect ):
+    img1_detect= DeepFace.detectFace(img1_path)
+    img2_detect= DeepFace.detectFace(img2_path)
+    model_name = 'ArcFace'  #VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib, ArcFace or Ensemble
+    result = DeepFace.verify(img1_path=img1_path,img2_path=img2_path,model_name = model_name)
+    return result
 
-#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)
+title = "DeepFace"
+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."
+examples=[["10Jan_1.jpeg"],["10Jan_2.jpeg"]]
 
 
-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(get_deepface_verify,["image","image", label="distance metric"],[gr.outputs.Label(label="same person"),gr.outputs.Label(label="distance"),gr.outputs.Label(label="max threshold to verify"),gr.outputs.Label(label="model"),gr.outputs.Label(label="similarity metric")],enable_queue=True,examples=examples, title=title,description=description,article=article,theme="darkdefault").launch(debug=True)
 
-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()