Create app.py

app.py

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+import gradio as gr
+from deepface import DeepFace
+import numpy as np
+import PIL
+from PIL import Image, ImageDraw, ImageFont
+import time
+
+dbackends = [
+  'opencv', 
+  'ssd', 
+  'dlib', 
+  'mtcnn', 
+  'retinaface', 
+  'mediapipe',
+  'yolov8',
+  'yunet',
+  'fastmtcnn',
+]
+
+annotated_image = gr.AnnotatedImage()
+jsontext =  gr.Text(label= "deepface extract_faces results",)
+
+def findFaces(imgfile,dbackend):
+	start_time = time.time()
+	print(start_time)
+
+	face_objs = DeepFace.extract_faces(img_path = imgfile, enforce_detection = False, detector_backend = dbackend)
+	numberoffaces = len(face_objs)
+	jsontext = ''
+	faceannotations = []
+	for face_obj in face_objs:
+		face_coordinates = (face_obj["facial_area"]["x"],face_obj["facial_area"]["y"], (face_obj["facial_area"]["x"] + face_obj["facial_area"]["w"]),(face_obj["facial_area"]["y"] + face_obj["facial_area"]["h"]))
+		face_confidence =  "{:.0%}".format(face_obj["confidence"])
+		face_result=[face_coordinates,face_confidence]
+		faceannotations.append(face_result)
+	jsontext=faceannotations
+	run_time = str(round((time.time() - start_time),2))
+	results = gr.AnnotatedImage(
+        label= "Detected " + str(numberoffaces) + " faces via " + dbackend + ' in ' + run_time + ' seconds.',
+        value=(imgfile, faceannotations)
+    )
+	
+	print(run_time)
+	return(results,jsontext,numberoffaces,run_time)
+
+dbackendchoice = gr.Radio(choices=dbackends,label='Detector Backend:')
+demo = gr.Interface(
+	allow_flagging = "never",
+	fn=findFaces,
+	inputs=[gr.Image(value="8428_26_SM.jpg"), dbackendchoice],
+	outputs=[annotated_image,jsontext],
+	
+)
+
+demo.launch(show_error=True)
+