Added face detection codes

app.py

@@ -1,7 +1,38 @@
 import gradio as gr
+import numpy as np
+import insightface
+from insightface.app import FaceAnalysis
+from insightface.data import get_image as ins_get_image
+from PIL import Image
 
-def greet(name):
-    return "Hello " + name + "!!"
+import PIL
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+app = FaceAnalysis(name="buffalo_sc", providers=['CPUExecutionProvider'], allowed_modules=['detection'])
+
+article="<p style='text-align: center'><a href='' target='_blank'>Face Detection</a></p>"
+description = "This Face Detection Project uses InsightFace Library (https://insightface.ai/). We use RetinaFace-500MF model for the Face Detection. Upload an image or click an example image to use."
+
+def show_preds(input_image, detection_threshold=0.2):
+
+  if detection_threshold<0.05 or detection_threshold==None: detection_threshold = 0.10
+
+  app.prepare(ctx_id=0, det_size=(640, 640), det_thresh=detection_threshold)
+
+  img = PIL.Image.fromarray(input_image, 'RGB')
+  basewidth = 900
+  wpercent = (basewidth/float(img.size[0]))
+  hsize = int((float(img.size[1])*float(wpercent)))
+  img = img.resize((basewidth,hsize), Image.ANTIALIAS)
+
+  #display(img)
+  faces = app.get(np.array(img))
+  detected = app.draw_on(np.array(img), faces)
+  return detected
+
+detection_threshold_slider = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.2, label="Detection Threshold")
+outputs = gr.outputs.Image(type="pil")
+
+examples = [['example1.jpg',0.2], ['example2.jpg',0.2]]
+
+gr_interface = gr.Interface(fn=show_preds, inputs=["image", detection_threshold_slider], outputs=outputs, title='Face Detection App', article=article,description=description, examples=examples, analytics_enabled = True, enable_queue=True)
+gr_interface.launch(inline=False, share=False, debug=True)

requirements.txt

@@ -0,0 +1,3 @@
+insightface==0.5
+onnx==1.10.2
+onnxruntime==1.10.0