Updated from colab

app.py

@@ -1,7 +1,8 @@
 
+from sentence_transformers import util
 from transformers import pipeline
 from PIL import Image, ImageDraw
-from sentence_transformers import util, SentenceTransformer
+from sentence_transformers import util,SentenceTransformer
 
 checkpoint = "google/owlvit-base-patch32"
 detector = pipeline(model=checkpoint, task="zero-shot-object-detection")
@@ -26,10 +27,10 @@ def get_face_image(im1):
         continue
       draw = ImageDraw.Draw(im1)
       draw.rectangle(box_area, outline="red", width=1)
-      draw.text((xmin, ymin), f"{label}: {round(score,2)}", fill="blue")
+      #draw.text((xmin, ymin), f"{label}: {round(score,2)}", fill="blue")
       crop_img1 = im1.crop(box_area)
       #display(crop_img1)
-      newsize = (200, 200)
+      newsize = (256, 256)
       face_img1 = crop_img1.resize(newsize)
       #display(face_img1)
   return face_img1
@@ -45,17 +46,3 @@ def predict(im1, im2,inp_sim):
   else:
     return sim, "DIFFERENT PEOPLE, DON'T UNLOCK"
 
-import gradio as gr
-description = "An application that can recognize if two faces belong to the same person or not"
-title = "Facial Identity Recognition System"
-
-interface = gr.Interface(fn=predict, 
-                         inputs= [gr.Image(type="pil", source="webcam"), 
-                                  gr.Image(type="pil"),
-                                  gr.Slider(0, 1, value=0.8, label="Similarity Percentage", info="Choose betwen 0 and 1")], 
-                         outputs= [gr.Number(label="Similarity"),
-                                   gr.Textbox(label="Message")]
-                         )
-
-interface.launch(debug=True)
-