change the similarity algorithm

app.py

@@ -42,9 +42,9 @@ transform = transforms.Compose([
     transforms.ToTensor()
 ])
 
-def cosine_similarity(embedding1, embedding2):
-    similarity = torch.nn.functional.cosine_similarity(embedding1.unsqueeze(0), embedding2.unsqueeze(0))
-    return similarity.item()
+def compare_faces(embedding1, embedding2, threshold=1.0):
+    dist = np.linalg.norm(embedding1.cpu().numpy() - embedding2.cpu().numpy())
+    return dist, dist < threshold
 
 def align_face(frame):
     # Convert the frame to a PIL image if it's a numpy array
@@ -60,6 +60,10 @@ def align_face(frame):
             return face
     return None
 
+def l2_normalize(tensor):
+    norm = torch.norm(tensor, p=2, dim=1, keepdim=True)
+    return tensor / norm
+
 def process_images(image1, image2):
     start_time = time.time()
     
@@ -79,17 +83,17 @@ def process_images(image1, image2):
         embedding1 = model(face1)
         embedding2 = model(face2)
     
-    similarity = cosine_similarity(embedding1, embedding2)
-    print(embedding1)
-    print(embedding2)
-    print(similarity)
+    embedding1 = l2_normalize(embedding1)
+    embedding2 = l2_normalize(embedding2)
+    
+    distance, is_match = compare_faces(embedding1, embedding2)
     
     end_time = time.time()
     inference_time = end_time - start_time
     
-    result = f"Similarity: {similarity:.2f}\nInference time: {inference_time:.2f} seconds"
+    result = f"Distance: {distance:.2f}\nMatch: {is_match}\nInference time: {inference_time:.2f} seconds"
     
-    return (frame1, frame2), result
+    return (image1, image2), result
 
 # Create the Gradio interface
 iface = gr.Interface(