Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 from deepface import DeepFace
-
 import cv2
 import numpy as np
 
@@ -30,16 +29,20 @@ def calculate_similarity(image1, image2):
         face2 = crop_face(image2)
 
         # Convert cropped images to RGB format for DeepFace
-        face1 = cv2.cvtColor(face1, cv2.COLOR_BGR2RGB)
-        face2 = cv2.cvtColor(face2, cv2.COLOR_BGR2RGB)
+        face1_rgb = cv2.cvtColor(face1, cv2.COLOR_BGR2RGB)
+        face2_rgb = cv2.cvtColor(face2, cv2.COLOR_BGR2RGB)
 
         # Analyze the two cropped images for facial similarity
-        result = DeepFace.verify(face1, face2, model_name='VGG-Face', enforce_detection=False)
+        result = DeepFace.verify(face1_rgb, face2_rgb, model_name='VGG-Face', enforce_detection=False)
         similarity_percentage = (1 - result['distance']) * 100  # Convert distance to similarity percentage
-        return similarity_percentage  
-    except Exception as e:
-        return str(e)
 
+        # Encode images to display in Gradio
+        _, face1_encoded = cv2.imencode('.png', face1)
+        _, face2_encoded = cv2.imencode('.png', face2)
+
+        return similarity_percentage, face1_encoded.tobytes(), face2_encoded.tobytes()
+    except Exception as e:
+        return str(e), None, None
 
 # Create a Gradio interface
 iface = gr.Interface(
@@ -48,9 +51,13 @@ iface = gr.Interface(
         gr.inputs.Image(type="filepath", label="Image 1"),
         gr.inputs.Image(type="filepath", label="Image 2")
     ],
-    outputs=gr.outputs.Textbox(label="Similarity Percentage"),
+    outputs=[
+        gr.outputs.Textbox(label="Similarity Percentage"),
+        gr.outputs.Image(label="Cropped Face 1"),
+        gr.outputs.Image(label="Cropped Face 2"),
+    ],
     title="Face Similarity Checker",
-    description="Upload two images of faces to check their similarity."
+    description="Upload two images of faces to check their similarity. The faces will be detected and cropped automatically."
 )
 
 # Launch the interface