Update app.py

app.py

@@ -32,6 +32,13 @@ if image1 and image2:
     img1 = Image.open(image1).convert("RGB")
     img2 = Image.open(image2).convert("RGB")
 
+    # Display images
+    col1, col2 = st.columns(2)
+    with col1:
+        st.image(img1, caption='First Signature Image', use_container_width=True)
+    with col2:
+        st.image(img2, caption='Second Signature Image', use_container_width=True)
+
     # Transform the images before feeding them into the model
     img1 = transform(img1).unsqueeze(0).to(device)
     img2 = transform(img2).unsqueeze(0).to(device)
@@ -43,19 +50,9 @@ if image1 and image2:
     # Set a threshold for similarity (can be tuned based on model performance)
     threshold = 0.5  # You can adjust this threshold based on your model's performance
 
-    # Display both images and results side by side
-    col1, col2 = st.columns(2)
-    with col1:
-        st.image(img1.squeeze(0).cpu().permute(1, 2, 0), caption='First Signature Image', use_container_width=True)
-    with col2:
-        st.image(img2.squeeze(0).cpu().permute(1, 2, 0), caption='Second Signature Image', use_container_width=True)
-
-    # Display similarity score and interpretation side by side
-    col1, col2 = st.columns(2)
-    with col1:
-        st.success(f'Similarity Score (Euclidean Distance): {euclidean_distance.item():.4f}')
-    with col2:
-        if euclidean_distance.item() < threshold:
-            st.write("The signatures are likely from the **same person**.")
-        else:
-            st.write("The signatures **do not match**, one might be **forged**.")
+    # Display similarity score and interpretation
+    st.success(f'Similarity Score (Euclidean Distance): {euclidean_distance.item():.4f}')
+    if euclidean_distance.item() < threshold:
+        st.write("The signatures are likely from the **same person**.")
+    else:
+        st.write("The signatures **do not match**, one might be **forged**.")